1. A medical information pushing method based on intelligent medical big data is applied to an intelligent medical system, the intelligent medical system is in communication connection with a plurality of intelligent medical subscription devices, and the method comprises the following steps:

obtaining interest feedback behaviors of the intelligent medical subscription equipment aiming at pushed medical e-commerce information;

when an information selection instruction of the interest feedback behavior on the medical e-commerce information is detected, performing interest matching processing on the interest feedback behavior to obtain a corresponding interest matching result;

if the interest matching result is matching failure, detecting whether the interest feedback behavior belongs to an invalid interest feedback behavior, wherein the invalid interest feedback behavior is used for representing misoperation feedback behavior;

and if the interest feedback behavior belongs to an invalid interest feedback behavior, migrating the selected medical e-commerce information fragment of the information selection instruction from the medical e-commerce information to a pre-generated interest pushing fragment, wherein at least part of the same data information exists between the interest pushing fragment and the medical e-commerce information.

2. The method for pushing medical information based on intelligent medical big data according to claim 1, wherein if the interest matching result is a matching failure, the step of detecting whether the interest feedback behavior belongs to an invalid interest feedback behavior comprises:

if the interest matching result is matching failure, acquiring a pre-formed and stored invalid interest feedback behavior set from a target behavior library, and acquiring feedback behavior tag information of the interest feedback behavior;

judging whether the invalid interest feedback behavior set comprises feedback behavior tag information of the interest feedback behavior, and determining that the interest feedback behavior belongs to an invalid interest feedback behavior when the invalid interest feedback behavior set comprises the feedback behavior tag information of the interest feedback behavior.

3. The method for pushing medical information based on intelligent medical big data according to claim 2, wherein if the interest matching result is a matching failure, the step of detecting whether the interest feedback behavior belongs to an invalid interest feedback behavior further comprises:

if the invalid interest feedback behavior set does not include feedback behavior tag information of the interest feedback behavior, determining that the interest feedback behavior does not belong to an invalid interest feedback behavior; or

If the invalid interest feedback behavior set does not include the feedback behavior tag information of the interest feedback behavior, calculating a behavior correlation degree between each invalid interest feedback behavior in the invalid interest feedback behavior set and the interest feedback behavior, and determining whether the interest feedback behavior belongs to the invalid interest feedback behavior or not based on the behavior correlation degree between each invalid interest feedback behavior in the invalid interest feedback behavior set and the interest feedback behavior.

4. The intelligent medical big data-based medical information pushing method according to any one of claims 1-3, further comprising a step of generating the interest pushing segment for the medical e-commerce information, the step including:

performing frequent item mining on the medical E-commerce information to obtain corresponding frequent item mining information;

acquiring at least one frequent item segment from the medical e-commerce information based on the frequent item mining information, wherein the at least one frequent item segment forms the medical e-commerce information, and the contact ratio between different frequent item segments is smaller than a preset contact ratio;

respectively determining the ranking weight of each frequent item segment in the at least one frequent item segment based on the frequent item mining information to obtain the ranking weight information of each frequent item segment;

clustering the at least one frequent item segment based on the sorting weight information and predetermined sorting weight threshold information to obtain at least one first-class frequent item segment, or obtaining at least one first-class frequent item segment and at least one second-class frequent item segment, wherein the sorting weight information of each first-class frequent item segment is greater than or equal to the sorting weight threshold information, and the sorting weight information of each second-class frequent item segment is less than the sorting weight threshold information;

respectively enriching the content of each first-type frequent item segment to obtain a rich frequent item segment corresponding to each first-type frequent item segment;

constructing the interest pushing segment corresponding to the medical e-commerce information based on each rich frequent item segment and each second type frequent item segment.

5. The method for pushing medical information based on intelligent medical big data according to any one of claims 1-4, wherein the method further comprises:

acquiring E-commerce hotspot information of each relevant mining tag in a corresponding target medical E-commerce data source according to the big data mining information of the intelligent medical subscription equipment;

acquiring the E-commerce hotspot distribution characteristics of the relevant mining tags according to the E-commerce hotspot information of the relevant mining tags;

fusing the E-commerce hotspot distribution characteristics of the relevant mining labels to obtain mining hotspot distribution characteristics of the target medical E-commerce data source;

performing medical e-commerce push attribute decision on the target medical e-commerce data source according to the mining hotspot distribution characteristics to obtain at least one medical e-commerce push attribute of the target medical e-commerce data source;

and carrying out corresponding medical e-commerce information pushing on the intelligent medical subscription equipment according to at least one medical e-commerce pushing attribute of the target medical e-commerce data source.

6. The method for pushing medical information based on intelligent medical big data according to claim 5, wherein the step of obtaining the e-commerce hotspot distribution characteristics of each relevant mining tag according to the e-commerce hotspot information of each relevant mining tag comprises:

for the E-commerce hotspot information of each relevant mining label, carrying out hotspot public opinion information extraction on the E-commerce hotspot information of the relevant mining label to obtain hotspot public opinion information corresponding to the E-commerce hotspot information of the relevant mining label;

carrying out public opinion trend characteristic identification on the hot public opinion information of the e-commerce hotspot information of the relevant mining label, and determining at least one public opinion trend characteristic of the hot public opinion information of the e-commerce hotspot information of the relevant mining label;

performing hot point extraction on each public opinion trend feature in the hot point public opinion information of the E-commerce hotspot information of the relevant mining label to obtain the public opinion trend hot point feature of each public opinion trend feature of the E-commerce hotspot information of the relevant mining label;

and according to the preset weighting information of the decision result of the target medical e-commerce data source of the e-commerce hotspot information of the relevant mining label, fusing the hotspot public opinion information of the e-commerce hotspot information of the relevant mining label and the public opinion trend hotspot characteristics of the public opinion trend characteristics to obtain the e-commerce hotspot distribution characteristics of the relevant mining label.

7. The method as claimed in claim 6, wherein the step of fusing the hot public opinion information of the e-commerce hotspot information of the related mining tag and the public opinion trend hotspot characteristics of the public opinion trend characteristics to obtain the e-commerce hotspot distribution characteristics of the related mining tag comprises:

determining fusion parameters corresponding to all public opinion trend characteristics of the E-commerce hotspot information of the relevant mining labels according to preset weighted information of all public opinion trend characteristics of the E-commerce hotspot information of the relevant mining labels on the decision result of the target medical E-commerce data source;

and according to the fusion parameters, carrying out weighted calculation on the hot spot public opinion information of the e-commerce hotspot information of the related mining labels and the public opinion trend hotspot characteristics of all the public opinion trend characteristics to obtain the e-commerce hotspot distribution characteristics of the related mining labels.

8. The intelligent medical big data-based medical information pushing method according to claim 6, wherein the step of fusing the e-commerce hotspot distribution characteristics of the relevant mining tags to obtain the mining hotspot distribution characteristics of the target medical e-commerce data source comprises the steps of:

clustering the E-commerce hotspot distribution characteristics of the relevant mining tags to obtain at least one cluster, and determining the core distribution characteristics serving as a clustering core in each cluster;

aiming at each cluster, calculating hot point distribution node characteristics of non-core distribution characteristics and core distribution characteristics in the cluster to obtain a hot point distribution node characteristic sequence of the cluster;

fusing the characteristic sequences of the hot spot distribution nodes of each cluster to obtain the mining hot spot distribution characteristics of the target medical e-commerce data source;

the clustering the e-commerce hotspot distribution characteristics of the relevant mining tags to obtain at least one cluster, and determining the core distribution characteristics serving as a clustering core in each cluster, includes:

determining the number N of clusters, wherein N is a positive integer greater than or equal to 2;

selecting N e-commerce hotspot distribution characteristics from the e-commerce hotspot distribution characteristics of the relevant mining tags as N clustered core distribution characteristics respectively;

calculating the feature similarity of the E-commerce hotspot distribution features of the relevant mining tags and the core distribution features;

adding each E-commerce hotspot distribution characteristic to a cluster to which a core distribution characteristic with the largest characteristic similarity to the E-commerce hotspot distribution characteristics belongs to obtain N clusters;

and aiming at each cluster, selecting the E-commerce hotspot distribution characteristics meeting the cluster core conditions from the clusters as new core distribution characteristics, returning to execute the step of calculating the characteristic similarity between the E-commerce hotspot distribution characteristics of each relevant mining label and each core distribution characteristic until the core distribution characteristics of each cluster meet the cluster termination requirement, obtaining N clusters, and obtaining the core distribution characteristics serving as cluster cores in each cluster.

9. The method as claimed in claim 7, wherein the extracting hot spot public opinion information from the e-commerce hot spot information of the relevant mining tag to obtain the hot spot public opinion information corresponding to the e-commerce hot spot information of the relevant mining tag comprises:

extracting hotspot public opinion information from the e-commerce hotspot information of the relevant mining label through a deep learning network model to obtain hotspot public opinion information corresponding to the e-commerce hotspot information of the relevant mining label;

the public opinion trend feature recognition is carried out on the hot spot public opinion information of the e-commerce hotspot information of the relevant mining label, and at least one public opinion trend feature of the hot spot public opinion information of the e-commerce hotspot information of the relevant mining label is determined, wherein the public opinion trend feature recognition comprises the following steps:

performing public opinion trend feature recognition on the hot spot public opinion information of the e-commerce hotspot information of the relevant mining label through the deep learning network model, and determining at least one public opinion trend feature of the hot spot public opinion information of the e-commerce hotspot information of the relevant mining label;

the performing medical e-commerce push attribute decision on the target medical e-commerce data source according to the mining hotspot distribution characteristics to obtain at least one medical e-commerce push attribute of the target medical e-commerce data source includes:

and performing medical e-commerce push attribute decision on the target medical e-commerce data source according to the mining hotspot distribution characteristics through the deep learning network model to obtain at least one medical e-commerce push attribute of the target medical e-commerce data source.

10. An intelligent medical system, comprising at least one storage medium and at least one processor, the at least one storage medium configured to store computer instructions; the at least one processor is used for executing the computer instructions to execute the intelligent medical big data-based medical information pushing method of any one of claims 1 to 9.

Background

With the rapid growth of networks and technologies in recent years, the generation speed and quantity of digital data have been rapidly increased, and thus, all industries have actively invested in large data applications so far, and the intelligent medical industry is no exception, so that it is expected to speed up the search intention demand of users, and further find out effective information service approaches, thereby providing convenient intelligent medical services for users.

Currently, for an intelligent medical subscription device, related interest feedback behaviors are generally generated for pushed medical e-commerce information, however, some interest feedback behaviors may belong to invalid interest feedback behaviors, and if information is pushed directly, user experience is affected subsequently, so that it is necessary to perform optimization consideration on service pushing experience in such a case.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, an object of the present disclosure is to provide a medical information pushing method and an intelligent medical system based on intelligent medical big data.

In a first aspect, the present disclosure provides a medical information pushing method based on smart medical big data, which is applied to a smart medical system, where the smart medical system is in communication connection with a plurality of smart medical subscription devices, and the method includes:

obtaining interest feedback behaviors of the intelligent medical subscription equipment aiming at pushed medical e-commerce information;

when an information selection instruction of the interest feedback behavior on the medical e-commerce information is detected, performing interest matching processing on the interest feedback behavior to obtain a corresponding interest matching result;

if the interest matching result is matching failure, detecting whether the interest feedback behavior belongs to an invalid interest feedback behavior, wherein the invalid interest feedback behavior is used for representing misoperation feedback behavior;

and if the interest feedback behavior belongs to an invalid interest feedback behavior, migrating the selected medical e-commerce information fragment of the information selection instruction from the medical e-commerce information to a pre-generated interest pushing fragment, wherein at least part of the same data information exists between the interest pushing fragment and the medical e-commerce information.

For example, the embodiment of the present disclosure further provides a medical information model training method based on deep learning, including the following steps:

acquiring a reference data set, wherein the reference data set comprises e-commerce hotspot information of each relevant mining tag in a corresponding target medical e-commerce data source acquired according to different big data mining information and actual medical e-commerce push attributes corresponding to the target medical e-commerce data source;

performing hot public opinion information extraction on the E-commerce hotspot information of the target medical E-commerce data source through a deep learning network model to obtain hot public opinion information corresponding to the E-commerce hotspot information of the target medical E-commerce data source, performing public opinion trend feature recognition on the hot public opinion information of the E-commerce hotspot information of the target medical E-commerce data source, and determining at least one decision public opinion trend feature of the hot public opinion information of the E-commerce hotspot information of the target medical E-commerce data source;

training and obtaining a deep learning network model based on at least one decision public opinion trend characteristic of the hot public opinion information of the E-commerce hotspot information of the target medical E-commerce data source.

For example, in one embodiment, training and obtaining a deep learning network model based on at least one decision public opinion trend feature of the hot public opinion information of the e-commerce hot point information of the target medical e-commerce data source comprises the following steps:

extracting features of each decision public opinion trend feature in the hot point public opinion information of the E-commerce hotspot information of the target medical E-commerce data source to obtain public opinion trend hotspot features of each decision public opinion trend feature of the E-commerce hotspot information of the target medical E-commerce data source, and fusing the hot point public opinion information of the E-commerce hotspot information of the target medical E-commerce data source and the public opinion trend hotspot features of each decision public opinion trend feature according to the preset weighting information of the decision result of the target medical E-commerce data source of each decision public opinion trend feature of the E-commerce hotspot information of the target medical E-commerce data source to obtain E-commerce hotspot distribution features of the E-commerce hotspot information of the target medical E-commerce data source;

fusing E-commerce hotspot distribution characteristics of E-commerce hotspot information of each target medical E-commerce data source to obtain mining hotspot distribution characteristics of the target medical E-commerce data source;

according to the distribution characteristics of the mining hot spots, determining the matching probability of the target medical e-commerce data source on each preset medical e-commerce pushing attribute;

calculating a first model evaluation index value between the matching probability and an actual medical e-commerce pushing attribute of the target medical e-commerce data source;

calculating a gradient descending value of the first model evaluation index value to the mining hotspot distribution characteristic of the target medical e-commerce data source, and calculating public opinion probability distribution corresponding to hotspot public opinion information of e-commerce hotspot information of the target medical e-commerce data source according to the gradient descending value;

determining medical e-commerce pushing attribute information of the target medical e-commerce data source according to the matching probability of the target medical e-commerce data source;

when the medical e-commerce pushing attribute information of the target medical e-commerce data source is consistent with the actual medical e-commerce pushing attribute, acquiring public opinion trend characteristics of hot public opinion information of e-commerce hot spot information of the target medical e-commerce data source according to the public opinion probability distribution, and setting the acquired public opinion trend characteristics as the actual public opinion trend characteristics of the e-commerce hot spot information of the target medical e-commerce data source;

when the medical e-commerce pushing attribute information of the target medical e-commerce data source is not matched with the actual medical e-commerce pushing attribute, acquiring non-public opinion trend characteristics of hot point public opinion information of e-commerce hotspot information of the target medical e-commerce data source according to the public opinion probability distribution, and setting the acquired non-public opinion trend characteristics as the non-actual public opinion trend characteristics of the e-commerce hotspot information of the target medical e-commerce data source;

calculating a second model evaluation index value of the decision public opinion trend characteristic of the E-commerce hotspot information of the target medical E-commerce data source according to the actual public opinion trend characteristic and the non-actual public opinion trend characteristic;

and adjusting parameters of the deep learning network model according to the first model evaluation index value and the second model evaluation index value to obtain the deep learning network model meeting the conditions.

For example, the calculating a second model evaluation index value of the decision-making public opinion trend feature of the e-commerce hotspot information of the target medical e-commerce data source according to the actual public opinion trend feature and the non-actual public opinion trend feature includes:

determining the actual public opinion trend characteristic probability of the decision public opinion trend characteristic according to the behavior similarity of the decision public opinion trend characteristic of the E-commerce hotspot information of the target medical E-commerce data source and the actual public opinion trend characteristic, and the behavior similarity of the decision public opinion trend characteristic of the E-commerce hotspot information of the target medical E-commerce data source and the non-actual public opinion trend characteristic;

determining the matching probability of the decision public opinion trend characteristic as the actual public opinion trend characteristic according to the public opinion trend hotspot characteristic of the decision public opinion trend characteristic through a deep learning network model;

calculating a decision difference parameter of the decision public opinion trend characteristic according to the matching probability of the decision public opinion trend characteristic and the corresponding actual public opinion trend characteristic probability;

calculating regression difference parameters of the decision public opinion trend characteristics according to the decision public opinion trend characteristics of which the actual public opinion trend characteristic probability is not lower than a preset probability threshold, the service nodes in the hot point public opinion information of the E-commerce hotspot information of the target medical E-commerce data source and the service nodes of the actual public opinion trend characteristics in the hot point public opinion information of the E-commerce hotspot information of the target medical E-commerce data source;

and fusing the decision difference parameter and the regression difference parameter to obtain a second model evaluation index value of the decision public opinion trend characteristic of the E-commerce hotspot information of the target medical E-commerce data source.

In a second aspect, the disclosed embodiment further provides a medical information pushing system based on smart medical big data, where the medical information pushing system based on smart medical big data includes a smart medical system and a plurality of smart medical subscription devices in communication connection with the smart medical system;

the intelligent medical system is used for:

obtaining interest feedback behaviors of the intelligent medical subscription equipment aiming at pushed medical e-commerce information;

when an information selection instruction of the interest feedback behavior on the medical e-commerce information is detected, performing interest matching processing on the interest feedback behavior to obtain a corresponding interest matching result;

if the interest matching result is matching failure, detecting whether the interest feedback behavior belongs to an invalid interest feedback behavior, wherein the invalid interest feedback behavior is used for representing misoperation feedback behavior;

and if the interest feedback behavior belongs to an invalid interest feedback behavior, migrating the selected medical e-commerce information fragment of the information selection instruction from the medical e-commerce information to a pre-generated interest pushing fragment, wherein at least part of the same data information exists between the interest pushing fragment and the medical e-commerce information.

According to any one of the above aspects, the present disclosure provides an implementation manner, when an information selection instruction of an interest feedback behavior on medical e-commerce information is received, performing interest matching processing first, and when matching fails, detecting whether the interest feedback behavior belongs to an invalid interest feedback behavior, so as to migrate a selected medical e-commerce information segment of the information selection instruction from the medical e-commerce information to a pre-generated interest push segment when the interest feedback behavior belongs to the invalid interest feedback behavior. Based on the method, the problem that subsequent user experience is affected due to the fact that information is directly pushed when the interest feedback behavior is determined to be the invalid interest feedback behavior can be avoided, and the experience of service pushing is improved.

Drawings

Fig. 1 is a schematic view of an application scenario of a medical information pushing system based on smart medical big data according to an embodiment of the present disclosure;

fig. 2 is a schematic flowchart of a medical information pushing method based on smart medical big data according to an embodiment of the disclosure;

fig. 3 is a schematic functional block diagram of a medical information pushing device based on smart medical big data according to an embodiment of the disclosure;

fig. 4 is a schematic block diagram of a structure of an intelligent medical system for implementing the above medical information pushing method based on intelligent medical big data according to an embodiment of the present disclosure.

Detailed Description

The following describes in detail aspects of embodiments of the present disclosure with reference to the drawings attached hereto. In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, interfaces, techniques, etc. in order to provide a thorough understanding of the particular embodiments of the disclosure.

Fig. 1 is a schematic view of a scenario of a medical information pushing system 10 based on smart medical big data according to an embodiment of the present disclosure. The medical information pushing system 10 based on smart medical big data can include a smart medical system 100 and a smart medical subscription device 200 communicatively connected to the smart medical system 100. The medical information pushing system 10 based on smart medical big data shown in fig. 1 is only one possible example, and in other possible embodiments, the medical information pushing system 10 based on smart medical big data may also include only at least some of the components shown in fig. 1 or may also include other components.

In this embodiment, the intelligent medical system 100 and the intelligent medical subscription device 200 in the intelligent medical big data-based medical information pushing system 10 can cooperatively execute the intelligent medical big data-based medical information pushing method described in the following method embodiments, and the detailed description of the following method embodiments can be referred to in the execution steps of the intelligent medical system 100 and the intelligent medical subscription device 200.

In order to solve the technical problem in the foregoing background, fig. 2 is a schematic flow chart of a medical information pushing method based on smart medical big data according to an embodiment of the present disclosure, which can be executed by the smart medical system 100 shown in fig. 1, and the medical information pushing method based on smart medical big data is described in detail below.

Step S110, when receiving an information selection instruction of the interest feedback behavior on the medical E-commerce information, performing interest matching processing on the interest feedback behavior to obtain a corresponding interest matching result.

In this embodiment, when receiving an information selection instruction of an interest feedback action on the medical e-commerce information, the intelligent medical system 100 may perform an interest matching process on the interest feedback action, so that a corresponding interest matching result may be obtained.

If the interest matching result is a matching failure, step S120 may be executed.

Step S120, detecting whether the interest feedback behavior belongs to an invalid interest feedback behavior.

In this embodiment, when the interest matching result is a matching failure based on step S110, the smart medical system 100 may detect whether the interest feedback behavior belongs to an invalid interest feedback behavior, i.e., determine whether the network attack of the interest feedback behavior is likely to be received.

Wherein the invalid interest feedback behavior is used to represent a misoperation feedback behavior. And, if the interest feedback behavior belongs to the invalid interest feedback behavior, step S130 may be executed.

Step S130, migrating the selected medical e-commerce information segment of the information selection instruction from the medical e-commerce information to a pre-generated interest pushing segment.

In this embodiment, when it is detected based on step S120 that the interest feedback behavior belongs to an invalid interest feedback behavior, the intelligent medical system 100 may migrate the selected medical e-commerce information segment of the information selection instruction from the medical e-commerce information to a pre-generated interest push segment.

Wherein at least partial same data information exists between the interest push fragment and the medical e-commerce information.

Based on the method, when an information selection instruction of the interest feedback behavior for the medical e-commerce information is received, interest matching processing is performed firstly, whether the interest feedback behavior belongs to an invalid interest feedback behavior is detected when matching fails, and when the interest feedback behavior belongs to the invalid interest feedback behavior, a selected medical e-commerce information fragment of the information selection instruction is migrated from the medical e-commerce information to a pre-generated interest push fragment. Based on the method, the problem that subsequent user experience is affected due to the fact that information is directly pushed when the interest feedback behavior is determined to be the invalid interest feedback behavior can be avoided, and the experience of service pushing is improved.

For example, in one embodiment, the interest feedback behavior may be detected based on the following steps:

firstly, if the interest matching result is a matching failure, a pre-formed and stored invalid interest feedback behavior set can be obtained from a target behavior library, and feedback behavior tag information of the interest feedback behavior is obtained. Secondly, judging whether the invalid interest feedback behavior set comprises feedback behavior tag information of the interest feedback behavior, and determining that the interest feedback behavior belongs to the invalid interest feedback behavior when the invalid interest feedback behavior set comprises the feedback behavior tag information of the interest feedback behavior. Based on this, by directly comparing the feedback behavior tag information of the interest feedback behavior with the invalid interest feedback behavior set, the detection efficiency can be fully improved, and the calculation amount is small.

Optionally, on the basis of the above example, if the invalid interest feedback behavior set does not include the feedback behavior tag information of the interest feedback behavior, in an embodiment, if the invalid interest feedback behavior set does not include the feedback behavior tag information of the interest feedback behavior, it may be determined that the interest feedback behavior does not belong to an invalid interest feedback behavior.

For another example, in another embodiment, if the invalid interest feedback behavior set does not include the feedback behavior tag information of the interest feedback behavior, a behavior correlation between each invalid interest feedback behavior in the invalid interest feedback behavior set and the interest feedback behavior may be calculated first, and then, based on the behavior correlation between each invalid interest feedback behavior in the invalid interest feedback behavior set and the interest feedback behavior, it may be determined whether the interest feedback behavior belongs to an invalid interest feedback behavior.

On the basis of the above example, since the interest push segment needs to be used in step S130, the interest push segment also needs to be generated first, wherein a specific manner of generating the interest push segment is not limited, and may be selected according to actual application requirements.

For example, in one embodiment, any common hotspot data segment that is related to the medical e-commerce information and has been published can be obtained as the interest push segment.

For another example, in another embodiment, in order to improve the disguising effect of the interest push segment, i.e., avoid the invalid interest feedback behavior to identify that the interest push segment is not the medical e-commerce information, the interest push segment may be generated based on the following steps:

firstly, performing frequent item mining on the medical e-commerce information to obtain corresponding frequent item mining information;

secondly, segmenting the medical e-commerce information based on the similarity between the frequent item mining information and the data content to obtain at least one frequent item segment, wherein the at least one frequent item segment forms the medical e-commerce information, and the contact ratio between different frequent item segments is smaller than a preset contact ratio;

thirdly, respectively determining the ranking weight of each frequent item segment in the at least one frequent item segment based on the frequent item mining information to obtain the ranking weight information of each frequent item segment;

fourthly, clustering the at least one frequent item segment based on the ranking weight information and predetermined ranking weight threshold information to obtain at least one first-class frequent item segment, or obtaining at least one first-class frequent item segment and at least one second-class frequent item segment, wherein the ranking weight information of each first-class frequent item segment is greater than or equal to the ranking weight threshold information, and the ranking weight information of each second-class frequent item segment is less than the ranking weight threshold information (for example, each frequent item segment with a ranking weight greater than or equal to the ranking weight threshold may be determined as a first-class frequent item segment, each frequent item segment with a ranking weight less than or equal to the ranking weight threshold may be determined as a second-class frequent item segment, wherein if there is no frequent item segment with a ranking weight greater than the ranking weight threshold, any number of frequent item segments may be determined as a first type of frequent item segment; and, the sorting weight threshold may be generated based on configuration operations performed by a user according to an actual application scenario);

fifthly, respectively enriching the content of each first-type frequent item segment to obtain a rich frequent item segment corresponding to each first-type frequent item segment (that is, each first-type frequent item segment can be adjusted, for example, relevant information of data in the first-type frequent item segment is expanded into other disclosed information);

and sixthly, constructing and forming the interest pushing fragment corresponding to the medical e-commerce information based on each rich frequent item fragment and each second type frequent item fragment.

In one embodiment, the above method may further comprise the following steps.

Step a110, obtaining e-commerce hotspot information of each relevant mining tag in the corresponding target medical e-commerce data source according to the big data mining information of the intelligent medical subscription device 200.

In this embodiment, the e-commerce hotspot information of each relevant mining tag can be obtained through the information acquisition service of each relevant mining tag. The e-commerce hotspot information can comprise e-commerce information hotspot information, recent scientific research hotspot information and the like.

And A120, acquiring the E-commerce hotspot distribution characteristics of the relevant mining tags according to the E-commerce hotspot information of the relevant mining tags.

In this embodiment, feature extraction may be performed through a pre-trained AI model, and the extracted features are subjected to processing such as screening and fusion to obtain the e-commerce hotspot distribution feature.

Step A130, fusing the E-commerce hotspot distribution characteristics of the relevant mining labels to obtain mining hotspot distribution characteristics of the target medical E-commerce data source.

In this embodiment, the mining hotspot distribution characteristics of the whole target medical e-commerce data source are obtained by fusing the e-commerce hotspot distribution characteristics of the relevant mining tags, so that the mining hotspot distribution characteristics can reflect the influence of e-commerce hotspot information of the relevant mining tags in the medical e-commerce data source on the whole medical e-commerce data source, and the decision characteristics of the push attribute of the target medical e-commerce data source can be more accurately expressed.

Step A140, performing medical e-commerce push attribute decision on the target medical e-commerce data source according to the mining hotspot distribution characteristics to obtain at least one medical e-commerce push attribute of the target medical e-commerce data source.

Step a150, performing corresponding medical e-commerce information pushing on the intelligent medical subscription device 200 according to at least one medical e-commerce pushing attribute of the target medical e-commerce data source.

In one embodiment, the medical e-commerce push attribute decision of the target medical e-commerce data source may be implemented in step a140 by a classifier, such as a support vector machine or a fully connected deep neural network.

In this embodiment, since the mining hotspot distribution characteristics are obtained by fusing the e-commerce hotspot distribution characteristics of the relevant mining tags, the medical e-commerce push attribute decision of the target medical e-commerce data source executed according to the mining hotspot distribution characteristics can be more accurate.

In one embodiment, step a120 may include the following substeps.

Step A210, for the e-commerce hotspot information of each relevant mining tag, performing hotspot public opinion information extraction on the e-commerce hotspot information of the relevant mining tag to obtain hotspot public opinion information corresponding to the e-commerce hotspot information of the relevant mining tag.

In this embodiment, the e-commerce hotspot information of each relevant mining tag is respectively input into a pre-trained AI model, and one or more convolution processes are performed through the AI model to perform feature extraction on the e-commerce hotspot information, so as to obtain hotspot public opinion information corresponding to the e-commerce hotspot information of the relevant mining tag.

Step A220, performing public sentiment trend characteristic identification on the hot public sentiment information of the e-commerce hotspot information of the relevant mining label, and determining at least one public sentiment trend characteristic of the hot public sentiment information of the e-commerce hotspot information of the relevant mining label.

The E-commerce hotspot information of a single relevant mining tag may have some invalid features, such as universality features; or have some behavior that is too personalized to characterize the hot spot push attributes of the medical e-commerce data source. Therefore, in the embodiment, representative public opinion trend features need to be identified from the hot public opinion information of a single relevant mining label, and the features corresponding to the public opinion trend features will influence the subsequent feature extraction and fusion process.

Step A230, performing hotspot extraction on each public opinion trend feature in the hotspot public opinion information of the e-commerce hotspot information of the relevant mining label to obtain the public opinion trend hotspot features of each public opinion trend feature of the e-commerce hotspot information of the relevant mining label.

In this embodiment, after the public opinion trend features are identified, specific feature vectors corresponding to the public opinion trend features in the hotspot public opinion information may be extracted and hotspot extraction may be performed to obtain the public opinion trend hotspot features corresponding to the public opinion trend features.

Step A240, according to the preset weighting information of the decision result of the target medical e-commerce data source of the e-commerce hotspot information of the relevant mining label, fusing the hotspot public opinion information of the e-commerce hotspot information of the relevant mining label and the public opinion trend hotspot characteristics of the public opinion trend characteristics to obtain the e-commerce hotspot distribution characteristics of the relevant mining label.

In this embodiment, after the public opinion trend hotspot characteristic is determined, the public opinion trend hotspot characteristic and the hotspot public opinion information may be weighted and fused according to the importance of the public opinion trend hotspot characteristic to the medical e-commerce push attribute decision of the medical e-commerce data source. Therefore, the obtained E-commerce hotspot distribution characteristics comprise the global distribution characteristics and the local distribution characteristics of the related mining tags, and the hotspot distribution characteristics of the related mining tags can be more accurately reflected.

In one embodiment, step a240 may include the following substeps.

Step A241, determining fusion parameters corresponding to all public sentiment trend characteristics of the E-commerce hotspot information of the relevant mining labels according to preset weighted information of all public sentiment trend characteristics of the E-commerce hotspot information of the relevant mining labels on the decision result of the target medical E-commerce data source.

In this embodiment, different public opinion trend characteristics may have different preset weighting information, and the fusion parameter may be determined by an influence degree of the public opinion trend characteristics on medical e-commerce push attribute decision for target medical e-commerce data source classification.

Step A242, according to the fusion parameters, performing weighted calculation on the hot spot public sentiment information of the e-commerce hotspot information of the related mining labels and the public sentiment trend hotspot characteristics of all the public sentiment trend characteristics to obtain e-commerce hotspot distribution characteristics of the related mining labels.

In this embodiment, the public opinion information of the e-commerce hotspot information of the relevant mining tag and the public opinion trend hotspot features of each public opinion trend feature are weighted and calculated, so that the obtained e-commerce hotspot distribution features have both the hotspot public opinion information representing the global features of the relevant mining tag and the public opinion trend hotspot features having the characteristic hotspot distribution features, and the public opinion trend hotspot features can reflect the importance of some public opinion trend features through weighting calculation. Therefore, the obtained E-commerce hotspot distribution characteristics can more accurately reflect hotspot distribution characteristics of relevant mining tags, which can influence the target medical E-commerce data source.

In one embodiment, step a130 may include the following sub-steps.

Step A131, clustering the e-commerce hotspot distribution characteristics of the relevant excavation tags to obtain at least one cluster, and determining the core distribution characteristics serving as a clustering core in each cluster.

Step A132, for each cluster, calculating hot spot distribution node characteristics of the non-core distribution characteristics and the core distribution characteristics in the cluster to obtain a hot spot distribution node characteristic sequence of the cluster.

Step A133, fusing the characteristic sequences of the hot spot distribution nodes of each cluster to obtain the mining hot spot distribution characteristics of the target medical e-commerce data source.

In this embodiment, the clustering calculation may be performed by using a K-means (K-means) clustering algorithm.

In one embodiment, step a131 may include the following substeps.

Step 1311, determining the number N of clusters, where N is a positive integer greater than or equal to 2.

Step 1312, selecting N e-commerce hot spot distribution characteristics from the e-commerce hot spot distribution characteristics of the relevant mining tags to serve as N clustered core distribution characteristics respectively.

And 1313, calculating feature similarity between the e-commerce hotspot distribution features of the relevant mining tags and the core distribution features.

In this embodiment, the feature similarity between the e-commerce hotspot distribution feature and the core distribution feature may represent a matching degree between the e-commerce hotspot distribution feature and the core distribution feature. The greater the feature similarity, the greater the degree of matching. The way of calculating the feature similarity between the e-commerce hotspot distribution feature and the core distribution feature may be calculated by a cosine distance or a euclidean distance, etc.

Step 1314, adding each e-commerce hotspot distribution characteristic to the cluster to which the core distribution characteristic with the largest characteristic similarity with the e-commerce hotspot distribution characteristic belongs to respectively to obtain N clusters.

And 1315, selecting the e-commerce hotspot distribution characteristics meeting the clustering core conditions from the clusters as new core distribution characteristics for each cluster, and returning to the step of calculating the feature similarity between the e-commerce hotspot distribution characteristics of each relevant mining tag and each core distribution characteristic until the core distribution characteristics of each cluster meet the clustering termination requirement, so as to obtain N clusters, and obtain the core distribution characteristics serving as clustering cores in each cluster.

In this embodiment, for each cluster, whether the latest core distribution feature of the cluster is the same as the core distribution feature used at the maximum time in the clustering process, that is, whether the feature similarity between the two is 0, is calculated. If the cluster cores are the same, the cluster cores of the cluster are not changed, if all the cluster cores of the cluster are not changed, the clustering process is completed, N clusters are obtained, and the core distribution characteristics serving as the cluster cores in each cluster are obtained; if not all clustered cores have changed, then return to step A1313 until each clustered core has no more changes.

It should be understood that the latest core distribution characteristic of each cluster in the cluster calculation is the same as the cluster core used by the cluster at the maximum time, which is only an optional condition for ending the loop, and the optional condition may also be that the hot spot distribution node characteristic between the two cluster cores is smaller than a certain preset value.

In one embodiment, in step a220, when hot spot public opinion information is extracted from the e-commerce hotspot information of the relevant mining tag to obtain hot spot public opinion information corresponding to the e-commerce hotspot information of the relevant mining tag, the hot spot public opinion information may be extracted from the e-commerce hotspot information of the relevant mining tag through a deep learning network model to obtain hot spot public opinion information corresponding to the e-commerce hotspot information of the relevant mining tag.

In step a220, performing public opinion trend feature recognition on the hot spot public opinion information of the e-commerce hotspot information of the relevant mining tag, and when determining at least one public opinion trend feature of the hot spot public opinion information of the e-commerce hotspot information of the relevant mining tag, performing public opinion trend feature recognition on the hot spot public opinion information of the e-commerce hotspot information of the relevant mining tag through the deep learning network model, and determining at least one public opinion trend feature of the hot spot public opinion information of the e-commerce hotspot information of the relevant mining tag.

In step a140, when a medical e-commerce push attribute decision is made on the target medical e-commerce data source according to the mining hotspot distribution characteristics to obtain at least one medical e-commerce push attribute of the target medical e-commerce data source, the medical e-commerce push attribute decision can be made on the target medical e-commerce data source according to the mining hotspot distribution characteristics through the deep learning network model to obtain at least one medical e-commerce push attribute of the target medical e-commerce data source.

In this embodiment, the deep learning network model may be a residual network, a densely connected convolutional network, or the like.

For example, in one embodiment, the present disclosure further provides a deep learning-based medical information model training method, including the following steps.

Step A401, a reference data set is obtained, wherein the reference data set comprises e-commerce hotspot information of a target medical e-commerce data source and actual medical e-commerce push attributes corresponding to the target medical e-commerce data source.

Step A402, performing hotspot public opinion information extraction on the E-commerce hotspot information of the target medical E-commerce data source through a deep learning network model to obtain hotspot public opinion information corresponding to the E-commerce hotspot information of the target medical E-commerce data source, performing public opinion trend feature recognition on the hotspot public opinion information of the E-commerce hotspot information of the target medical E-commerce data source, and determining at least one decision public opinion trend feature of the hotspot public opinion information of the E-commerce hotspot information of the target medical E-commerce data source.

Step A403, training and obtaining a deep learning network model based on at least one decision public opinion trend characteristic of the hot public opinion information of the E-commerce hotspot information of the target medical E-commerce data source.

For example, in one embodiment, step a403 may be implemented as follows.

Step A4031, extracting features of each decision public opinion trend feature in the hot point public opinion information of the e-commerce hotspot information of the target medical e-commerce data source to obtain the public opinion trend hotspot feature of each decision public opinion trend feature of the e-commerce hotspot information of the target medical e-commerce data source, presetting weighting information of the decision result of the target medical e-commerce data source according to each decision public opinion trend feature of the e-commerce hotspot information of the target medical e-commerce data source, and fusing the hot point public opinion information of the e-commerce hotspot information of the target medical e-commerce data source and the public opinion trend hotspot feature of each decision public opinion trend feature to obtain the e-commerce hotspot distribution feature of the e-commerce hotspot information of the target medical e-commerce data source.

Step A4032, the E-commerce hotspot distribution characteristics of the E-commerce hotspot information of each target medical E-commerce data source are fused to obtain mining hotspot distribution characteristics of the target medical E-commerce data source.

Step A4033, according to the distribution characteristics of the mining hot spots, determining the matching probability of the target medical e-commerce data source on each preset medical e-commerce pushing attribute.

Step A4034, calculating a first model evaluation index value between the matching probability and the actual medical e-commerce pushing attribute of the target medical e-commerce data source.

Step A4035, calculating a gradient descending value of the first model evaluation index value to the mining hotspot distribution characteristic of the target medical e-commerce data source, and calculating public opinion probability distribution corresponding to hotspot public opinion information of the e-commerce hotspot information of the target medical e-commerce data source according to the gradient descending value.

Step A4036, determining medical e-commerce push attribute information of the target medical e-commerce data source according to the matching probability of the target medical e-commerce data source.

Step A4037, when the medical e-commerce pushing attribute information of the target medical e-commerce data source is consistent with the actual medical e-commerce pushing attribute, acquiring the public opinion trend characteristic of the hot public opinion information of the e-commerce hotspot information of the target medical e-commerce data source according to the public opinion probability distribution, and setting the acquired public opinion trend characteristic as the actual public opinion trend characteristic of the e-commerce hotspot information of the target medical e-commerce data source.

Step A438, when the medical e-commerce pushing attribute information of the target medical e-commerce data source is not matched with the actual medical e-commerce pushing attribute, acquiring the non-public opinion trend characteristic of the hot public opinion information of the e-commerce hot spot information of the target medical e-commerce data source according to the public opinion probability distribution, and setting the acquired non-public opinion trend characteristic as the non-actual public opinion trend characteristic of the e-commerce hot spot information of the target medical e-commerce data source.

Step A439, calculating a second model evaluation index value of the decision public opinion trend characteristic of the e-commerce hotspot information of the target medical e-commerce data source according to the actual public opinion trend characteristic and the non-actual public opinion trend characteristic, and adjusting parameters of the deep learning network model according to the first model evaluation index value and the second model evaluation index value to obtain the deep learning network model meeting the conditions.

In this embodiment, a back propagation algorithm may be adopted to adjust parameters of the deep learning network model, so that a first model evaluation index value between the matching probability obtained through the deep learning network model and the actual medical e-commerce push attribute is smaller than a target feature degree, where the target feature degree may be set as small as possible, so as to improve the classification accuracy of the deep learning network model.

Generally, if the matching probability of the deep learning network model on a certain preset medical e-commerce pushing attribute exceeds a threshold value, the target medical e-commerce data source can be considered as the medical e-commerce data source on the preset medical e-commerce pushing attribute. In the training process of the deep learning network model, if the pushing attribute information of the medical e-commerce decided by the deep learning network model is consistent with the pushing attribute of the actual medical e-commerce, namely the decision is correct, public opinion probability distribution can be obtained through analysis according to the parameters related in the decision process, public opinion trend feature recognition can be carried out according to the public opinion probability distribution, and the actual public opinion trend feature of the e-commerce hotspot information of the target medical e-commerce data source is obtained.

In the training process of the deep learning network model, if the pushing attribute information of the medical e-commerce decided by the deep learning network model is not matched with the pushing attribute of the actual medical e-commerce, namely, the pushing attribute of the medical e-commerce of the target medical e-commerce data source is decided wrongly by the deep learning network model, public opinion probability distribution can be obtained through analysis according to the parameters related in the decision process, and non-actual public opinion trend characteristics of the e-commerce hotspot information of the target medical e-commerce data source can be obtained according to the public opinion probability distribution.

For example, in this embodiment, step a439 may comprise the following sub-steps.

Step A501, determining the probability of the non-actual public opinion trend characteristic of the decision public opinion trend characteristic according to the behavior similarity of the decision public opinion trend characteristic of the E-commerce hotspot information of the target medical E-commerce data source and the actual public opinion trend characteristic, and according to the decision public opinion trend characteristic of the E-commerce hotspot information of the target medical E-commerce data source and the behavior similarity of the non-actual public opinion trend characteristic.

Optionally, in an embodiment, the probability of the actual public opinion trend feature of the decision public opinion trend feature having a feature matching degree with the actual public opinion trend feature larger than the first target feature may be set to 1; setting the probability of the actual public opinion tendency feature of the decision public opinion tendency feature with the feature matching degree of the non-actual public opinion tendency feature larger than the second target feature degree to 0; the first target feature degree and the second target feature degree can be set according to actual conditions.

Step A502, determining the matching probability of the decision public opinion trend characteristic as the actual public opinion trend characteristic according to the public opinion trend hotspot characteristic of the decision public opinion trend characteristic through a deep learning network model.

Step A503, calculating a decision difference parameter of the decision public opinion trend characteristic according to the matching probability of the decision public opinion trend characteristic and the corresponding actual public opinion trend characteristic probability.

Step A504, calculating regression difference parameters of the decision public opinion trend characteristics according to the decision public opinion trend characteristics of which the actual public opinion trend characteristic probability is not lower than a preset probability threshold, the service nodes in the hot public opinion information of the E-commerce hotspot information of the target medical E-commerce data source and the service nodes of the actual public opinion trend characteristics in the hot public opinion information of the E-commerce hotspot information of the target medical E-commerce data source.

And step A505, fusing the decision difference parameter and the regression difference parameter to obtain a second model evaluation index value of the decision public opinion trend characteristic of the E-commerce hotspot information of the target medical E-commerce data source.

In one embodiment, public opinion probability distribution can be obtained through analysis of a class activation graph weighted by a gradient descending value, a gradient descending value of a first model evaluation index value to a feature of mining hotspot distribution of a target medical e-commerce data source is calculated, a weight parameter corresponding to each hotspot public opinion object in hotspot public opinion information of e-commerce hotspot information of the target medical e-commerce data source is calculated by using a global average of the gradient descending values, and public opinion probability distribution corresponding to the hotspot public opinion information can be drawn according to the weight parameter of each hotspot public opinion object in the hotspot public opinion information. The mining hotspot distribution characteristics of the target medical e-commerce data source can be obtained by splicing e-commerce hotspot distribution characteristics corresponding to e-commerce hotspot information of each relevant mining tag of the target medical e-commerce data source. The basic idea of the Grad-CAM is that the weight parameter of the feature map corresponding to a certain decision attribute can be converted to express this weight parameter by a back-propagation gradient descent value.

Therefore, by extracting the E-commerce hotspot information of each relevant mining tag in the target medical E-commerce data source, identifying public opinion trend characteristics from the E-commerce hotspot information of a single relevant mining tag, determining the E-commerce hotspot distribution characteristics of each relevant mining tag according to the public opinion trend characteristics, then fusing the E-commerce hotspot distribution characteristics of each relevant mining tag to obtain the mining hotspot distribution characteristics of the whole target medical E-commerce data source, and then performing medical E-commerce push attribute decision on the target medical E-commerce data source according to the mining hotspot distribution characteristics. The determined mining hotspot distribution characteristics can more accurately reflect the influence of the related mining tags on the medical e-commerce data source, so that the characteristics of the whole medical e-commerce data source are more accurately reflected, and the medical e-commerce push attribute decision of the medical e-commerce data source based on the mining hotspot distribution characteristics is more accurate.

For example, in one embodiment, the above method may further comprise the following steps.

And step B110, acquiring medical resource behavior big data of the target subscribed medical resource.

In one embodiment, the target subscribed medical resource may refer to a medical service scenario of practical application, such as a medical drug consultation scenario, a medical department consultation scenario, a medical knowledge point learning scenario, and the like, but is not limited thereto. The medical resource behavior big data may refer to behavior big data generated by a user under the condition that the user subscribes to the medical resource, for example, for a medical knowledge point learning scene, the behavior big data may refer to big data information generated by knowledge point searching behavior, knowledge point sharing behavior and the like of the user.

And step B120, processing the medical resource behavior big data to obtain the search intention information distribution of the medical resource behavior big data.

The search intention information distribution comprises intention distribution information of a target medical pushing label in the medical resource behavior big data, wherein the intention distribution information points to the target in the search. And the search intention information distribution specifically comprises a shared search intention characteristic and a continuously shared search intention characteristic, wherein the shared search intention characteristic comprises a plurality of intention components, and each intention component represents the confidence degree that the behavior interest behavior in the medical resource behavior big data corresponding to the intention component is the intention interest point of the target medical pushing label searched and directed to the target. The dimension of the continuously shared search intention feature is 2, which means that the continuously shared search intention feature is specifically composed of the search intention feature of the feature range attribute and the search intention feature of the feature hierarchy attribute, and the search intention feature of the feature range attribute and the intention keyword interval of the search intention feature of the feature hierarchy attribute are the same. In addition, the intention keyword section sharing the search intention feature is the same as the intention keyword section continuously sharing the search intention feature. For example, both the intention keyword interval sharing the search intention feature and the intention keyword interval continuously sharing the search intention feature are (a 1, a 2.. a.. an.). The search intention characteristics of the characteristic range attribute comprise a plurality of intention components, and each intention component represents a decision characteristic range corresponding to behavior interest behaviors in the medical resource behavior big data corresponding to the intention component; similarly, the search intention feature of the feature level attribute comprises a plurality of intention components, and each intention component represents a decision feature level corresponding to the behavior interest behavior in the medical resource behavior big data corresponding to the intention component.

For example, the representation form of the intent of the embodiment of the present disclosure is a form of sharing + persistent sharing. The search intention information distribution makes decisions on the two, namely sharing the search intention characteristics and continuously sharing the search intention characteristics. The dimension sharing the search intention features is (a 1, a 2.... ann.., an). C, the dimension continuously sharing the intention component features is (a 1, a 2.... ann.. an). 2, and C is the target number of target medical push labels to be decided. Each intention component on the search intention characteristic respectively expresses the confidence of the search pointing to the intention interest point of the target and the decision value of the characteristic range characteristic level at the coordinate position.

Step B130, determining big data mining information of the medical resource behavior big data according to the search intention information distribution, wherein the big data mining information comprises mining label information of a search pointing target of the target medical push label in the medical resource behavior big data.

In one embodiment, mining tag information may include intent objects, which may include, for example: the medical resource behavior big data is decision confidence that the search of the target medical push tag points to the intention interest point of the target, and the medical resource behavior big data is the feature range and the feature level of the intention object corresponding to the search of the target medical push tag.

In one embodiment, the search intent information distribution includes sharing the search intent feature and continuously sharing the search intent feature. The shared search intention features comprise confidence degrees of intention interest points of the target medical pushing label searched by each behavior interest behavior in the medical resource behavior big data, and the continuously shared search intention features comprise feature ranges and feature level data corresponding to each behavior interest behavior in the medical resource behavior big data.

In one embodiment, first, the smart medical system 100 determines the intention interest point of the target for searching the target medical push tag in the medical resource behavior big data according to the shared search intention characteristics. Then, the intelligent medical system 100 determines an intention object of the target in the medical resource behavior big data, which is pointed by the search of the target medical push tag, according to the intention interest point and the feature range and the feature level data corresponding to the behavior interest behavior at the intention interest point. Finally, the smart medical system 100 takes the intention target of the search direction target of the target medical push tag as the mining tag information of the search direction target of the target medical push tag.

For example, each behavioral interest behavior in the medical resource behavior big data corresponds to an intent component in the search intent information distribution. Therefore, big data mining information of the medical resource behavior big data can be determined, and the big data mining information comprises mining label information of a target medical pushing label in the medical resource behavior big data, wherein the target medical pushing label is searched and points to the target.

Subsequently, the smart medical system 100 may label the intention object in the medical resource behavior big data according to the mining tag information of the search direction target of the target medical push tag, for example, the intention interest point of the intention object is (i, j), and since the feature range and the feature level data in the search intention information distribution are converted to the original intention graph keyword interval according to a down-sampling rate (usually 4), the feature range of the intention object in the original medical resource behavior big data is 40, and the feature level is 80. The expression form of the intention object is usually a jump expression form, but the expression form of the intention object may be determined according to a search target of a target medical delivery tag included in the medical resource behavior big data, and if the search target of the target medical delivery tag is a web page, a video stream bullet screen, or the like, the intention object is usually a video stream bullet screen, a web page element, or the like. The representation of the intended object may be flexibly floated according to the search of the target medical push tag, or different business scenarios, which is not specifically limited by the present disclosure. Of course, if the target medical push tag is of more than one type, that is, the target medical push tag includes two or more types, the expression form of each type of the intention object may be different, so as to distinguish different types of search pointing targets in the medical resource behavior big data.

By the medical resource behavior data processing method provided by the embodiment of the disclosure, the medical resource behavior big data of the target subscription medical resource can be processed, so that the search intention information distribution of the medical resource behavior big data is obtained. The search intention information distribution comprises intention distribution information of the target medical pushing label in the medical resource behavior big data, and big data mining information of the medical resource behavior big data is determined according to the search intention information distribution. By the design, the second reference medical resource behavior data of the target subscription medical resource can be trained to obtain the search intention classification unit without marking, and the search intention classification unit can directly process the medical resource behavior data of the target subscription medical resource, so that big data mining information is obtained, marking cost of the medical resource behavior data of the target subscription medical resource can be saved, and efficiency and precision of big data mining are improved.

In one embodiment, the intelligent medical system 100 may invoke the search intention classification unit to process the medical resource behavior big data to obtain the search intention information distribution of the medical resource behavior big data. The search intention classification unit is obtained by performing network weight learning and updating on reference medical resource behavior data of the target subscription medical resource. For example, the search intention classification unit is obtained by training based on first reference medical resource behavior data of the original subscribed medical resource, reference intention distribution information of a search pointing target of a target medical push tag in the first reference medical resource behavior data, and second reference medical resource behavior data of the target subscribed medical resource.

Compared with the prior art, the design is that the second reference medical resource behavior data of the target subscription medical resource can be trained to obtain the search intention classification unit without marking, and the search intention classification unit is obtained by performing network weight learning updating training by using the redundant exclusion information amount of the reference medical resource behavior data of the target subscription medical resource. Finally, the machine learning network obtained through training can directly process the medical resource behavior data of the target subscription medical resource, so that big data mining information is obtained, the marking cost of the medical resource behavior data of the target subscription medical resource can be saved, and the efficiency and the precision of big data mining are improved.

In one embodiment, the search intention classification unit includes a search intention extraction node and a search intention fusion node. For example, a machine learning network includes a search intention extraction node and a search intention fusion node. The specific implementation manner of the intelligent medical system 100 invoking the search intention classification unit to process the medical resource behavior big data to obtain the search intention information distribution of the medical resource behavior big data may include: the intelligent medical system 100 calls the search intention extraction node to perform feature extraction on the medical resource behavior big data so as to obtain initial search intention features of the medical resource behavior big data; and calling the search intention fusion node to perform feature fusion on the medical resource behavior big data and the initial search intention characteristics so as to obtain the search intention information distribution of the medical resource behavior big data. The search intention extraction nodes are composed of convolutional layers, batch regularization, nonlinear activation, pooling layers and the like. The search intention extraction node can effectively extract a feature level dimension feature expression (namely an initial search intention feature) of input medical resource behavior data (medical resource behavior big data).

In one embodiment, first, the smart medical system 100 calls a search intention fusion node to perform convolution processing and sampling processing on medical resource behavior big data and an initial search intention characteristic to obtain a first search intention characteristic; then, the intelligent medical system 100 calls the search intention fusion node to perform compression processing and excitation processing on the medical resource behavior big data and the initial search intention characteristics to obtain influence weights corresponding to the initial search intention characteristics, and performs weighted calculation on the initial search intention characteristics according to the influence weights to obtain second search intention characteristics; finally, the intelligent medical system 100 performs fusion processing on the first to-be-fused search intention feature and the second to-be-fused search intention feature to obtain search intention information distribution of medical resource behavior big data.

For example, the search intention fusion node may include a first fusion structure and a second fusion structure, and the first fusion structure may be, for example, a feature pyramid structure. The feature pyramid structure is a low-level search intention feature representation of an initial search intention feature of the medical resource behavior big data and a feature level layer, and therefore a first search intention feature is obtained. The basic operation unit of the characteristic pyramid structure is also the meta-operation of a convolution layer, batch regularization, nonlinear activation and pooling layer. The second fusion structure may be a compression-excitation module, for example, which may pool the initial search intention features globally and process them by excitation, resulting in impact weights. Finally, the intelligent medical system 100 performs weight comprehensive calculation on the influence weight and the initial search intention feature to obtain a second search intention feature.

Finally, the intelligent medical system 100 performs fusion processing on the first search intention feature and the second search intention feature, so as to obtain search intention information distribution of medical resource behavior big data. Of course, after the initial search intention features of the medical resource behavior big data are processed through the feature pyramid structure, the obtained first search intention features serve as the input of the compression-excitation module, and then the second search intention features are obtained. And finally, taking the second search intention characteristic obtained by processing the compression-excitation module according to the first search intention characteristic as the search intention information distribution of the medical resource behavior big data.

Fig. 3 is a schematic diagram of functional modules of the smart medical big data-based medical information pushing apparatus 300 according to the embodiment of the disclosure, and the smart medical big data-based medical information pushing apparatus 300 may be a computer program (including program code) running in the smart medical system 10010, for example, the smart medical big data-based medical information pushing apparatus 300 is an application software, and the functions of the functional modules of the smart medical big data-based medical information pushing apparatus 300 are described in detail below.

An obtaining module 310, configured to obtain an interest feedback behavior of the intelligent medical subscription device 200 for the pushed medical e-commerce information.

The matching module 320 is configured to, when the information selection instruction of the interest feedback behavior for the medical e-commerce information is detected, perform interest matching processing on the interest feedback behavior to obtain a corresponding interest matching result.

A detecting module 330, configured to detect whether the interest feedback behavior belongs to an invalid interest feedback behavior if the interest matching result is a matching failure, where the invalid interest feedback behavior is used to indicate a misoperation feedback behavior.

A migration module 340, configured to migrate, if the interest feedback behavior belongs to an invalid interest feedback behavior, a selected medical e-commerce information segment of the information selection instruction from the medical e-commerce information to a pre-generated interest push segment, where at least part of the same data information exists between the interest push segment and the medical e-commerce information.

Fig. 4 is a schematic diagram illustrating a hardware structure of an intelligent medical system 100 for implementing the above-mentioned method for pushing medical information based on intelligent medical big data according to an embodiment of the present disclosure, and as shown in fig. 4, the intelligent medical system 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a communication unit 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120, so that the processor 110 may execute the method for pushing medical information based on smart medical big data according to the above method embodiment, the processor 110, the machine-readable storage medium 120, and the communication unit 140 are connected through the bus 130, and the processor 110 may be configured to control the transceiving action of the communication unit 140, so as to perform data transceiving with the smart medical subscription device 200.

The specific implementation process of the processor 110 can be seen in the above-mentioned embodiments of the method performed by the intelligent medical system 100, which have similar implementation principles and technical effects, and the detailed description of the embodiments is omitted here.

In addition, the embodiment of the disclosure also provides a readable storage medium, wherein a computer executing instruction is preset in the readable storage medium, and when a processor executes the computer executing instruction, the above medical information pushing method based on smart medical big data is realized.

While the disclosure has been described with reference to specific exemplary embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure.