1. A data processing method applied to intelligent medical treatment and big data, which is applied to a medical server, wherein the medical server is in communication connection with a remote medical client, and the method at least comprises the following steps:

acquiring visual interactive misleading information among the to-be-identified online diagnosis guide policies based on the acquired data security prompt information of the to-be-identified online diagnosis guide policies in at least two diagnosis interactive attention analysis states;

determining the online diagnosis guiding policy aiming at the diagnosis interaction attention degree of each to-be-identified online diagnosis guiding policy according to the visual interaction misleading information among the to-be-identified online diagnosis guiding policies; the online encounter guidance policy for encounter interaction concerns is for distribution to the remote medical client.

2. The method of claim1, wherein obtaining visual interactive misleading information between each of the to-be-identified online diagnosis guide guidelines based on the obtained data security prompt information of the to-be-identified online diagnosis guide guidelines in at least two diagnosis interactive attention analysis states comprises:

acquiring data security prompt information of the to-be-identified online diagnosis guide policy in at least two diagnosis interaction attention analysis states;

determining a data threat response log of the to-be-identified online diagnosis guiding policy in at least two diagnosis interaction attention analysis states according to data security prompt information of the to-be-identified online diagnosis guiding policy in the at least two diagnosis interaction attention analysis states;

and obtaining visual interactive misleading information among the to-be-identified online diagnosis guide policies according to the data threat response logs of the to-be-identified online diagnosis guide policies in the at least two diagnosis interactive attention analysis states.

3. The method of claim2, wherein the obtaining data security prompt information of the to-be-identified online medical treatment guideline in at least two medical treatment interaction attention analysis states comprises:

acquiring a to-be-identified online diagnosis guide policy in a preset data threat triggering time period and data security prompt information of the to-be-identified online diagnosis guide policy responded by a remote medical client;

and acquiring data security prompt information of the to-be-identified online diagnosis guide policy in at least two diagnosis interaction attention analysis states in the preset data threat triggering time period according to the to-be-identified online diagnosis guide policy in the preset data threat triggering time period and the data security prompt information of the to-be-identified online diagnosis guide policy responded by the remote medical client.

4. The method of claim3, wherein the encounter interaction interest analysis state comprises a first encounter interaction interest analysis state, a second encounter interaction interest analysis state, and a third encounter interaction interest analysis state; the method comprises the following steps of acquiring data security prompt information of a to-be-identified online diagnosis guide policy in a preset data threat triggering time period and a data security prompt message of the to-be-identified online diagnosis guide policy responded by a remote medical client, wherein the data security prompt message of the to-be-identified online diagnosis guide policy in at least two diagnosis interaction attention analysis states comprises the following steps:

according to data security prompt information of the to-be-identified online diagnosis guiding policy responded by the remote medical client, determining data threat response information between the remote medical client and the to-be-identified online diagnosis guiding policy in a preset data threat triggering time period as data security prompt information of the to-be-identified online diagnosis guiding policy in the first diagnosis interaction attention analysis state;

extracting data security reference instructions from the online diagnosis guide policy to be identified;

acquiring a reference indication pairing result of the data security reference indication in the to-be-identified online diagnosis guiding policy within the preset data threat triggering time period, and identifying the reference indication pairing result as data security prompt information of the to-be-identified online diagnosis guiding policy in the second diagnosis interaction attention analysis state;

according to the preset data threat triggering time period, data threat response information between the remote medical client and the to-be-identified online diagnosis guiding policy and a reference indication pairing result of the data security reference indication in the to-be-identified online diagnosis guiding policy are confirmed, and the data threat response information of the reference indication pairing result of the remote medical client and the data security reference indication in the to-be-identified online diagnosis guiding policy is used as data security prompt information of the to-be-identified online diagnosis guiding policy in the third diagnosis interaction attention analysis state.

5. The method of claim2, wherein determining the data threat response log of the to-be-identified online medical guideline in the at least two medical interaction attention analysis states according to the data security prompt information of the to-be-identified online medical guideline in the at least two medical interaction attention analysis states comprises:

generating a guide policy use condition of the to-be-identified online diagnosis guide policy in at least two diagnosis interaction attention analysis states according to data security prompt information of the to-be-identified online diagnosis guide policy in the at least two diagnosis interaction attention analysis states;

carrying out log information identification on the use condition of the guide policy to obtain a data threat response log of the to-be-identified online diagnosis guide policy in the at least two diagnosis interaction attention analysis states;

according to wait to discern and see doctor online guide policy data security protection tip information under two at least interactive attention analytic states of seeing a doctor, generate wait to discern and see doctor online guide policy in the guide policy in two at least interactive attention analytic states of seeing a doctor, include:

generating a first guiding policy use condition, a second guiding policy use condition and a third guiding policy use condition according to data security prompt information of the to-be-identified online diagnosis guiding policy in at least two diagnosis interaction attention analysis states; the first guide policy usage is used for representing data threat response information between the remote medical client tag and the data threat response log, the second guide policy usage is used for representing data threat response information between the data threat response log and the data security reference indication index, and the third guide policy usage is used for representing data threat response information between the remote medical client tag and the data security reference indication index;

analyzing the use condition of the first guide policy, the use condition of the second guide policy and the use condition of the third guide policy into the use conditions of the guide policies to be identified in the online diagnosis guide policy analysis state of the at least two diagnosis interaction attention degrees;

if the guiding policy usage is the first guiding policy usage or the second guiding policy usage, the log information identification of the guiding policy usage is performed, and obtaining the data threat response log of the to-be-identified online diagnosis guiding policy in the at least two diagnosis interaction attention analysis states includes: carrying out log information identification on the use condition of the guide policy to obtain a data threat response log which is used as the data threat response log of the to-be-identified online diagnosis guide policy in the corresponding diagnosis interaction attention analysis state;

if the guiding policy using condition is the third guiding policy using condition, the log information identification is performed on the guiding policy using condition, and obtaining the data threat response log of the to-be-identified online diagnosis guiding policy in the at least two diagnosis interaction attention analysis states includes: carrying out log information identification on the use condition of the guide policy to obtain a data security reference indication index in the to-be-identified online medical treatment guide policy; acquiring a reference indication pairing result of the data security reference indication in the to-be-identified online medical guide policy, wherein the reference indication pairing result is used as a reference indication pairing result corresponding to the data security reference indication index; and obtaining a data threat response log according to the data security reference indication index and the corresponding reference indication pairing result, wherein the data threat response log is used as the data threat response log of the to-be-identified online diagnosis guide policy in the corresponding diagnosis interaction attention analysis state.

6. The method of claim2, wherein obtaining visual interactive misleading information between each of the to-be-identified online medical guideline comprises, from the log of data threat responses of the to-be-identified online medical guideline in the at least two medical interactive interest analysis states:

determining guideline correlation coefficients of every two to-be-identified online diagnosis guide guidelines in at least two diagnosis interaction attention analysis states according to data threat response logs of the to-be-identified online diagnosis guide guidelines in the at least two diagnosis interaction attention analysis states;

obtaining visual interactive misleading information between the to-be-identified online diagnosis guide guidelines according to the guideline correlation coefficients of every two to-be-identified online diagnosis guide guidelines in at least two diagnosis interactive attention analysis states;

the step of obtaining the visual interactive misleading information between the to-be-identified online diagnosis guide guidelines according to the guideline correlation coefficients of every two to-be-identified online diagnosis guide guidelines in at least two diagnosis interactive attention analysis states comprises the following steps:

inputting the guideline correlation coefficients of every two to-be-identified online diagnosis guide guidelines in at least two diagnosis interaction attention analysis states into an interaction attention processing network which is trained in advance;

confirming the timeliness of the threat coping guidelines among the guideline correlation coefficients through the interactive attention processing network;

acquiring reference indication pairing results of the guideline correlation coefficients and reference indication pairing results of timeliness of the threat coping guidelines;

and determining to obtain visual interactive misleading information among the to-be-identified online diagnosis guide guidelines according to the guideline correlation coefficients and the corresponding reference indication pairing results, and the timeliness of the threat coping guidelines and the corresponding reference indication pairing results.

7. The method of claim 6, wherein the interactive attention processing network is trained by:

acquiring guideline correlation coefficients and corresponding first reference indication pairing results of sample online diagnosis guide guidelines in at least two diagnosis interaction attention analysis states, threat response guideline timeliness and corresponding second reference indication pairing results among the guideline correlation coefficients, and actual visual interaction misleading information among the sample online diagnosis guide guidelines;

training the interactive attention processing network according to the guideline relevance coefficients and the corresponding first reference indication pairing results of the sample online diagnosis guide guidelines in at least two diagnosis interactive attention analysis states, the threat coping guideline timeliness among the guideline relevance coefficients and the corresponding second reference indication pairing results to obtain the trained interactive attention processing network;

obtaining the quantitative difference of the attention degree between the visual interaction misleading information output by the trained interaction attention degree processing network and the corresponding actual visual interaction misleading information;

and when the quantized difference of the attention degree is larger than or equal to a preset quantized difference value, improving the first reference indication pairing result and the second reference indication pairing result according to the quantized difference of the attention degree, and performing staged training on the interactive attention degree processing network according to the improved first reference indication pairing result and the improved second reference indication pairing result until the quantized difference of the attention degree obtained according to the trained interactive attention degree processing network is smaller than the preset quantized difference value.

8. The method of claim2, wherein determining the online encounter guidance guidelines for each of the identified online encounter guidance guidelines with respect to encounter interaction attention based on the visual interaction misleading information between each of the identified online encounter guidance guidelines comprises:

and respectively according to the visual interactive misleading information among the to-be-identified online diagnosis guiding guidelines, taking the to-be-identified online diagnosis guiding guidelines, which are used as the to-be-identified online diagnosis guiding guidelines aiming at the aspect of the to-be-identified online diagnosis guiding guidelines, wherein the guideline interactive attention degree corresponding to the visual interactive misleading information among the to-be-identified online diagnosis guiding guidelines is greater than the first set diagnosis interactive attention degree and less than the second set diagnosis interactive attention degree.

9. The method according to any one of claims 1 to 8, comprising, after determining the online encounter guidance policy for each of the to-be-identified online encounter guidance policies with respect to encounter interaction attention,:

acquiring a guide policy calling path of the online medical treatment guide policy to be identified;

guiding the online diagnosis guide policy aiming at the diagnosis interaction attention degree of the online diagnosis guide policy to be identified into an online diagnosis wind control network corresponding to the guide policy calling path;

configuring the online treatment wind control network;

preferably, the method further comprises:

receiving an on-line treatment application sent by the remote medical client; the online treatment application carries a key label corresponding to the guiding policy calling path;

extracting an online diagnosis guide policy aiming at the aspect of diagnosis interaction attention from the online diagnosis wind control network corresponding to the guide policy calling path;

and allocating the on-line doctor visit guiding policy aiming at the doctor visit interaction attention degree to the remote medical client.

10. A medical server comprising a processing engine, a system bus, and a memory; the processing engine and the memory communicate via the system bus, the processing engine reading a computer program from the memory and operating to perform the method of any of claims 1-9.

Background

The intelligent medical treatment is a novel medical treatment mode which comprehensively applies medical big data, medical cloud computing, medical internet of things and other technologies, fuses medical basic facilities and IT basic facilities through a communication technology, takes a medical center service platform as a core, crosses space-time limitation of a traditional medical treatment mode, makes intelligent decision on the basis and realizes optimization of medical services.

At present, an intelligent medical system generally comprises an intelligent medical server and an intelligent medical user terminal, and interaction and processing of medical services are also implemented based on the intelligent medical server and the intelligent medical user terminal, however, in this interaction processing mode, security of related user data information still remains a technical problem to be taken into account.

Disclosure of Invention

One of the embodiments of the present application provides a data processing method applied to intelligent medical treatment and big data, which is applied to a medical server, wherein the medical server is in communication connection with a remote medical client, and the method at least includes the following steps:

acquiring visual interactive misleading information among the to-be-identified online diagnosis guide policies based on the acquired data security prompt information of the to-be-identified online diagnosis guide policies in at least two diagnosis interactive attention analysis states;

determining the online diagnosis guiding policy aiming at the diagnosis interaction attention degree of each to-be-identified online diagnosis guiding policy according to the visual interaction misleading information among the to-be-identified online diagnosis guiding policies; the online encounter guidance policy for encounter interaction attention is for distribution to corresponding telemedicine clients.

Preferably, the obtaining of the visual interactive misleading information between the to-be-identified online diagnosis guide policy based on the obtained data security prompt information of the to-be-identified online diagnosis guide policy in the at least two diagnosis interactive attention analysis states includes:

acquiring data security prompt information of the to-be-identified online diagnosis guide policy in at least two diagnosis interaction attention analysis states;

determining a data threat response log of the to-be-identified online diagnosis guiding policy in at least two diagnosis interaction attention analysis states according to data security prompt information of the to-be-identified online diagnosis guiding policy in the at least two diagnosis interaction attention analysis states;

and obtaining visual interactive misleading information among the to-be-identified online diagnosis guide policies according to the data threat response logs of the to-be-identified online diagnosis guide policies in the at least two diagnosis interactive attention analysis states.

Preferably, the acquiring data security prompt information of the to-be-identified online diagnosis guide policy in at least two diagnosis interaction attention analysis states includes:

acquiring a to-be-identified online diagnosis guide policy in a preset data threat triggering time period and data security prompt information of the to-be-identified online diagnosis guide policy responded by a remote medical client;

and acquiring data security prompt information of the to-be-identified online diagnosis guide policy in at least two diagnosis interaction attention analysis states in the preset data threat triggering time period according to the to-be-identified online diagnosis guide policy in the preset data threat triggering time period and the data security prompt information of the to-be-identified online diagnosis guide policy responded by the remote medical client.

Preferably, the diagnosis interaction attention analysis states include a first diagnosis interaction attention analysis state, a second diagnosis interaction attention analysis state and a third diagnosis interaction attention analysis state; the method comprises the following steps of acquiring data security prompt information of a to-be-identified online diagnosis guide policy in a preset data threat triggering time period and a data security prompt message of the to-be-identified online diagnosis guide policy responded by a remote medical client, wherein the data security prompt message of the to-be-identified online diagnosis guide policy in at least two diagnosis interaction attention analysis states comprises the following steps:

according to data security prompt information of the to-be-identified online diagnosis guiding policy responded by the remote medical client, determining data threat response information between the remote medical client and the to-be-identified online diagnosis guiding policy in a preset data threat triggering time period as data security prompt information of the to-be-identified online diagnosis guiding policy in the first diagnosis interaction attention analysis state;

extracting data security reference instructions from the online diagnosis guide policy to be identified;

acquiring a reference indication pairing result of the data security reference indication in the to-be-identified online diagnosis guiding policy within the preset data threat triggering time period, and identifying the reference indication pairing result as data security prompt information of the to-be-identified online diagnosis guiding policy in the second diagnosis interaction attention analysis state;

according to the preset data threat triggering time period, data threat response information between the remote medical client and the to-be-identified online diagnosis guiding policy and a reference indication pairing result of the data security reference indication in the to-be-identified online diagnosis guiding policy are confirmed, and the data threat response information of the reference indication pairing result of the remote medical client and the data security reference indication in the to-be-identified online diagnosis guiding policy is used as data security prompt information of the to-be-identified online diagnosis guiding policy in the third diagnosis interaction attention analysis state.

Preferably, the determining, according to the data security prompt information of the to-be-identified online diagnosis guiding policy in the at least two diagnosis interaction attention analysis states, the data threat response log of the to-be-identified online diagnosis guiding policy in the at least two diagnosis interaction attention analysis states includes:

generating a guide policy use condition of the to-be-identified online diagnosis guide policy in at least two diagnosis interaction attention analysis states according to data security prompt information of the to-be-identified online diagnosis guide policy in the at least two diagnosis interaction attention analysis states;

carrying out log information identification on the use condition of the guide policy to obtain a data threat response log of the to-be-identified online diagnosis guide policy in the at least two diagnosis interaction attention analysis states;

according to wait to discern and see doctor online guide policy data security protection tip information under two at least interactive attention analytic states of seeing a doctor, generate wait to discern and see doctor online guide policy in the guide policy in two at least interactive attention analytic states of seeing a doctor, include:

generating a first guiding policy use condition, a second guiding policy use condition and a third guiding policy use condition according to data security prompt information of the to-be-identified online diagnosis guiding policy in at least two diagnosis interaction attention analysis states; the first guide policy usage is used for representing data threat response information between the remote medical client tag and the data threat response log, the second guide policy usage is used for representing data threat response information between the data threat response log and the data security reference indication index, and the third guide policy usage is used for representing data threat response information between the remote medical client tag and the data security reference indication index;

analyzing the use condition of the first guide policy, the use condition of the second guide policy and the use condition of the third guide policy into the use conditions of the guide policies to be identified in the online diagnosis guide policy analysis state of the at least two diagnosis interaction attention degrees;

if the guiding policy usage is the first guiding policy usage or the second guiding policy usage, the log information identification of the guiding policy usage is performed, and obtaining the data threat response log of the to-be-identified online diagnosis guiding policy in the at least two diagnosis interaction attention analysis states includes: carrying out log information identification on the use condition of the guide policy to obtain a data threat response log which is used as the data threat response log of the to-be-identified online diagnosis guide policy in the corresponding diagnosis interaction attention analysis state;

if the guiding policy using condition is the third guiding policy using condition, the log information identification is performed on the guiding policy using condition, and obtaining the data threat response log of the to-be-identified online diagnosis guiding policy in the at least two diagnosis interaction attention analysis states includes: carrying out log information identification on the use condition of the guide policy to obtain a data security reference indication index in the to-be-identified online medical treatment guide policy; acquiring a reference indication pairing result of the data security reference indication in the to-be-identified online medical guide policy, wherein the reference indication pairing result is used as a reference indication pairing result corresponding to the data security reference indication index; and obtaining a data threat response log according to the data security reference indication index and the corresponding reference indication pairing result, wherein the data threat response log is used as the data threat response log of the to-be-identified online diagnosis guide policy in the corresponding diagnosis interaction attention analysis state.

Preferably, the obtaining of the visual interactive misleading information between the to-be-identified online diagnosis guide guidelines according to the data threat response log of the to-be-identified online diagnosis guide guidelines in the at least two diagnosis interactive attention analysis states includes:

determining guideline correlation coefficients of every two to-be-identified online diagnosis guide guidelines in at least two diagnosis interaction attention analysis states according to data threat response logs of the to-be-identified online diagnosis guide guidelines in the at least two diagnosis interaction attention analysis states;

obtaining visual interactive misleading information between the to-be-identified online diagnosis guide guidelines according to the guideline correlation coefficients of every two to-be-identified online diagnosis guide guidelines in at least two diagnosis interactive attention analysis states;

the step of obtaining the visual interactive misleading information between the to-be-identified online diagnosis guide guidelines according to the guideline correlation coefficients of every two to-be-identified online diagnosis guide guidelines in at least two diagnosis interactive attention analysis states comprises the following steps:

inputting the guideline correlation coefficients of every two to-be-identified online diagnosis guide guidelines in at least two diagnosis interaction attention analysis states into an interaction attention processing network which is trained in advance;

confirming the timeliness of the threat coping guidelines among the guideline correlation coefficients through the interactive attention processing network;

acquiring reference indication pairing results of the guideline correlation coefficients and reference indication pairing results of timeliness of the threat coping guidelines;

and determining to obtain visual interactive misleading information among the to-be-identified online diagnosis guide guidelines according to the guideline correlation coefficients and the corresponding reference indication pairing results, and the timeliness of the threat coping guidelines and the corresponding reference indication pairing results.

Preferably, the interactive attention processing network is obtained by training through the following steps:

acquiring guideline correlation coefficients and corresponding first reference indication pairing results of sample online diagnosis guide guidelines in at least two diagnosis interaction attention analysis states, threat response guideline timeliness and corresponding second reference indication pairing results among the guideline correlation coefficients, and actual visual interaction misleading information among the sample online diagnosis guide guidelines;

training the interactive attention processing network according to the guideline relevance coefficients and the corresponding first reference indication pairing results of the sample online diagnosis guide guidelines in at least two diagnosis interactive attention analysis states, the threat coping guideline timeliness among the guideline relevance coefficients and the corresponding second reference indication pairing results to obtain the trained interactive attention processing network;

obtaining the quantitative difference of the attention degree between the visual interaction misleading information output by the trained interaction attention degree processing network and the corresponding actual visual interaction misleading information;

and when the quantized difference of the attention degree is larger than or equal to a preset quantized difference value, improving the first reference indication pairing result and the second reference indication pairing result according to the quantized difference of the attention degree, and performing staged training on the interactive attention degree processing network according to the improved first reference indication pairing result and the improved second reference indication pairing result until the quantized difference of the attention degree obtained according to the trained interactive attention degree processing network is smaller than the preset quantized difference value.

Preferably, the determining, according to the visual interactive misleading information between the to-be-identified online diagnosis guidance guidelines, the online diagnosis guidance guidelines, which are specific to the diagnosis interactive attention degree, of the to-be-identified online diagnosis guidance guidelines includes:

and respectively according to the visual interactive misleading information among the to-be-identified online diagnosis guiding guidelines, taking the to-be-identified online diagnosis guiding guidelines, which are used as the to-be-identified online diagnosis guiding guidelines aiming at the aspect of the to-be-identified online diagnosis guiding guidelines, wherein the guideline interactive attention degree corresponding to the visual interactive misleading information among the to-be-identified online diagnosis guiding guidelines is greater than the first set diagnosis interactive attention degree and less than the second set diagnosis interactive attention degree.

Preferably, after determining the online medical consultation guidance policy of each to-be-identified online medical consultation guidance policy in terms of attention to medical consultation interaction, the method includes:

acquiring a guide policy calling path of the online medical treatment guide policy to be identified;

guiding the online diagnosis guide policy aiming at the diagnosis interaction attention degree of the online diagnosis guide policy to be identified into an online diagnosis wind control network corresponding to the guide policy calling path;

configuring the online treatment wind control network;

preferably, the method further comprises:

receiving an on-line treatment application sent by the remote medical client; the online treatment application carries a key label corresponding to the guiding policy calling path;

extracting an online diagnosis guide policy aiming at the aspect of diagnosis interaction attention from the online diagnosis wind control network corresponding to the guide policy calling path;

and allocating the on-line doctor visit guiding policy aiming at the doctor visit interaction attention degree to the remote medical client.

One of the embodiments of the present application provides a medical server, which includes a processing engine, a system bus and a memory; the processing engine and the memory communicate via the system bus, and the processing engine reads a computer program from the memory and runs the computer program to perform the method described above.

In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.

Drawings

The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

fig. 1 is an application scenario architecture diagram of a data processing method applied to smart medicine and big data in one embodiment.

Fig. 2 shows a schematic diagram of an internal hardware architecture of a cloud computing device (medical server) in one embodiment.

Fig. 3 is a flow chart of a data processing method applied to smart medicine and big data.

FIG. 4 is a block diagram of functional blocks of an exemplary data processing device for smart medicine and big data applications, according to some embodiments of the present invention.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Fig. 1 is an application scenario architecture diagram of a data processing method applied to smart medicine and big data in one embodiment. Referring to fig. 1, the application scenario architecture diagram includes a medical server 110 and a telemedicine client 120. The medical server 110 acquires data security prompt information of the to-be-identified online diagnosis guide policy in at least two diagnosis interaction attention analysis states based on the acquired to-be-identified online diagnosis guide policy, such as the to-be-identified online diagnosis guide policy1, the to-be-identified online diagnosis guide policy2, and the like (which can be understood as an online diagnosis indication policy); determining a data threat response log of the to-be-identified online diagnosis guiding policy in at least two diagnosis interaction attention analysis states according to data security prompt information of the to-be-identified online diagnosis guiding policy in the at least two diagnosis interaction attention analysis states; according to the data threat response logs of the to-be-identified online diagnosis guide guidelines in at least two diagnosis interaction attention analysis states, obtaining visual interaction misleading information among the to-be-identified online diagnosis guide guidelines; determining the online diagnosis guiding policy aiming at the diagnosis interaction attention degree of each to-be-identified online diagnosis guiding policy according to the visual interaction misleading information among the to-be-identified online diagnosis guiding policies; such as the online visit guide policy for the visit interaction attention of the to-be-identified online visit guide policy1, the online visit guide policy for the visit interaction attention of the to-be-identified online visit guide policy2, and so on. Wherein, the online diagnosis guide policy aiming at the aspect of diagnosis interaction attention is used for being distributed to the corresponding remote medical client; for example, the medical server 110 allocates the corresponding online medical consultation guidance policy for the interactive attention of the remote medical client to the corresponding remote medical client based on the online medical consultation application of the remote medical client, so that the remote medical client performs the relevant online medical consultation operation based on the online medical consultation guidance policy for the interactive attention of the medical consultation, thereby ensuring the user data security of the remote medical client in the remote medical consultation process and avoiding the loss of privacy data or important data due to the error of the online medical consultation operation or the misguidance of a hacker.

FIG. 2 illustrates a schematic diagram of the internal hardware architecture of the cloud computing device in one embodiment. The cloud computing device may specifically be the medical server 110 in fig. 1. As shown in fig. 2, the cloud computing device includes a processor 112, a memory 113, a network interface 114, an input device 115, and a display 116 connected by a system bus 111. The memory 113 includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium of the cloud computing device stores an operating system and may also store a computer program that, when executed by the processor 112, may cause the processor 112 to implement a data processing method applied to smart medicine and big data. The internal memory may also store a computer program that, when executed by the processor 112, causes the processor 112 to perform data processing methods applied to smart medicine and big data. The display 116 of the cloud computing device may be a liquid crystal display or an electronic ink display, and the input device 115 of the cloud computing device may be a touch layer covered on the display 116, a key, a trackball or a touch pad arranged on a housing of the cloud computing device, or an external keyboard, a touch pad or a mouse. Those skilled in the art will appreciate that the structure shown in fig. 2 is a block diagram of only a portion of the structure relevant to the present application, and does not constitute a limitation on the cloud computing device to which the present application is applied, and that a particular cloud computing device may include more or less components than those shown, or combine certain components, or have a different arrangement of components.

The data processing method applied to the intelligent medical treatment and the big data provided by the embodiment of the application can be summarized as the following contents: acquiring visual interactive misleading information among the to-be-identified online diagnosis guide policies based on the acquired data security prompt information of the to-be-identified online diagnosis guide policies in at least two diagnosis interactive attention analysis states; determining the online diagnosis guiding policy aiming at the diagnosis interaction attention degree of each to-be-identified online diagnosis guiding policy according to the visual interaction misleading information among the to-be-identified online diagnosis guiding policies; the online encounter guidance policy for encounter interaction attention is for distribution to corresponding telemedicine clients.

By the design, the visual interactive misleading information among all the to-be-identified online diagnosis guiding guidelines can be obtained based on the acquired data security prompt information of the to-be-identified online diagnosis guiding guidelines in at least two diagnosis interactive attention analysis states, and the to-be-identified online diagnosis guiding guidelines, aiming at the diagnosis interactive attention aspect, of all the to-be-identified online diagnosis guiding guidelines are determined. Thus, when an online diagnosis application of the remote medical client is received, the corresponding online diagnosis guiding policy aiming at the diagnosis interaction attention degree can be distributed to the remote medical client, so that the remote medical client executes relevant online diagnosis operation based on the online diagnosis guiding policy aiming at the diagnosis interaction attention degree, the user data safety of the remote medical client in the remote diagnosis process is ensured, and the loss of privacy data or important data caused by online diagnosis operation errors or hacker misleading is avoided.

It is to be appreciated that further description of the above summary can be found in conjunction with fig. 3, as shown in fig. 3, and in one embodiment, a data processing method for intelligent medicine and big data is provided. The embodiment is mainly illustrated by applying the method to the medical server 110 in fig. 1. Referring to fig. 3, the data processing method applied to smart medicine and big data specifically includes the following steps:

s31: and acquiring data security prompt information of the to-be-identified online diagnosis guide policy in at least two diagnosis interaction attention analysis states.

For example, the online visit guide policy to be identified refers to a guide policy that requires a medical interaction misleading (interaction indication interference) analysis. For example, the online medical treatment guidance policy to be identified may specifically be an online medical treatment operation guidance policy, an identity verification guidance policy, a medical treatment item selection guidance policy, and the like. The online diagnosis guide policy to be identified can be analyzed through a related application program installed in the remote medical client; the related application program can be a medical help small APP or the like downloaded from a medical server. In addition, the online medical consultation guidance policy to be identified may be obtained from a set of policies that are stored in advance, or may be obtained from another medical server or another remote medical client, and is not limited herein.

Further, the diagnosis interaction attention analysis state refers to one of the reference criteria for weighing visual interaction misleading information between the online diagnosis guide policies. For example, the diagnosis interaction attention degree analysis state may specifically be a remote medical client-medical application response standard, a medical application feedback condition-medical resource allocation attention degree standard, a remote medical client-medical resource allocation attention degree standard, and the like. The remote medical client-hospitalizing application response standard is used for describing whether the remote medical client verifies the hospitalizing application feedback condition, the hospitalizing application feedback condition-medical resource allocation attention degree standard is used for describing a reference indication pairing result corresponding to the medical resource allocation attention degree in the hospitalizing application feedback condition, and the remote medical client-medical resource allocation attention degree standard is used for describing whether the remote medical client verifies the hospitalizing application feedback condition containing the medical resource allocation attention degree. The data security prompt information refers to data security prompt information of the to-be-identified online diagnosis guide policy in each diagnosis interaction attention analysis state, for example, the data security prompt information of the to-be-identified online diagnosis guide policy in the telemedicine client-medical application response standard refers to data threat response information between the telemedicine client and the to-be-identified online diagnosis guide policy, for example, the to-be-identified online diagnosis guide policy is used by the telemedicine client, and the to-be-identified online diagnosis guide policy is not used by the telemedicine client.

It should be noted that the diagnosis interaction attention degree analysis state may include, in addition to the listed remote medical client-medical application response standard, medical application feedback condition-medical resource allocation attention degree standard, and remote medical client-medical resource allocation attention degree standard, other reference standards corresponding to the diagnosis interaction attention degree analysis state, and the specific application is not limited. The at least two diagnosis interaction attention analysis states may be two or more diagnosis interaction attention analysis states, and the specific application is not limited.

Illustratively, the medical server 110 collects online encounter guidance guidelines that are not deployed on the current online encounter network as to-be-identified online encounter guidance guidelines based on relevant frontier technologies (such as big data technologies and cloud computing technologies); and acquiring a plurality of diagnosis interaction attention analysis states, and analyzing the to-be-identified online diagnosis guide policy according to the diagnosis interaction attention analysis states to obtain data security prompt information of the to-be-identified online diagnosis guide policy in each diagnosis interaction attention analysis state.

In one embodiment, the medical server 110 may further extract the online encounter guidance policy for the same data threat triggering period from the locally cached online encounter guidance policies as the online encounter guidance policy to be identified; and acquiring analysis states of the diagnosis interaction attention degrees, and analyzing the to-be-identified online diagnosis guide policy according to the analysis states of the diagnosis interaction attention degrees to obtain data security prompt information of the to-be-identified online diagnosis guide policy in each diagnosis interaction attention analysis state.

In the embodiment of the application, the diagnosis interaction attention analysis state may include reference standards related to the diagnosis interaction attention on one hand and reference standards of the online diagnosis guidance policy in terms of policy contradiction on the other hand, and thus, by taking different diagnosis interaction attention analysis states into consideration, after the online diagnosis guidance policy is allocated, it is possible to avoid that the remote medical client fails to use the policy or is abnormal when the policy is used due to policy guidance conflict between different online diagnosis guidance policies.

In a related embodiment that can be implemented independently, the step of "obtaining data security prompt information of the to-be-identified online medical consultation guiding policy in at least two medical consultation interaction attention analysis states" may include the following contents: acquiring a to-be-identified online diagnosis guide policy in a preset data threat triggering time period and data security prompt information of the to-be-identified online diagnosis guide policy responded by a remote medical client; and acquiring data security prompt information of the to-be-identified online diagnosis guide policy in at least two diagnosis interaction attention analysis states in the preset data threat triggering time period according to the to-be-identified online diagnosis guide policy in the preset data threat triggering time period and the data security prompt information of the to-be-identified online diagnosis guide policy responded by the remote medical client. For example, the data threat triggering period may be set according to a historical online visit interaction record, for example, operation information may be sampled and analyzed for some remote medical clients with abnormal visit operations, so as to determine a period in which a risk of the data information threat is most likely to occur as the data threat triggering period, and certainly, when the data threat triggering period is determined, actual medical interaction conditions of different remote medical clients and related information of medical interaction participants need to be considered.

Further, the "data security prompt information of the to-be-identified online medical examination guide policy responded by the remote medical client" may be understood as data security prompt information parsed by the remote medical client after using the to-be-identified online medical examination guide policy. Further, after the to-be-identified online diagnosis guide policy in the data threat triggering time period is obtained, the data security prompt information of the to-be-identified online diagnosis guide policy in the at least two diagnosis interaction attention analysis states can be determined in the data threat triggering time period.

In a related, independently implementable embodiment, the diagnosis interaction attention analysis state may include a plurality of states, such as a first diagnosis interaction attention analysis state, a second diagnosis interaction attention analysis state, and a third diagnosis interaction attention analysis state. The first diagnosis interaction attention analysis state can be a diagnosis interaction attention analysis state selected by a department, the second diagnosis interaction attention analysis state can be a diagnosis interaction attention analysis state paid for medical fees, and the third diagnosis interaction attention analysis state can be a diagnosis interaction attention analysis state paid for hospitalization and identity verification. Of course, the first, second, and third diagnosis interaction attention analysis states may also be exemplified based on a reference standard level, which is not described herein again.

Further, on the basis of the above content, the step "obtaining, according to the to-be-identified online diagnosis guidance policy in the preset data threat triggering period and the data security prompt information of the to-be-identified online diagnosis guidance policy responded by the remote medical client, the data security prompt information of the to-be-identified online diagnosis guidance policy in at least two diagnosis interaction attention analysis states in the preset data threat triggering period" may include the following content: according to data security prompt information of the to-be-identified online diagnosis guiding policy responded by the remote medical client, determining data threat response information between the remote medical client and the to-be-identified online diagnosis guiding policy in a preset data threat triggering time period as data security prompt information of the to-be-identified online diagnosis guiding policy in the first diagnosis interaction attention analysis state; extracting data security reference instructions from the online diagnosis guide policy to be identified; acquiring a reference indication pairing result of the data security reference indication in the to-be-identified online diagnosis guiding policy within the preset data threat triggering time period, and identifying the reference indication pairing result as data security prompt information of the to-be-identified online diagnosis guiding policy in the second diagnosis interaction attention analysis state; according to the preset data threat triggering time period, data threat response information between the remote medical client and the to-be-identified online diagnosis guiding policy and a reference indication pairing result of the data security reference indication in the to-be-identified online diagnosis guiding policy are confirmed, and the data threat response information of the reference indication pairing result of the remote medical client and the data security reference indication in the to-be-identified online diagnosis guiding policy is used as data security prompt information of the to-be-identified online diagnosis guiding policy in the third diagnosis interaction attention analysis state.

It can be understood that, according to the data security prompt message of the to-be-identified online diagnosis guide policy responded by the remote medical client, the data threat response message between the remote medical client and the to-be-identified online diagnosis guide policy is determined within the preset data threat triggering time period, so that the diagnosis guide interaction condition between the remote medical client and the to-be-identified online diagnosis guide policy can be obtained, and thus, the data threat response message between the remote medical client and the to-be-identified online diagnosis guide policy is used as the data security prompt message of the to-be-identified online diagnosis guide policy in the first diagnosis interaction attention analysis state, so that the adaptability of the data security prompt message and the first diagnosis interaction attention analysis state can be ensured. Further, the data security reference indication may specifically be a minimized guidance element (guidance unit) for a remote medical treatment operation of the remote medical treatment client, such as guiding the treatment operation of the remote medical treatment client in different intelligent medical treatment interaction flows. In this way, after the reference indication pairing result of the data security reference indication in the to-be-identified online diagnosis guiding policy within the preset data threat triggering period is obtained, the association relationship between the data security reference indication and the reference indication pairing result can be established, so that the reference indication pairing result is identified as the data security prompt information of the to-be-identified online diagnosis guiding policy in the second diagnosis interaction attention analysis state. Further, for the data security prompt information of the to-be-identified online diagnosis guide policy in the second diagnosis interaction attention analysis state, when determining the data security prompt information of the to-be-identified online diagnosis guide policy in the third diagnosis interaction attention analysis state, the data security reference indicates that the reference indication pairing result in the to-be-identified online diagnosis guide policy is taken into account in the preset data threat triggering time period, so that the data threat response information of the reference indication pairing result in the to-be-identified online diagnosis guide policy can be determined.

For example, "the remote medical client and the data security reference indicate data threat response information of the pairing result of the reference indication in the online visit guiding policy to be identified" may be understood as data threat response information corresponding to the remote medical client and the data security guidance on the minimum guiding element level, so that refined analysis of the data threat response information may be achieved. In this way, after the data threat response information of the reference indication pairing result of the remote medical client and the data security reference indication in the to-be-identified online diagnosis guiding policy is determined as the data security prompt information of the to-be-identified online diagnosis guiding policy in the third diagnosis interactive attention analysis state, the accuracy of the data security prompt information of the to-be-identified online diagnosis guiding policy in the third diagnosis interactive attention analysis state can be ensured. Therefore, through the determination of the data security prompt information under the different diagnosis interaction attention analysis states, the data security prompt information under the different diagnosis interaction attention analysis states can be ensured to be different as much as possible, so that the comprehensiveness and the abundance during subsequent information processing and analysis are improved, and the reduction of the reliability of misleading analysis of the guideline due to the fact that the data security prompt information under the different diagnosis interaction attention analysis states is similar is avoided.

S32: and determining a data threat response log of the to-be-identified online diagnosis guiding policy in at least two diagnosis interaction attention analysis states according to the data security prompt information of the to-be-identified online diagnosis guiding policy in the at least two diagnosis interaction attention analysis states.

In a related embodiment which can be independently implemented, the to-be-identified online diagnosis guiding policy comprises a plurality of types of data security prompt information, and the to-be-identified online diagnosis guiding policy has different combination modes of the data security prompt information in different diagnosis interaction attention analysis states. For example, the data security prompt information of the to-be-identified online diagnosis guide policy under the diagnosis interaction attention analysis state condition1 may be { prompt information1, prompt information2, prompt information3, prompt information5}, the data security prompt information of the to-be-identified online diagnosis guide policy under the diagnosis interaction attention analysis state condition2 may be { prompt information2, prompt information5, prompt information6, prompt information9}, and the data security prompt information of the to-be-identified online diagnosis guide policy under the diagnosis interaction attention analysis state condition3 may be { prompt information1, prompt information3, prompt information4, prompt information 7.

Further, the data security prompt message can be used for instructing the remote medical client to perform related online diagnosis interaction operation. It will be appreciated that some telemedicine clients that have just participated in or come into contact with the online medical service may not be very skilled in the flow of the online medical service and may perform non-secure operations due to non-normative operations or misleading by hackers, which may result in data information risks and asset risks for the telemedicine clients. And the data security prompt information can guide the remote medical client to carry out safe and correct online diagnosis interactive operation. It can be understood that the data security prompt information and the data threat response log have relativity. For example, the data security prompt message may be understood as being sent by the medical server to the remote medical client, and the data threat response log may be understood as being fed back to the medical server by the remote medical client. For example, the data security prompt information "please process according to the step1-step2-step3 operation sequence", the corresponding data threat response log may be "processed according to the step1-step2-step3 operation sequence", may also be "processed according to the step2-step3-step1 operation sequence", or "processed according to the step1-step3-step2 operation sequence", which is not limited herein. In a related embodiment which can be independently implemented, the medical server can obtain the guide policy use condition of the online diagnosis guide policy to be identified in at least two diagnosis interaction attention analysis states according to the data security prompt information of the online diagnosis guide policy to be identified in the at least two diagnosis interaction attention analysis states, and perform log information identification on the guide policy use condition to obtain the data threat response log of the online diagnosis guide policy to be identified in each diagnosis interaction attention analysis state.

For example, the guiding policy usage may be operation feedback of the remote medical client for the to-be-identified online diagnosis guiding policy, which is obtained by the medical server based on the to-be-identified online diagnosis guiding policy, and the guiding policy usage may include diagnosis operation content corresponding to the remote medical client. Further, log information identification of the use condition of the guide policy can be realized in a natural language analysis or feature mining mode, so that a data threat response log of the to-be-identified online diagnosis guide policy in each diagnosis interaction attention analysis state can be accurately obtained.

Further, on the basis of the above-described related implementation manner in which the medical server obtains the data threat response log of the to-be-identified online diagnosis guidance policy in each diagnosis interaction attention analysis state, generating, according to the data security prompt information of the to-be-identified online diagnosis guidance policy in at least two diagnosis interaction attention analysis states, a guidance policy usage situation of the to-be-identified online diagnosis guidance policy in the at least two diagnosis interaction attention analysis states, includes: generating a first guiding policy use condition, a second guiding policy use condition and a third guiding policy use condition according to data security prompt information of the to-be-identified online diagnosis guiding policy in at least two diagnosis interaction attention analysis states; the first guide policy usage is used for representing data threat response information between the remote medical client tag and the data threat response log, the second guide policy usage is used for representing data threat response information between the data threat response log and the data security reference indication index, and the third guide policy usage is used for representing data threat response information between the remote medical client tag and the data security reference indication index; and respectively analyzing the first guiding policy using condition, the second guiding policy using condition and the third guiding policy using condition into guiding policy using conditions of the to-be-identified online diagnosis guiding policy in the at least two diagnosis interaction attention analyzing states. By the design, the guide policy using condition of the to-be-identified online diagnosis guide policy in the at least two diagnosis interaction attention analyzing states can be determined based on different guide policy using conditions. For example, the remote medical client tag may be a tag or a device identifier of a client user, and the data security reference indication index may record a plurality of data security reference indications from a time sequence perspective.

On the basis of the determination of the use condition of the first guiding policy, the use condition of the second guiding policy and the use condition of the third guiding policy, because the data threat response log is considered in the use condition of the first guiding policy and the use condition of the second guiding policy, when the data threat response log of the to-be-identified online medical guide in the corresponding medical interaction attention analysis state is determined, the following two embodiments can be implemented.

In a first embodiment, if the guiding policy usage is the first guiding policy usage or the second guiding policy usage, the performing log information identification on the guiding policy usage to obtain the data threat response log of the to-be-identified online medical guideline in the at least two medical interaction attention analysis states includes: and carrying out log information identification on the use condition of the guide policy to obtain a data threat response log which is used as the data threat response log of the to-be-identified online diagnosis guide policy in the corresponding diagnosis interaction attention analysis state. Because the first guide policy use condition or the second guide policy use condition contains the relevant information of the data threat response log, the data threat response log can be directly obtained in a log information identification mode.

In a second embodiment, if the guiding policy usage is the third guiding policy usage, the performing log information identification on the guiding policy usage to obtain the data threat response log of the to-be-identified online medical guideline in the at least two medical interactive attention analysis states includes: carrying out log information identification on the use condition of the guide policy to obtain a data security reference indication index in the to-be-identified online medical treatment guide policy; acquiring a reference indication pairing result of the data security reference indication in the to-be-identified online medical guide policy, wherein the reference indication pairing result is used as a reference indication pairing result corresponding to the data security reference indication index; and obtaining a data threat response log according to the data security reference indication index and the corresponding reference indication pairing result, wherein the data threat response log is used as the data threat response log of the to-be-identified online diagnosis guide policy in the corresponding diagnosis interaction attention analysis state. By the design, the credibility of the data threat response log of the to-be-identified online diagnosis guide policy in the corresponding diagnosis interaction attention analysis state can be ensured based on the data security reference indication index and the corresponding reference indication pairing result.

And S33, obtaining visual interactive misleading information among the to-be-identified online diagnosis guide guidelines according to the data threat response logs of the to-be-identified online diagnosis guide guidelines in at least two diagnosis interactive attention analysis states.

In a related, independently implementable embodiment, the visual interactive misleading information may be used to describe guideline relevance and guideline misleading between online visit guide guidelines. For example, the higher the quantitative evaluation value corresponding to the visual interaction misleading information between the to-be-identified online diagnosis guide guidelines is, the higher the guideline correlation and the guideline misleading between the to-be-identified online diagnosis guide guidelines are. For example, for the case of the misdirection of the guideline guidance between different online visit guide guidelines, taking the to-be-identified online visit guide guideline 1 and the to-be-identified online visit guide guideline 2 as an example, if the to-be-identified online visit guide guideline 1 indicates that the remote medical client performs the remote visit interaction according to the voice interaction mode, and the to-be-identified online visit guide guideline 2 indicates that the remote medical client performs the remote visit interaction according to the touch interaction mode, when the to-be-identified online visit guide guideline 1 and the to-be-identified online visit guide guideline 2 are allocated to the remote medical client, confusion may occur in the remote visit interaction of the remote medical client. Therefore, by analyzing visual interactive misleading information among different online diagnosis guide policies to be identified, the online diagnosis guide policies can be improved before the online diagnosis guide policies are distributed, so that the determined online diagnosis guide policies aiming at the diagnosis interactive attention can not be misled mutually, the remote medical client can correctly and orderly perform online diagnosis interactive operation according to the relevant guide policies after receiving different online diagnosis guide policies aiming at the diagnosis interactive attention, the user data safety of the remote medical client in the remote medical process is ensured, and the loss of privacy data or important data caused by the misleading of online diagnosis operation errors or hackers is avoided. In a related embodiment, which can be implemented independently, the medical server may determine, according to the data threat response log of the to-be-identified online diagnosis guidance policy in the at least two diagnosis interaction attention analysis states, the policy relevance coefficient of each two to-be-identified online diagnosis guidance policies in the at least two diagnosis interaction attention analysis states, and determine, according to the policy relevance coefficient of each two to-be-identified online diagnosis guidance policies in the at least two diagnosis interaction attention analysis states, the visual interaction misleading information between each two to-be-identified online diagnosis guidance policies, so as to obtain the visual interaction misleading information between each to-be-identified online diagnosis guidance policy. For example, the value range of the guideline correlation coefficient may be 0-1, and the higher the value of the guideline correlation coefficient is, the higher the correlation degree and the misleading degree of each two to-be-identified online diagnosis guide guidelines in at least two diagnosis interaction attention analysis states are. Therefore, quantitative analysis can be carried out on the medical interaction misleading, and therefore the visual interaction misleading information among all the to-be-identified online diagnosis guide guidelines can be accurately obtained.

In the above further embodiment of determining the visual interactive misleading information, the visual interactive misleading information may be determined by means of an Artificial Intelligence (AI) technique, and further, the visual interactive misleading information between the to-be-identified online medical consultation guide guidelines may be determined by means of a relevant neural network model. In order to achieve the above object, the step "obtaining visual interactive misleading information between each to-be-identified online diagnosis guide policy according to the policy relevance coefficient of each two to-be-identified online diagnosis guide policies in at least two diagnosis interactive attention analysis states" may include the following contents: inputting the guideline correlation coefficients of every two to-be-identified online diagnosis guide guidelines in at least two diagnosis interaction attention analysis states into an interaction attention processing network which is trained in advance; confirming the timeliness of the threat coping guidelines among the guideline correlation coefficients through the interactive attention processing network; acquiring reference indication pairing results of the guideline correlation coefficients and reference indication pairing results of timeliness of the threat coping guidelines; and determining to obtain visual interactive misleading information among the to-be-identified online diagnosis guide guidelines according to the guideline correlation coefficients and the corresponding reference indication pairing results, and the timeliness of the threat coping guidelines and the corresponding reference indication pairing results.

In a related embodiment, which can be implemented independently, the interactive attention processing network may be a Neural network model (Neural Networks, NN), and by pre-training the Neural network model, the threat response policy timeliness between the policy relevancy coefficients can be obtained based on the policy relevancy coefficients of each of the two online diagnosis guidelines to be identified in at least two diagnosis interaction attention analysis states, and the threat response policy timeliness is used for characterizing the safety guidance stability and the persistence of different online diagnosis guidance policies, further, by obtaining the reference indication pairing result of each of the policy relevancy coefficients and the reference indication pairing result of each of the threat response policy timeliness, the policy relevancy coefficients, the threat response policy timeliness and their respective reference indication pairing results can be analyzed comprehensively, therefore, the visual interactive misleading information among the to-be-identified online diagnosis guide policies is obtained completely, accurately and reliably, timeliness of the threat coping policies can be guaranteed by the visual interactive misleading information, stability and continuity of the guide policies obtained through optimization can be guaranteed when the follow-up policies are improved, a reliable guide policy is provided for the remote medical client, and user data safety of the remote medical client in the online diagnosis service handling process is guaranteed.

Further, in the practical implementation process, the interactive attention processing network can be obtained by training through the following steps: acquiring guideline correlation coefficients and corresponding first reference indication pairing results of sample online diagnosis guide guidelines in at least two diagnosis interaction attention analysis states, threat response guideline timeliness and corresponding second reference indication pairing results among the guideline correlation coefficients, and actual visual interaction misleading information among the sample online diagnosis guide guidelines; training the interactive attention processing network according to the guideline relevance coefficients and the corresponding first reference indication pairing results of the sample online diagnosis guide guidelines in at least two diagnosis interactive attention analysis states, the threat coping guideline timeliness among the guideline relevance coefficients and the corresponding second reference indication pairing results to obtain the trained interactive attention processing network; obtaining the quantitative difference of the attention degree between the visual interaction misleading information output by the trained interaction attention degree processing network and the corresponding actual visual interaction misleading information; and when the quantized difference of the attention degree is larger than or equal to a preset quantized difference value, improving the first reference indication pairing result and the second reference indication pairing result according to the quantized difference of the attention degree, and performing staged training on the interactive attention degree processing network according to the improved first reference indication pairing result and the improved second reference indication pairing result until the quantized difference of the attention degree obtained according to the trained interactive attention degree processing network is smaller than the preset quantized difference value. In a related embodiment which can be independently implemented, the quantitative difference of the attention can be used for representing the prediction rate of the interactive attention processing network, and the related network parameters of the interactive attention processing network can be indirectly improved by improving the first reference indication pairing result and the second reference indication pairing result, so that the staged training of the interactive attention processing network is realized, and the accurate and stable interactive attention processing network can be ensured. It is to be understood that the preset quantized disparity value is selected according to the actual scene, and is not limited herein.

S34, determining the online diagnosis guide policy of each to-be-identified online diagnosis guide policy aiming at the diagnosis interaction attention degree according to the visual interaction misleading information among the to-be-identified online diagnosis guide policies; online encounter guidance guidelines in terms of encounter interaction attention are used for distribution to corresponding telemedicine clients.

For example, the online diagnosis guidance policy of each to-be-identified online diagnosis guidance policy with respect to the attention of the diagnosis interaction may be understood as the online diagnosis guidance policy after policy improvement for each to-be-identified online diagnosis guidance policy. In the embodiment of the present application, "regarding the attention degree of the diagnosis interaction" may be understood as referring to the misleading angle of the diagnosis interaction and the associated angle of the diagnosis interaction, that is, each online diagnosis guidance policy regarding the attention degree of the diagnosis interaction can be compatible with other online diagnosis guidance policies regarding the attention degree of the diagnosis interaction, so as to avoid repeated guidance problems regarding similar interaction operations or occurrence of misleading/conflicting guidance problems regarding similar interaction operations. In this way, when the on-line doctor seeing guide policy aiming at the doctor seeing interaction attention degree is distributed to the corresponding remote medical client, the remote medical client can be ensured not to generate errors in subsequent operations due to contradiction of guide logic between the on-line doctor seeing guide policies.

By way of example, the online consultation guidance policy for the consultation interaction attention aspect of the to-be-identified online consultation guidance policy1 is policy1-1, the online consultation guidance policy for the consultation interaction attention aspect of the to-be-identified online consultation guidance policy2 is policy2-1, the to-be-identified online consultation guidance policy1 can instruct the remote medical client to conduct remote consultation interaction according to a voice interaction mode, the to-be-identified online consultation guidance policy2 can instruct the remote medical client to conduct remote consultation interaction according to a touch interaction mode, the optimized online consultation guidance policy1-1 can instruct the remote medical client to conduct biometric information collection, verification and warehousing, the optimized online consultation guidance policy2-1 can instruct the remote medical client to conduct remote consultation interaction according to the touch interaction mode, thus, when the online visit guide policy1-1 and the online visit guide policy2-1 are assigned to the remote medical client, the remote medical client can perform relevant operations without interfering with each other according to the guidance of the online visit guide policy1-1 and the online visit guide policy 2-1.

In a related independently implementable embodiment, the step of "determining an online medical treatment guidance policy for each of the to-be-identified online medical treatment guidance policies with respect to the medical treatment interaction attention degree according to the visual interaction misleading information between each of the to-be-identified online medical treatment guidance policies" may include: and respectively according to the visual interactive misleading information among the to-be-identified online diagnosis guiding guidelines, taking the to-be-identified online diagnosis guiding guidelines, which are used as the to-be-identified online diagnosis guiding guidelines aiming at the aspect of the to-be-identified online diagnosis guiding guidelines, wherein the guideline interactive attention degree corresponding to the visual interactive misleading information among the to-be-identified online diagnosis guiding guidelines is greater than the first set diagnosis interactive attention degree and less than the second set diagnosis interactive attention degree. For example, the guideline interaction attention can be understood as the misleading degree and the correlation degree between the to-be-identified online diagnosis guide guidelines. Generally speaking, the guideline interaction concerns are not suggested to be too small, otherwise the remote medical client can start more functional threads with larger differences, and the processing efficiency of the on-line medical service of the remote medical client is reduced. Too much attention is not suggested for guideline interaction, which would otherwise lead to contradictions between the guidance guidelines. Therefore, the guideline interaction attention degree corresponding to the visual interaction misleading information among the to-be-identified online diagnosis guide guidelines is larger than the first set diagnosis interaction attention degree and smaller than the second set diagnosis interaction attention degree, and the to-be-identified online diagnosis guide guidelines are used as the online diagnosis guide guidelines aiming at the diagnosis interaction attention degree of the to-be-identified online diagnosis guide guidelines, so that the online diagnosis service processing efficiency of the remote medical client can be ensured, and errors of the remote medical client in the online diagnosis service processing process caused by contradiction among the guide guidelines can be avoided.

In some embodiments, which may be implemented independently, after the step of determining the online medical treatment guidance policy for each of the to-be-identified online medical treatment guidance policies with respect to the medical treatment interaction attention, "the method may further include the following: acquiring a guide policy calling path of the online medical treatment guide policy to be identified; guiding the online diagnosis guide policy aiming at the diagnosis interaction attention degree of the online diagnosis guide policy to be identified into an online diagnosis wind control network corresponding to the guide policy calling path; and configuring the online treatment wind control network. For example, the guidance policy calling path is used for representing an index path directory for obtaining the online diagnosis guidance policy, and the online diagnosis guidance policy for the diagnosis interaction attention degree is led into the online diagnosis wind control network corresponding to the guidance policy calling path, so that the online diagnosis wind control network is trained and optimized based on the online diagnosis guidance policy for the diagnosis interaction attention degree, and subsequent model use is facilitated. Configuring the online visit wind control network may be understood as deploying and configuring the model in a medical server. It is understood that the online visit wind control network can also be a machine learning-based network model. Based on the deployment of the online medical treatment wind control network, the medical server may also use the above online medical treatment wind control network in the interaction process with the remote medical client, such as: receiving an on-line treatment application sent by the remote medical client; the online treatment application carries a key label corresponding to the guiding policy calling path; extracting an online diagnosis guide policy aiming at the aspect of diagnosis interaction attention from the online diagnosis wind control network corresponding to the guide policy calling path; and allocating the on-line doctor visit guiding policy aiming at the doctor visit interaction attention degree to the remote medical client. For example, before performing online medical service processing, the remote medical client may synchronously send an online medical application to the medical server, and the guidance policy calling path corresponds to a key tag for instructing the medical server to determine a corresponding online medical guidance policy. Furthermore, the medical server can extract the online diagnosis guiding policy aiming at the diagnosis interaction attention degree from the online diagnosis wind control network corresponding to the guiding policy calling path according to the label, and the online diagnosis guiding policy aiming at the diagnosis interaction attention degree is determined to be updated in time because the online diagnosis wind control network is updated in time, so that the timeliness and the medical service matching of the online diagnosis guiding policy aiming at the diagnosis interaction attention degree distributed to the remote medical client can be ensured, and the safety of user data information and the safety of medical charge can be ensured when the remote medical client performs online diagnosis interaction operation according to the online diagnosis guiding policy.

In some independently implementable embodiments, a model training method applied to big data and smart medicine is also provided, which may include the following.

A model training method applied to big data and smart medicine, applied to a medical server, the method comprising:

acquiring guideline correlation coefficients and corresponding first reference indication pairing results of sample online diagnosis guide guidelines in at least two diagnosis interaction attention analysis states, threat response guideline timeliness and corresponding second reference indication pairing results among the guideline correlation coefficients, and actual visual interaction misleading information among the sample online diagnosis guide guidelines;

training the interactive attention processing network according to the guideline relevance coefficients and the corresponding first reference indication pairing results of the sample online diagnosis guide guidelines in at least two diagnosis interactive attention analysis states, the threat coping guideline timeliness among the guideline relevance coefficients and the corresponding second reference indication pairing results to obtain the trained interactive attention processing network;

obtaining the quantitative difference of the attention degree between the visual interaction misleading information output by the trained interaction attention degree processing network and the corresponding actual visual interaction misleading information;

and when the quantized difference of the attention degree is larger than or equal to a preset quantized difference value, improving the first reference indication pairing result and the second reference indication pairing result according to the quantized difference of the attention degree, and performing staged training on the interactive attention degree processing network according to the improved first reference indication pairing result and the improved second reference indication pairing result until the quantized difference of the attention degree obtained according to the trained interactive attention degree processing network is smaller than the preset quantized difference value.

The method of claim close 1, further comprising:

inputting the guideline correlation coefficients of every two to-be-identified online diagnosis guide guidelines in at least two diagnosis interaction attention analysis states into the interaction attention processing network;

confirming the timeliness of the threat coping guidelines among the guideline correlation coefficients through the interactive attention processing network;

acquiring reference indication pairing results of the guideline correlation coefficients and reference indication pairing results of timeliness of the threat coping guidelines;

and determining to obtain visual interactive misleading information among the to-be-identified online diagnosis guide guidelines according to the guideline correlation coefficients and the corresponding reference indication pairing results, and the timeliness of the threat coping guidelines and the corresponding reference indication pairing results.

The method of claim2, further comprising:

determining the online diagnosis guiding policy aiming at the diagnosis interaction attention degree of each to-be-identified online diagnosis guiding policy according to the visual interaction misleading information among the to-be-identified online diagnosis guiding policies; the online encounter guidance policy for encounter interaction attention is for distribution to corresponding telemedicine clients.

The method of claim3, further comprising:

acquiring a guide policy calling path of the online medical treatment guide policy to be identified;

guiding the online diagnosis guide policy aiming at the diagnosis interaction attention degree of the online diagnosis guide policy to be identified into an online diagnosis wind control network corresponding to the guide policy calling path;

and configuring the online treatment wind control network.

The method of claim4, further comprising:

receiving an on-line treatment application sent by the remote medical client; the online treatment application carries a key label corresponding to the guiding policy calling path;

extracting an online diagnosis guide policy aiming at the aspect of diagnosis interaction attention from the online diagnosis wind control network corresponding to the guide policy calling path;

and allocating the on-line doctor visit guiding policy aiming at the doctor visit interaction attention degree to the remote medical client.

It is understood that, for the above description of the model training method applied to big data and smart medical treatment, reference may be made to the description of the method shown in fig. 3, which is not repeated herein.

In view of the above-mentioned data processing method applied to smart medical treatment and big data, an exemplary data processing apparatus applied to smart medical treatment and big data is further provided in the embodiments of the present invention, as shown in fig. 4, the data processing apparatus 400 applied to smart medical treatment and big data may include the following functional modules.

The misleading information obtaining module 410 obtains visual interactive misleading information between the to-be-identified online diagnosis guide guidelines based on the obtained data security prompt information of the to-be-identified online diagnosis guide guidelines in at least two diagnosis interactive attention analysis states.

A guidance policy determining module 420, configured to determine, according to the visual interactive misleading information between the to-be-identified online diagnosis guidance policies, an online diagnosis guidance policy for the to-be-identified online diagnosis guidance policy with respect to the diagnosis interactive attention degree; the online encounter guidance policy for encounter interaction attention is for distribution to corresponding telemedicine clients.

In an actual implementation process, for further description of the misleading information obtaining module 410 and the guidance policy determining module 420, reference may be made to the description of the method shown in fig. 3, which is not described herein again.

It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.