1. An intelligent follow-up system, which is characterized in that: the intelligent follow-up system comprises a hospital third-party electronic medical record subsystem, an information data management platform, a user terminal, a hospital terminal and a detection terminal, wherein the user terminal, the detection terminal and the hospital terminal are respectively connected with the information data management platform; wherein, the information data management platform is connected with the hospital third-party electronic medical record subsystem,

the third-party electronic medical record system is used for storing medical record data information of the patient;

the information data management platform is used for storing, managing and sending user information, the information management platform stores label data information of a user, and the user label data information is extracted according to data of a hospital third-party electronic medical record system and a user detection terminal;

the user terminal is used for logging in and information acquisition of a user and sending user information to the information data management platform;

the detection terminal is used for detecting the user sign data and sending the obtained user sign data to the information data management platform;

the hospital terminal is used for checking user information, user sign data, detection analysis results and classification results, performing classification extraction of the user information, performing classification follow-up visits according to user label data of the information data management platform, if the classification results are of a home follow-up visit type, the user is checked and treated by home, if the classification results are of a notification check type, the relevant follow-up visit data are sent to the user, and whether the user is notified to the hospital to perform check and treatment is judged based on the follow-up visit data;

the hospital terminal also comprises an encryption processing unit and a baseband data processing unit, wherein the encryption processing unit encrypts the follow-up data information to ensure the privacy of the user, maps the acquired encryption information of the follow-up data information, the user terminal information and the service data packaging type information to the corresponding bit of the header and sends the header to the baseband data processing unit, and the baseband processing unit encodes and sends the header and the encrypted follow-up data information to be sent together.

2. The intelligent follow-up system as recited in claim 1, further characterized by: the hospital terminal comprises a short message sending unit, wherein the short message sending unit is used for extracting basic information data of a user, selecting keywords to perform aggregation operation to obtain classified followed-up user data information, performing normal distribution sampling on the classified followed-up user data to obtain sampled followed-up user data information, and taking the followed-up user data information as a user for receiving the followed-up information.

3. The intelligent follow-up system as recited in claim 2, further characterized by: the user terminal comprises an emotion sensing unit, and the emotion sensing unit senses the emotion of the user according to an emotion embedding algorithm of the user terminal.

4. The intelligent follow-up system as recited in claim 3, further characterized by: the emotion perception unit of the user terminal classifies the current emotion of the user by adopting a vector machine classification algorithm and combining model parameters obtained by historical data training and real-time measurement data, so that emotion perception is realized.

5. The intelligent follow-up system as recited in claim 4, further characterized by: the hospital terminal comprises a computer terminal, an intelligent small server and a virtual cloud server.

6. The intelligent random access system of claim 5, further characterized by: the user terminal periodically feeds back emotion identification information to a short message sending unit in the hospital terminal, the short message sending platform selects the ID of a group member needing to send follow-up information communication according to the emotion identification information, and sends the encrypted follow-up information, the user name, the ID of the group member needing to send the information and a group emotion identification number to the user terminal connected with a communication network through the communication network; the hospital client acquires emotion identification information of the user terminal, and maps a label of the emotion identification information with the user follow-up visit data so as to execute push of different user follow-up visit information.

7. The intelligent follow-up system as recited in claim 6, further characterized by: the follow-up visit data information comprises data, video, images and voice, and the hospital terminal classifies the follow-up visit data information and encrypts the follow-up visit data information and sends the follow-up visit data information to the coding and decoding unit.

8. The intelligent follow-up system as recited in claim 7, further characterized by: optionally, the user side executes the follow-up data information question and answer form filling, and the user side is provided with a complete detection unit for detecting whether all the information on the follow-up form is recorded completely or not, and detecting whether all the information on the follow-up form is recorded completely or not item by adopting a sequence opposite to the response.

9. The intelligent follow-up system as recited in claim 8, further characterized by: wherein the detection terminal comprises an intelligent wristwatch, a sphygmomanometer, a blood glucose meter, a thermometer and a human body fat scale.

10. The intelligent follow-up system as recited in claim 9, further characterized by: the user terminal is executed through the image acquisition unit and the biological signal acquisition unit, emotion information of a user is identified, the user image acquisition unit is a camera, and the biological signal acquisition unit is a touch pressure identification detector.

Background

At present, the important form of continuous nursing of part of chronic diseases developed in China at present is family visit and telephone follow-up, and although certain nursing intervention can be provided for patients, the number of community professionals is small, so that the continuous nursing duration is short, and the coverage is narrow. The home time of most chronic patients after discharge is still a blind area for treatment and nursing, the psychological health conditions of the patients are lost in monitoring and intervention, information cannot be stored, and the treatment effect and the long-term prognosis are influenced to a certain extent.

In recent years, with the rapid development of social economy, the popularity and the per-capita utilization rate of mobile phones are greatly increased, and a large space and a new platform are provided for management and service of various industries. Compared with the traditional mass media, the intelligent terminal has the advantages of information dissemination, huge audience groups, access of WIFI networks and the like and general application of intelligent sensors of the mobile phone, so that the intelligent terminal mobile phone provides strong information processing capacity. An intelligent mobile phone is established to establish a follow-up system for a working platform, a nursing service mode is continued for a patient, community nurses evaluate problems in aspects of physiological, psychosocial, environmental and health related behaviors of the patient by sending and receiving information, and the nursing intervention effect is evaluated through mobile phone information. And performing classified and differentiated intervention on the patient to improve the operation efficiency of the follow-up system.

Meanwhile, with the development of economy, the medical guarantee idea is improved, medical care is moved to communities, the daily trend toward the public becomes a consensus, particularly after a hospitalized patient or a chronic patient returns home, on the basis of the existing information database management system, the establishment of the system is convenient for medical staff to judge whether the patient needs family visit, treatment or referral and the like, the information safety of the patient is further improved, the health service is better developed, accurate information acquisition and intelligent safety follow-up to the patient are realized, and the problem of successful urgent need to be solved is solved by improving the efficiency of follow-up visit. In view of the above, the present invention provides an intelligent follow-up system.

Disclosure of Invention

An intelligent follow-up system comprises a hospital third-party electronic medical record subsystem, an information data management platform, a user terminal, a hospital terminal and a detection terminal, wherein the user terminal, the detection terminal and the hospital terminal are respectively communicated with the information data management platform; wherein, the information data management platform is connected with the hospital third-party electronic medical record subsystem,

the third-party electronic medical record system is used for storing medical record data information of the patient;

the information data management platform is used for storing, managing and sending user information, the information management platform stores label data information of a user, and the user label data information is extracted according to data of a hospital third-party electronic medical record system and a user detection terminal;

the user terminal is used for logging in and information acquisition of a user and sending user information to the information data management platform;

the detection terminal is used for detecting the user sign data and sending the obtained user sign data to the information data management platform;

the hospital terminal is used for checking user information, user sign data, detection analysis results and classification results, performing classification extraction of the user information, performing classification follow-up visits according to user label data of the information data management platform, if the classification results are of a home follow-up visit type, the user is checked and treated by home, if the classification results are of a notification check type, the relevant follow-up visit data are sent to the user, and whether the user is notified to the hospital to perform check and treatment is judged based on the follow-up visit data;

the hospital terminal also comprises an encryption processing unit and a baseband data processing unit, wherein the encryption processing unit encrypts the follow-up information to ensure the privacy of the user, maps the acquired encryption information of the follow-up data information, the user terminal information and the service data packaging type information to the corresponding bit of the header and sends the header to the baseband data processing unit, and the baseband processing unit encodes and sends the header and the encrypted follow-up data information to be sent together.

The hospital terminal comprises a short message sending unit, wherein the short message sending unit is used for extracting basic information data of a user, selecting keywords to perform aggregation operation to obtain classified followed-up user data information, performing normal distribution sampling on the classified followed-up user data to obtain sampled followed-up user data information, and taking the followed-up user data information as a user for receiving the followed-up information.

The user terminal comprises an emotion sensing unit, and the emotion sensing unit senses the emotion of the user according to an emotion embedding algorithm of the user terminal.

The intelligent terminal emotion perception adopts a vector machine classification algorithm, and the current emotion of the user is classified by combining model parameters obtained by historical data training and real-time measurement data, so that emotion perception is realized.

The hospital terminal comprises a computer terminal, an intelligent small server and a virtual cloud server.

Further, the user terminal periodically feeds back emotion identification information to a short message sending unit in the hospital terminal, the short message sending platform selects the ID of the group member needing to send follow-up information communication according to the emotion identification information, and sends the encrypted follow-up information, the user name, the ID of the group member needing to send the information and the group emotion identification number to the user terminal connected with the communication network through the communication network; the hospital client acquires emotion identification information of the user terminal, and maps emotion identification information labels with the follow-up visit information data of the user so as to execute different follow-up visit data push.

The follow-up visit data information comprises data, video, images and voice, and the hospital terminal classifies the follow-up visit data information and encrypts the follow-up visit information data and sends the follow-up visit data information to the coding and decoding unit.

Optionally, the user side executes the follow-up data information question and answer form filling, and the user side is provided with a complete detection unit for detecting whether all the information on the follow-up form is completely recorded or not, and detecting whether all the information on the follow-up form is completely recorded or not one by one in a sequence opposite to the response. The detection terminal comprises an intelligent wristwatch, a sphygmomanometer, a glucometer, a thermometer and a human body fat scale.

Further, the user terminal is executed through an image acquisition unit and a biological signal acquisition unit, the emotion information of the user is identified, the user image acquisition unit is a camera, and the biological signal acquisition unit can be a touch pressure identification detector.

Optionally, the emotion recognition algorithm of the camera may be a table of correspondence between a combination of relative distances between muscles at preset positions and the plurality of preset emotions. The preset parts at least comprise a forehead part, an eye part, a mouth part and a cheekbone part, the relative distance between muscles of the preset parts is identified according to the shot three-dimensional image of each user face, and the forehead part, the eye part, the cheekbone part and the mouth part in the three-dimensional image of the user face are determined through feature identification; and respectively calculating the relative distance between the forehead and the eye, the relative distance between the eye and the cheekbone and the relative distance between the cheekbone and the mouth in the three-dimensional image.

Preferably, the emotion sensing unit obviously has different importance to expression classification according to feature information contained in each sub-block in the image, and a reasonable weight is given to each sub-block by adopting an adaptive weighting mechanism, that is, a neutral average face of all neutral expression images in a training sample is firstly solved, and a local texture feature matrix a ═ a of the neutral average face is extracted_1,…a_m]And respectively solving corresponding local texture feature matrixes for all the expressive imagesThen, the distance between chi-squared and chi-squared is used to calculate the LBP histogram of all the expressive images and the neutral average face image on a certain sub-block, i.e. the difference of a certain column of the corresponding local texture feature matrix, and finally the sum of the obtained differences is used as the adaptive weight of the sub-block, as shown below:

wherein j is 1, … m

And reducing the dimension of the sample image which is characterized in the form of a local texture feature matrix by using a 2DPLS method. When constructing the class membership matrix, the diagonal elements of the unit matrix in the middle of the construction class membership are replaced by the self-adaptive weight obtained by the formula so as to embody different importance of different sub-blocks to expression recognition.

Optionally, the SIM card or the hardware MAC address built in the client terminal determines the uniqueness of each terminal and binds with multiple pieces of user information in the home.

The communication module in the client terminal machine can adopt a GSM, a GPRS, a 3G, a 4G or a 5G and future more advanced communication modes, and the wireless communication module is connected with the internet or is connected with the internet through a wired communication module so as to carry out data transmission with the cloud server.

The information data management platform is used for uploading the special return visit data returned by the client and the data of the detection end, and uploading the special follow-up visit data to the data statistics module in the information data management platform, the data statistics module stores the special follow-up visit data into the database storage module of the information data management platform, and the patient review management module displays the follow-up visit conditions of the patient in the form of graphs and data characters, such as whether the follow-up visit is returned or not, whether the patient is abnormal or not, and the like.

The data management platform comprises a processor and a memory having stored thereon a computer program for execution by the processor to implement the method of the information data management platform.

A computer-readable storage medium having stored thereon a computer program for execution by a processor to perform method steps in a system.

According to the invention, by sampling extraction of follow-up user data and identification feedback processing of emotion identification, follow-up data information is transmitted based on the group identification ID and the emotion identification information, so that the efficiency and efficiency of follow-up data information transmission are improved, and meanwhile, normalization and classified group transmission of centralized sampling of follow-up user data are facilitated, thereby facilitating sampling statistics and follow-up for users of the same type. Particularly, in group application, a plurality of user account information are associated through a unified user terminal, a plurality of account numbers of a single device are used simultaneously, and by means of processing of encryption information and verification, safety of user information can be guaranteed and sending efficiency of follow-up data information can be improved. The method is particularly suitable for internal networks of large-scale national enterprises, factories and mines or discipline units.

Drawings

The features and advantages of the present invention will be understood more clearly by reference to the accompanying drawings, which are schematic and should not be construed as limiting the invention in any way.

FIG. 1 is a block diagram of the system of the present invention.

Detailed Description

These and other features and characteristics of the present invention, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will be better understood upon consideration of the following description and the accompanying drawings, which form a part of this specification. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. It will be understood that the figures are not drawn to scale. Various block diagrams are used in the present invention to illustrate various variations of embodiments according to the present invention.

Example 1

An intelligent follow-up visit system comprises a patient information management platform, a user terminal, a hospital terminal and a detection terminal, wherein the user terminal, the detection terminal and the hospital terminal are respectively communicated with an information database management system; wherein, the information data management system is connected with a third-party electronic medical record system of the hospital,

the user terminal is used for logging in and information acquisition of a user and sending user information to the information data management platform;

the detection terminal is used for detecting the user sign data and sending the obtained user sign data to the information data management platform;

the hospital terminal is used for checking user information, user sign data, detection analysis results and classification results, performing classification extraction of the user information, and performing classification follow-up visit according to user label data of the information data management platform;

the hospital terminal further comprises a short message sending unit, wherein the short message sending unit is used for extracting basic information data of a user, selecting keywords to perform aggregation operation to obtain classified follow-up user data information, performing normal distribution sampling on the classified follow-up user data, and taking the user data information as a user for receiving follow-up information;

the user terminal comprises an emotion sensing unit, the emotion sensing unit senses the emotion of the user according to an emotion embedding algorithm of the intelligent terminal, optionally, the emotion sensing of the intelligent terminal is performed by a vector machine classification algorithm, and the current emotion of the user is classified by combining model parameters obtained by historical data training and real-time measurement data, so that emotion sensing is achieved.

The hospital terminal also comprises an encryption processing unit and a baseband data processing unit, wherein the encryption processing unit carries out encryption processing on the follow-up visit information to ensure the privacy of users, maps the acquired encryption information of the follow-up visit data information, the terminal equipment information and the service data packaging type information to the corresponding bit of a header and sends the header to the baseband data processing unit, and the baseband processing unit codes and sends the header and the encrypted follow-up visit data information to be sent together;

the follow-up data information can further comprise classified information, such as data, video, image and voice, and the classified information is sent to the coding and decoding unit after the service of the follow-up data information is classified and the data of the follow-up information is encrypted;

the hospital terminal can be a computer terminal, an intelligent small server and a virtual cloud server.

The detection terminal comprises an intelligent wristwatch, a sphygmomanometer, a glucometer, a thermometer and a human body fat scale.

The user terminal comprises an emotion sensing unit, the emotion sensing unit senses the emotion of the user according to an emotion embedding algorithm of the intelligent terminal, optionally, the emotion sensing of the intelligent terminal is performed by a vector machine classification algorithm, and the current emotion of the user is classified by combining model parameters obtained by historical data training and real-time measurement data, so that emotion sensing is achieved.

The user terminal stores and analyzes information obtained by the frequency and the strength of the touch pressure sensor of the image acquisition unit, such as a camera and/or a biological signal acquisition unit touch screen, and judges the emotion of the old.

Optionally, the emotion recognition algorithm of the camera may be a table of correspondence between a combination of relative distances between muscles at preset positions and the plurality of preset emotions. The preset parts at least comprise a forehead part, an eye part, a mouth part and a cheekbone part, the relative distance between muscles of the preset parts is identified according to the shot three-dimensional image of each user face, and the forehead part, the eye part, the cheekbone part and the mouth part in the three-dimensional image of the user face are determined through feature identification; and respectively calculating the relative distance between the forehead and the eye, the relative distance between the eye and the cheekbone and the relative distance between the cheekbone and the mouth in the three-dimensional image.

The priority emotion processing unit obviously has different importance on expression classification according to the feature information contained in each sub-block in the image, and a self-adaptive weighting mechanism is adopted to endow each sub-block with reasonable weight, namely, neutral average faces of all neutral expression images in a training sample are firstly solved, and a local texture feature matrix A [ a ] of the neutral average faces is extracted_1,…a_m]And respectively solving corresponding local texture feature matrixes for all the expressive imagesThen, the chi-square distance is used to calculate the difference between the LBP histograms of all the expressive images and the neutral average face image on a certain sub-block, that is, a certain column of the corresponding local texture feature matrix, and finally, the sum of the obtained differences is used as the adaptive weight of the sub-block, as shown below:

wherein j is 1, … m

And reducing the dimension of the sample image which is characterized in the form of a local texture feature matrix by using a 2DPLS method. When constructing the class membership matrix, the diagonal elements of the unit matrix in the middle of the construction class membership are replaced by the self-adaptive weight obtained by the formula so as to embody different importance of different sub-blocks to expression recognition. Let X be an element of the sample data matrix X.

Optionally, the user terminal periodically feeds back to a short message sending unit of the hospital terminal to send emotion identification information, the short message sending platform selects an ID of a group member needing to send follow-up information communication from the management data platform according to emotion expression information, and sends the encrypted follow-up information, a user name, the ID of the group member needing to send information and a group emotion identification number to all other handheld wireless terminals connected with the communication network through the communication network; the group member ID information is keyword identification information in sampling after use.

The hospital terminal acquires emotion identification information of the user terminal, and maps emotion identification information labels with the follow-up visit data of the user so as to execute different follow-up visit data push.

Optionally, a user attention tag library is arranged on the server side in the management data platform system, and after receiving an emotion tag reported by a client (i.e., a user terminal), the emotion tag is optionally re-labeled as an emotion identifier, and a user account, i.e., a user name, logged in by the client and the emotion tag thereof are correspondingly stored in the library, so that the server can perform push operation according to the user attention tag library and issue corresponding follow-up information data to a medical terminal. For example, if the client 1 reports the follow-up information data of the emotion information tag 1, the user account 1 logged in by the client 1 is correspondingly stored with the emotion information tag 1; and the client 2 reports the follow-up information data of the label 2, and the user account 1 logged in by the client 2 is correspondingly stored with the emotion label 2.

And pushing follow-up information data associated with the label corresponding to the user account to the client logged in by the corresponding user account according to the corresponding relation in the user concern label library.

In the intelligent follow-up practice, the acceptance degrees of different problems are different under different emotional states, particularly the emotion fluctuation of chronic patients is large, and the execution of emotion follow-up information test data is easy to cause misjudgment when the emotion is depressed, so that the follow-up data information can be adjusted according to the emotion recognition detection of the user terminal during the follow-up data. Meanwhile, the follow-up visit data is encrypted to ensure personal privacy.

In reality, the follow-up data needs to be classified and analyzed, and the application of data difference of individuals is basically performed through group data communication. The intelligent follow-up system can accurately push, further, a user corresponding to user account information is combined with third-party medical record data information, user portrait processing is executed, if the user portrait corresponding to the current user is the user portrait D, the push strategy corresponding to the current user can be determined to be a push strategy D according to a preset effect mapping table, and an estimated effect value corresponding to the current user and the push strategy D is determined to be 70 points. The push policy D may specifically include: the message pushing time is 10: 00 to 12: 00, the pushing mode is banner message.

The push strategy comprises message push time and a message push mode. Push means include, but are not limited to, pop-up window messages, banner messages, short message messages, and the like. The preset effect mapping table is used for storing one or more effect mapping relations, wherein the effect mapping relations are used for determining the mapping relations between the user portrait and the push strategy and between the user portrait and the pre-estimated effect value.

Each user portrait corresponds to a unique portrait identifier, and a pushing strategy and an estimated effect value corresponding to the user portrait can be determined through the portrait identifier and an effect mapping relation.

In particular, to improve the utilization efficiency of the device, the user terminal may be a handheld wireless terminal, and in a large-scale team application, such as an army, the wireless terminal may be a common user terminal, that is, the user terminal may be used by a plurality of user accounts. Therefore, optionally, in order to ensure information privacy and security, the user name and the ID information of the group members are verified, after the user terminal, that is, the wireless handheld terminal receives the message, the user name, the ID of the group member needing to send the message and the group emotion identification number, the judgment is carried out according to the received user name, if the ID of the group member needing to send the message contains the ID of the group member which is consistent with the registered user name in the handheld wireless terminal, the received message is decrypted by adopting a security decryption module, and the decryption mode is to decrypt by using a secret key and carry out data verification; judging whether the check result is correct or not, if so, processing the request of the wireless handheld terminal and returning the result to the wireless handheld terminal; if not, the wireless handheld terminal gives an alarm, returns an error prompt to the wireless handheld terminal, and then starts a message frame for displaying messages to display the decrypted messages.

If the ID of the group member which is consistent with the user name (namely the user account) registered in the handheld wireless terminal does not exist in the ID of the group member which needs to send the message, the received message is stored until a new user registers in the handheld wireless terminal, and the ID of the group member which is consistent with the new user name registered in the handheld wireless terminal exists in the ID of the group member which receives the message, a message frame for displaying the message is started to display the received message. Optionally, the user name and the ID of the group member that needs to send the message are combined, and the interval duration from sending the message to receiving the message is sent to a server of the data management service platform for storage, so that when the follow-up data is sent, different follow-up group interval sending messages are set, and are fed back to the hospital terminal to adjust and sample the followed-up user, and the statistical processing of the subsequent data is improved.

Optionally, the SIM card or the hardware MAC address built in the client terminal determines the uniqueness of each terminal and binds with multiple pieces of user information in the home.

The communication module in the client terminal machine can adopt a GSM, a GPRS, a 3G, a 4G or a 5G and future more advanced communication modes, and the wireless communication module is connected with the internet or is connected with the internet through a wired communication module so as to carry out data transmission with the cloud server.

The information data management platform is used for uploading the special return visit data returned by the client and the data of the detection end, and uploading the special follow-up visit data to the data statistics module in the information data management platform, the data statistics module stores the special follow-up visit data into the database storage module of the information data management platform, and the patient review management module displays the follow-up visit conditions of the patient in the form of graphs and data characters, such as whether the follow-up visit is returned or not, whether the patient is abnormal or not, and the like.

The optional follow-up information is: the patient self-evaluation information is periodically transmitted to the patient and the patient self-evaluation is requested. The scores were scored according to patient fill and selection, ranging from 1-5 points.

For example: evaluation of psychological field:

knowledge: positive mental functions and relaxing knowledge and skill scores.

1 minute: not at all known; and 2, dividing: a little knowledge; and 3, dividing: has basic knowledge; and 4, dividing: the knowledge is available; and 5, dividing: has rich knowledge.

Behavior: diet, sleep, scores after expression evaluation.

1 minute: the behavior is very inappropriate; and 2, dividing: most of the behavior is inappropriate; and 3, dividing: behavior is sometimes inappropriate; and 4, dividing: most of the behavior is correct; and 5, dividing: the behavior is always correct.

The state is as follows: subjective and objective patient status, such as heart rate, fidgetiness, anxiety, pain scores.

1 minute: the symptoms are severe; and 2, dividing: severe symptoms; and 3, dividing: the symptoms are generally severe; and 4, dividing: there is a little symptom; and 5, dividing: there were no symptoms.

Optionally, the user side executes the follow-up data question and answer form filling, and the user side device is provided with a complete detection unit, and is configured to detect whether all information on the follow-up form has been recorded in a sequence opposite to the response one by one when all information on the follow-up form has been recorded in the complete detection unit;

the information input on the follow-up form can be firstly realized one by one through voice interaction between an optional user and a user side, and because the information input in the later sequence is more difficult to complete than the information input in the former sequence in the preset time in the complete process of the information input under the general condition, the information input in the later sequence can be preferentially detected during detection, and once the information input is found to be incomplete, the detection result can be judged that all the information on the follow-up form is not completely input.

Example 2

A speech recognition apparatus comprising a processor and a memory, the memory having stored thereon a computer program for execution by the processor to perform the method of embodiment 1.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.

As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).

It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.