A method for establishing a medical education platform based on Internet technology

Abstract

The present invention discloses a method for establishing a medical education platform based on Internet technology, including A001: obtaining basic information of a medical user terminal, wherein the basic information of the medical user terminal includes service content information, and obtaining a user category of the medical user terminal; A002: obtaining a doctor classification database and obtaining a medical education platform audit condition; A003: obtaining platform doctor user audit data, wherein the platform doctor user audit data is used as a doctor terminal classification feature; A004: obtaining service information adaptability; A005: obtaining a service information image summary, judging whether the service information image summary meets the medical education platform audit condition, and obtaining preliminary audit data; A006: establishing a user review decision tree, and obtaining a medical user terminal audit result; A007: obtaining preliminary construction data, wherein the preliminary construction data is used to establish a first online consultation area. The technical solution of this application is more timely through the Internet and artificial intelligence technology, and meets the privacy needs of patient users.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to a method for establishing a medical education platform based on Internet technology. Background Art

[0002] With the rapid development of Internet technology, there are more and more online consultation and online diagnosis and treatment functions, and more and more platforms for online consultation and online diagnosis and treatment. When people encounter physical or mental discomfort in their lives and work, they will first search for relevant knowledge on the Internet, take necessary precautions, or learn relevant common sense.

[0003] After the Internet medical platform is established, doctors and consultants will move in temporarily, so it needs to be reviewed, built, and maintained. However, the current medical platform has relatively poor results for offline consultations or offline education on infectious diseases, mainly because consultants have a strong demand for privacy. Currently, the maintenance and construction of this platform is mainly done manually, with a slow response speed and limited coverage. Summary of the invention

[0004] The present invention provides a method for establishing a medical education platform based on Internet technology, which adopts the Internet and artificial intelligence technology to construct, review and maintain the platform, and fully constructs a model to ensure a stable and smooth operating environment of the medical platform, thereby solving the technical problems in the prior art of low efficiency of manual processing in the maintenance and review of the medical platform.

[0005] The present invention is achieved through the following technical solutions:

[0006] A method for establishing a medical education platform based on Internet technology, the method comprising:

[0007] A001: Obtain basic information of a medical user terminal, wherein the basic information of the medical user terminal includes service content information, and obtain a user category of the medical user terminal according to the basic information of the medical user terminal and the service content information;

[0008] A002: obtaining a doctor classification database according to the user category of the medical user terminal, and obtaining the review conditions of the medical education platform according to the user category of the medical user terminal;

[0009] A003: According to the doctor classification database and the medical education platform review conditions, the platform doctor user review data is obtained, and the platform doctor user review data is used as the doctor-side classification feature;

[0010] A004: Obtaining service information adaptability according to the doctor classification data information and doctor classification service data information in the doctor classification database, wherein the service information adaptability is used as a doctor user level characteristic;

[0011] A005: Obtain a service information image summary based on the doctor classification service data information, determine whether the service information image summary meets the review conditions of the medical education platform, and obtain preliminary review data, which is the doctor user review classification characteristics;

[0012] A006: Establish a user review decision tree based on the doctor-side classification characteristics, the doctor user level characteristics and the doctor user review classification characteristics, input the medical user-side basic information into the user review decision tree, and obtain the medical user-side review result;

[0013] A007: If the medical user terminal audit result meets the preliminary audit requirements, preliminary construction data is obtained, and the preliminary construction data is used to establish a first online consultation area. The first online consultation area, the medical user terminal basic information and the service content information correspond to each other.

[0014] In the step A003, information theory coding operation is performed on the classification features of the doctor side to obtain the information entropy of the doctor side features;

[0015] In the step A004, information theory coding operation is performed on the doctor user level characteristics to obtain the doctor end user characteristic information entropy;

[0016] In the step A005, information theory coding operation is performed on the doctor user review and classification features to obtain the doctor user review feature information entropy;

[0017] The doctor-side feature information entropy, the doctor-side user feature information entropy and the doctor user review feature information entropy are input into a comparison information model for experimental simulation to obtain preliminary node feature data, and a doctor user review tree is established based on the preliminary node feature data and the doctor classification database.

[0018] Optionally, in step A007, the first online consultation area includes a common FAQ consultation area, an expert care consultation system area, and a surgical Q&A consultation area.

[0019] Optionally, after obtaining the preliminary construction data, step A007 further includes:

[0020] A0071: obtaining medical user terminal record information based on the basic information of the medical user terminal, obtaining medical user terminal related users based on the medical user terminal record information; obtaining medical user terminal related user information based on the basic information of the medical user terminal, obtaining medical user terminal related user information summary based on the medical user terminal related user information;

[0021] A0072: Obtaining a predetermined related information database, inputting the medical user terminal record information and the predetermined related information database into an analysis test model to obtain preliminary analysis information;

[0022] A0073: if the preliminary analysis information meets the preliminary set conditions, obtaining the user data of the medical user terminal according to the user related to the medical user terminal, and obtaining the medical user terminal related information according to the user data of the medical user terminal and the user information related to the medical user terminal;

[0023] A0074: Determine whether the medical user terminal associated information is associated with the medical user terminal record information. If so, obtain associated prompt information.

[0024] Optionally, in step A0072, the medical user terminal record information and the predetermined related information database are input into an analysis test model to obtain preliminary analysis information, specifically including:

[0025] The medical user terminal record information and the predetermined related information database are input into the analysis test model, and the analysis test model is obtained by training multiple sets of training data until convergence, wherein each set of data in the multiple sets of training data has the medical user terminal record information, the predetermined related information database and identification features for confirming preliminary analysis information.

[0026] Optionally, the step A0072 further includes:

[0027] A generation result of the analysis test model is obtained, wherein the generation result includes the preliminary analysis information.

[0028] Optionally, in step A0073, obtaining medical user terminal associated information according to the medical user terminal user data and the medical user terminal associated user information aggregation further includes:

[0029] A00731: obtaining the time information of the medical user terminal according to the record information of the medical user terminal, and obtaining the user storage database according to the user related to the medical user terminal and the time information of the medical user terminal;

[0030] A00732: Summarize user information associated with the medical user terminal to obtain user-related features, perform a feature comparison on the user storage database based on the user-related features to obtain comparison information, and obtain the medical user terminal-related information based on the comparison information.

[0031] Optionally, in step A0074, determining whether the medical user terminal associated information is associated with the medical user terminal record information includes:

[0032] A00741: obtaining recorded transaction data according to the medical user terminal recorded information, obtaining transaction time data according to the recorded transaction data, and obtaining associated recorded transaction information according to the medical user terminal associated information;

[0033] A00742: The associated recorded transaction information has associated transaction time information. A time association feature is obtained based on the associated transaction time information and the transaction time data, and a judgment is made based on the time association feature and the association.

[0034] Optionally, in step A007, after obtaining the preliminary construction data, the following steps are included:

[0035] Obtaining a basic information base of the education platform, obtaining an adaptation basic information type based on the service content information and the education platform basic information base, and obtaining matching basic feature information; obtaining medical user historical viewing data through big data based on the medical user terminal basic information, inputting the service content information and the adaptation basic information type into the extraction information prototype, and obtaining basic feature information;

[0036] Using the basic feature information, perform a feature comparison on the historical viewing data of the medical user to obtain a basic information comparison result, and obtain basic recommendation information according to the basic information comparison result and the matching basic feature information;

[0037] According to the basic recommendation information, selection information is obtained, and according to the selection information and the service content information, information for establishing a publicity and education platform is obtained.

[0038] Optionally, the education platform establishment information is to establish the platform according to the selection information and the service content information.

[0039] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0040] The technical solution of this application uses the Internet and artificial intelligence technology to build a medical education platform, and builds, reviews, and maintains the medical education platform. Compared with the existing technology of manually reviewing the existing medical education platform, it is more timely and efficient. At the same time, the construction of the medical platform can help protect the privacy needs of patient users. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic block diagram of the process structure of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments. The illustrative embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention. Example

[0043] like Figure 1 As shown, a method for establishing a medical education platform based on Internet technology includes the following steps:

[0044] A001: Obtain basic information of a medical user terminal, wherein the basic information of the medical user terminal includes service content information, and obtain a user category of the medical user terminal according to the basic information of the medical user terminal and the service content information;

[0045] Specifically, the medical user terminal is a user who is going to be established on the medical education platform. The basic information of the medical user terminal includes the identification data, qualification data, and service information that can be provided by the medical user terminal, such as a specific department of a hospital, level, main treatment direction, etc. The medical user terminal can be an individual, such as a doctor, or a hospital, or multiple hospitals jointly establish a medical education platform. If it is an individual, personal identity information and relevant professional qualifications need to be provided; if it is a hospital, a business license, a doctor's professional qualifications, etc. need to be provided to ensure that it is an individual or hospital with the qualifications to provide relevant online consultation and diagnosis and treatment. The service content information is mainly for commercial transactions or public welfare services on the medical education platform, including consultation on some common diseases and infectious diseases, as well as questions and answers for surgical patients before and after surgery, etc.

[0046] When establishing a medical education platform online, in addition to introducing the relevant themes of the medical education platform in the service content information, it is also necessary to provide some text introduction, including some images, such as professional photos of doctors, photos of hospitals, etc.

[0047] The judgment is made based on the specific service content that can be provided in the service content information and the relevant data information of the medical user terminal in the basic information of the medical user terminal. The specific type of content can be adjusted according to the content and conditions of the platform applied for, such as the common Good Doctor Online, Easy Treatment Service Platform, etc. There are many similar platforms, which can be specified according to actual needs and directions. Comprehensive directions can also be made, such as comprehensive hospital directions, which have all departments, or special specialized online consultation platforms, such as only infectious diseases, etc. The service content provided by different types of users has a certain commonality, and also has certain specific direction advantages. Therefore, the user category of the medical user terminal is judged by the basic information of the medical user terminal and the service content information, and after classification processing, it is more accurate.

[0048] A002: obtaining a doctor classification database according to the user category of the medical user terminal, and obtaining the review conditions of the medical education platform according to the user category of the medical user terminal;

[0049] Specifically, according to the determined user category of the medical user end, the same type of data information is retrieved for the medical education platform to be established. The doctor classification database is the user information similar to the user category of the medical user end in all relevant platforms at present. The doctor classification database corresponds to the content in the user category of the medical user end, including the user's basic information, qualifications, service content information, etc. According to the requirements of the platform or the different audit conditions, the information categories included in the doctor classification data will also be different. According to the type of the user category of the medical user end, the audit requirements of the medical education platform are judged. The audit conditions of the medical education platform are the audit conditions corresponding to the corresponding medical user end user category, such as qualification requirements, requirements for the main service direction provided by the doctor or hospital, and the number of consultations, successful or typical cases, etc. Although the platforms belong to the large online consultation medical direction, the specific focus will be different, so it can be pre-set according to the conditions of the medical education platform. For example, when entering the doctor's qualification information, there can be medical assistants, physicians, attending physicians, deputy chief physicians, chief physicians, etc. according to the doctor's level.

[0050] A003: According to the doctor classification database and the medical education platform review conditions, the platform doctor user review data is obtained, and the platform doctor user review data is used as the doctor-side classification feature;

[0051] A004: Obtaining service information adaptability according to the doctor classification data information and doctor classification service data information in the doctor classification database, wherein the service information adaptability is used as a doctor user level characteristic;

[0052] A005: Obtain a service information image summary based on the doctor classification service data information, determine whether the service information image summary meets the review conditions of the medical education platform, and obtain preliminary review data, which is the doctor user review classification characteristics;

[0053] A006: Establish a user review decision tree based on the doctor-side classification characteristics, the doctor user level characteristics and the doctor user review classification characteristics, input the medical user-side basic information into the user review decision tree, and obtain the medical user-side review result;

[0054] Specifically, there are multiple categories, multiple levels, and multiple steps in the review of users of the medical education platform. In this technical solution, advanced technologies such as artificial intelligence are used to make full use of decision trees to carry out multi-level screening and review. Decision trees are a machine learning method that is quite common in the current field of artificial intelligence. Decision trees are in a supervised learning state in artificial intelligence, and they represent the mapping relationship between object attributes and object values.

[0055] The decision tree algorithm uses a tree structure to establish a decision model based on the attributes of the data. Each node in the tree represents an object, and a forked path represents an attribute value with a certain probability, and each leaf node corresponds to the value of the object represented by the path from the root node to the leaf node. The decision tree includes a root node, several internal nodes and several leaf nodes, so the decision tree is equivalent to a multi-branch tree. The leaf node corresponds to a category, and the internal node corresponds to an attribute test, or a feature. The sample set contained in each node is divided into child nodes according to the result of the attribute test. Decision trees are generally generated from top to bottom, and the generation of decision trees is a recursive process. Each decision or event may lead to two or more events, resulting in different results.

[0056] The qualifications and information of medical users are used as classification features of the doctor side. First, the qualifications of medical users are reviewed. If the qualifications meet the requirements, the services provided are reviewed. If the services meet the requirements, the specific content of the services is reviewed, such as whether the content information and image information do not meet the requirements, such as unhealthy, exaggerated propaganda, threats, malicious competition, etc. The review is carried out level by level and type by type, and a decision tree is constructed. The same type of data is subjected to machine learning and supervised learning. The user review decision tree obtained is more in line with the review conditions of the platform and type of users, thereby improving and meeting the review conditions. At the same time, the use of artificial intelligence technology improves the efficiency and accuracy of the review compared to manual work.

[0057] Decision tree training includes feature selection, decision tree acquisition and pruning. The learning algorithm of decision trees generally recursively selects the best features and uses the best features to segment the data set. At the beginning, build the root node, select the best feature, and use the best feature to segment the data set. At the beginning, build the root node, select the best feature, and split the data set into multiple subsets if there are several values ​​of the feature. Each subset recursively calls this method and returns the node. The returned node is the child node of the previous layer. Until all features have been used up, or the data set has only one-dimensional features. In addition, the random forest classifier combines many decision trees to improve the accuracy of classification. If there are other audit requirements of the platform, you can continue to classify, expand and optimize the decision tree through relevant data and requirements to ensure the performance advantage of the decision tree.

[0058] A007: If the medical user terminal audit result meets the preliminary audit requirements, preliminary construction data is obtained, and the preliminary construction data is used to establish a first online consultation area. The first online consultation area, the medical user terminal basic information and the service content information correspond to each other.

[0059] Specifically, the information of the medical user terminal is input into a user review decision tree established with data of the same type as the user terminal, and the corresponding result information is obtained. When the review result of the medical user terminal meets the conditions, it is approved to establish medical education and consultation in the corresponding platform. If the conditions are not met, it is rejected. The preliminary review requirements, that is, when the review result of the medical user terminal meets or satisfies the requirements, the medical user terminal platform is established. It can be established by the platform itself, or the medical education platform can be used to construct space and requirements. The technical solution embodiment of the present application can also provide automatic construction, so that the intelligence and efficiency of the platform review can be provided through artificial intelligence technology. It is achieved to review, build and maintain the medical education platform by building a model, ensure the stability of the medical platform environment, and improve the efficiency of platform construction, so as to realize the review of platform users from multiple angles, and have a technical effect of a wide range of review. Thereby solving the technical problems in the prior art that the construction of the medical platform and the maintenance of the platform environment mainly rely on manual work, and there are insufficient timeliness and limited coverage.

[0060] In this embodiment, further, in step A003, information theory coding operation is performed on the doctor-side classification features to obtain doctor-side feature information entropy;

[0061] In the step A004, information theory coding operation is performed on the doctor user level characteristics to obtain the doctor end user characteristic information entropy;

[0062] In the step A005, information theory coding operation is performed on the doctor user review and classification features to obtain the doctor user review feature information entropy;

[0063] The doctor-side feature information entropy, the doctor-side user feature information entropy and the doctor user review feature information entropy are input into a comparison information model for experimental simulation to obtain preliminary node feature data, and a doctor user review tree is established based on the preliminary node feature data and the doctor classification database.

[0064] Specifically, in order to construct a user review decision tree, the information entropy value can be calculated for the doctor-side classification features, the doctor user level characteristics, and the doctor user review classification features, that is, the Shannon formula is used. The Shannon formula in information theory coding is used to specifically calculate the information entropy value, so as to obtain the corresponding doctor-side feature information entropy, the doctor-side user feature information entropy, and the doctor user review feature information entropy. Since the information entropy represents the uncertainty of information, the larger the uncertainty value, the larger the amount of information it contains, the higher the corresponding information entropy, and the lower the purity. When all samples in the set are mixed evenly, the information entropy is the largest and the purity is the lowest. Therefore, based on the comparative information model, when the doctor-side feature information entropy, the doctor user review feature information entropy, and the doctor user review feature information entropy are compared, the feature with the smallest entropy value is obtained, that is, the preliminary node feature data. By prioritizing the feature with the minimum entropy value, and then classifying each feature in order from small to large entropy values, a doctor user review tree is constructed, so that each doctor user information or hospital user information can be efficiently reviewed, thereby achieving the specific construction of the doctor user review tree.

[0065] Among them, the comparative information model is a neural network model, which is a neural network model in machine learning. It can be continuously learned and adjusted, and can also be understood as a mathematical model. It is an information comparison model that can be simulated using a large amount of training data. In this embodiment, the main function of the comparative information model is to compare the size of the doctor-side feature information entropy, the doctor user review feature information entropy, and the doctor user review feature information entropy, and then sort them according to the size of the entropy value, and then combine other features to jointly construct a doctor user review tree, so as to facilitate the review of the establishment of platform information. Compared with manual review, the review method through model establishment is more timely.

[0066] Furthermore, in step A007, the first online consultation area includes a common and frequently asked questions consultation area, an expert care consultation system area, and a surgical question and answer consultation area.

[0067] The common common questions consultation area refers to the online consultation between patient users and hospital doctors on common problems in life, such as respiratory tract infections, colitis, chronic gastritis, pneumonia and other diseases, as well as precautions in life, preventive measures, etc. The expert care consultation system area refers to infectious disease experts such as psychology, hepatology, and infection experts jointly developing a set of targeted care and counseling courses for patients with these diseases, so that patients with these diseases can have a better understanding of these diseases and reduce their sense of shame for infectious diseases. The surgical question and answer consultation area refers to the consultation and question-answering area for surgical patients with infectious diseases or other diseases, which can provide targeted answers to patients' preoperative and postoperative review, life, and family prevention issues.

[0068] The establishment of the first online consultation area is mainly based on the opinions of infectious disease patients and the starting point of protecting patient privacy, as well as some patients who are not infectious disease patients but whose privacy is involved. Therefore, the corresponding online consultation area can be a voice communication platform, which is more private and can communicate directly with experts with high communication efficiency.

[0069] Furthermore, the step A007, after obtaining the preliminary construction data, further includes:

[0070] A0071: obtaining medical user terminal record information based on the basic information of the medical user terminal, obtaining medical user terminal related users based on the medical user terminal record information; obtaining medical user terminal related user information based on the basic information of the medical user terminal, obtaining medical user terminal related user information summary based on the medical user terminal related user information;

[0071] A0072: Obtaining a predetermined related information database, inputting the medical user terminal record information and the predetermined related information database into an analysis test model to obtain preliminary analysis information;

[0072] A0073: if the preliminary analysis information meets the preliminary set conditions, obtaining the user data of the medical user terminal according to the user related to the medical user terminal, and obtaining the medical user terminal related information according to the user data of the medical user terminal and the user information related to the medical user terminal;

[0073] A0074: Determine whether the medical user terminal associated information is associated with the medical user terminal record information. If so, obtain associated prompt information.

[0074] Specifically, for the user usage of the medical education platform, such as consultation, transaction, and communication, the embodiment of the present application uses artificial intelligence technology to monitor, and to provide prompts and audits when necessary to maintain the stability of the medical platform transaction environment. The medical user terminal record information is the transaction record of the medical user terminal in the medical education platform and the communication information corresponding to the record. By reviewing the content of the medical user terminal record information, it is possible to avoid or eliminate the situation where platform users have false transactions. It is mainly carried out through a third-party platform, which is a platform with payment function. However, in transactions on third-party platforms, corresponding chat records are generally left in the medical education platform. Therefore, based on the information provided in the medical user-side record information of the relevant user of the medical user side, such as the doctor's ID card, mobile phone number, name and other related information, the corresponding social account can be obtained, and the correlation analysis is obtained from the corresponding records in the social account. The correlation analysis mainly includes the content of communication, the object of communication, the transaction amount of communication and the time information. These parts are analyzed, and the medical user-side related user information summary is set based on the current chat content information and the basic information of the medical user side. For example, based on the added account, mobile phone number, QQ number and other information in the medical user side record information, it is compared with the corresponding type of information of the communication party in the user consultation information related to the medical user side.

[0075] If there is relevant information about the need to add new communication software in the basic information of the medical user end. Then a predetermined relevant information database is constructed separately, and the predetermined relevant information database is extracted through big data or the chat communication information of the medical user end, or the user-related basic information, such as the pinyin, English abbreviation, abbreviation, etc. of the communication software name, key information of the same type, etc. If the words of the predetermined relevant information database appear in the record information of the medical user end, it can be preliminarily estimated that there is a certain possibility of suspicion, and it is necessary to further record, analyze and diagnose the relevant communication or social accounts of the relevant users of the medical user end. If there is a correlation between the transfer information and the first transaction information in the medical user end related information, a prompt is given. That is, there is suspicion in the record information of the medical user end, which needs further verification. By tracking and analyzing the transaction content related to the medical user end, illegal transaction operations are avoided to maintain the order of the medical education platform and the stability of the environment, and the neural network model is used to improve the efficiency of data processing, wherein the analysis test model is a neural network model, which further solves the defects of the construction of the medical education platform and the maintenance of the platform environment in the prior art mainly relying on manual work, and there are insufficient timeliness and limited coverage.

[0076] Optionally, in step A0072, the medical user terminal record information and the predetermined related information database are input into an analysis test model to obtain preliminary analysis information, specifically including:

[0077] The medical user terminal record information and the predetermined related information database are input into the analysis test model, and the analysis test model is obtained by training multiple sets of training data until convergence, wherein each set of data in the multiple sets of training data has the medical user terminal record information, the predetermined related information database and identification features for confirming preliminary analysis information.

[0078] Specifically, the analytical test model is a neural network model in machine learning. The neural network model uses continuous learning and adjustment and is a highly complex nonlinear dynamic learning system. Specifically, the analytical test model is a mathematical model. Through the learning of a large amount of training data, the medical user-side record information and the predetermined related information database are input into the neural network model, and preliminary analysis information is output. Furthermore, the training process is essentially a supervised learning process, and each group of learning supervision information includes the medical user-side record information, the predetermined related information database, and the record information used to mark the preliminary analysis information. The neural network model is continuously adjusted and corrected until the output result obtained is consistent with the marking information, then the supervised learning of this group of data is ended, and then the next round of supervised training is started. When the output information of the neural network model reaches the predetermined convergence situation, the supervised learning process ends.

[0079] Through supervised training of the neural network model, the neural network model can process the input information more accurately, thereby obtaining more accurate and appropriate preliminary analysis information, and further analyzing the transaction content within the platform, and matching the preliminary judgment of suspicious records to relevant users for further confirmation analysis.

[0080] By conducting correlation analysis on the transaction volume and time of the third-party trading platform, the orderliness and stability of the platform environment can be confirmed. At the same time, the addition of a neural network model improves the efficiency and accuracy of data processing results, laying the foundation for providing a more accurate medical consultation and treatment environment.

[0081] Optionally, in step A0073, obtaining medical user terminal associated information according to the medical user terminal user data and the medical user terminal associated user information aggregation further includes:

[0082] A00731: obtaining the time information of the medical user terminal according to the record information of the medical user terminal, and obtaining the user storage database according to the user related to the medical user terminal and the time information of the medical user terminal;

[0083] A00732: Summarize user information associated with the medical user terminal to obtain user-related features, perform a feature comparison on the user storage database based on the user-related features to obtain comparison information, and obtain the medical user terminal-related information based on the comparison information.

[0084] Specifically, the determination of the medical user-side associated information is mainly carried out by comparing the time information of the medical user-side recorded information to confirm the time information. For example: one day or one week before and after the corresponding time of the medical user-side recorded information, the records of the medical user-side user data within a set period of time are analyzed, and the information or data or keywords appearing in the user-related data summary are confirmed as user-related features. The user-related features are used to perform feature comparison on all the user-related features, and the information that meets the conditions is extracted, and the record information that meets the conditions most closely and has the largest number is obtained as the medical user-side associated information.

[0085] Optionally, in step A0074, determining whether the medical user terminal associated information is associated with the medical user terminal record information includes:

[0086] A00741: obtaining recorded transaction data according to the medical user terminal recorded information, obtaining transaction time data according to the recorded transaction data, and obtaining associated recorded transaction information according to the medical user terminal associated information;

[0087] A00742: The associated recorded transaction information has associated transaction time information. A time association feature is obtained based on the associated transaction time information and the transaction time data, and a judgment is made based on the time association feature and the association.

[0088] Specifically, when analyzing and comparing the medical user terminal record information and the medical user terminal associated information, in addition to analyzing according to the unusual information appearing in the medical user terminal record information, because third-party transactions are conducted through transfers, the medical user terminal user needs to protect his own interests, and thus compares the time point of the money transaction with the time point of the transaction in the medical user terminal record information through the transfer-related information in the medical user terminal associated information, such as time and amount, to confirm whether there is a correlation. If there is a cashback after the transaction is evaluated, and during the transaction, the time point of the medical user terminal record information and the medical user terminal associated information, as well as the predetermined related information database, is compared, it is confirmed that the adaptability is strong, and after comprehensive analysis, it is concluded that the correlation is met, then it is determined that there is a question about the transaction and a necessary prompt is issued.

[0089] Optionally, in step A007, after obtaining the preliminary construction data, the following steps are included:

[0090] Obtaining a basic information base of the education platform, obtaining an adaptation basic information type based on the service content information and the education platform basic information base, and obtaining matching basic feature information; obtaining medical user historical viewing data through big data based on the medical user terminal basic information, inputting the service content information and the adaptation basic information type into the extraction information prototype, and obtaining basic feature information;

[0091] Using the basic feature information, perform a feature comparison on the historical viewing data of the medical user to obtain a basic information comparison result, and obtain basic recommendation information according to the basic information comparison result and the matching basic feature information;

[0092] According to the basic recommendation information, selection information is obtained, and according to the selection information and the service content information, information for establishing a publicity and education platform is obtained.

[0093] Optionally, the education platform establishment information is to establish the platform according to the selection information and the service content information.

[0094] Specifically, after reviewing the information of the basic information construction platform of the medical user terminal, the service of automatically building the platform is provided to the user, and the service content information is adapted to the information provided by the platform to obtain information suitable for the service content. The user can make a choice according to the corresponding content. At the same time, the platform uses big data to analyze the personal preferences of the medical user terminal, extract the elements that are related to the personal preferences and the materials provided by the platform, and combine them with the materials in the platform to make recommendations for the medical user terminal. When the medical user terminal selects the reference material, a preliminary template is established in combination with the corresponding platform. The system will start the corresponding platform construction with the material, service content information, medical user terminal information, and display the service content and related information. The corresponding display interface and display information are selected for display location, and uploaded to achieve the automatic construction of the medical education platform and reduce manual review.

[0095] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for establishing a medical education platform based on Internet technology, characterized in that: The method comprises: A001: Obtain basic information of a medical user terminal, wherein the basic information of the medical user terminal includes service content information, and obtain a user category of the medical user terminal according to the basic information of the medical user terminal; A002: obtaining a doctor classification database according to the user category of the medical user terminal, and obtaining the review conditions of the medical education platform according to the user category of the medical user terminal; A003: According to the doctor classification database and the medical education platform review conditions, the platform doctor user review data is obtained, and the platform doctor user review data is used as the doctor-side classification feature; A004: Obtaining service information adaptability according to the doctor classification data information and doctor classification service data information in the doctor classification database, wherein the service information adaptability is used as a doctor user level characteristic; A005: Obtain a service information image summary based on the doctor classification service data information, determine whether the service information image summary meets the review conditions of the medical education platform, and obtain preliminary review data, which is the doctor user review classification characteristics; A006: Establish a user review decision tree based on the doctor-side classification characteristics, the doctor user level characteristics and the doctor user review classification characteristics, input the medical user-side basic information into the user review decision tree, and obtain the medical user-side review result; A007: If the medical user terminal review result meets the preliminary review requirements, obtain preliminary construction data, the preliminary construction data is used to establish a first online consultation area, the first online consultation area, the medical user terminal basic information and the service content information correspond; In the step A003, information theory coding operation is performed on the classification features of the doctor side to obtain the information entropy of the doctor side features; In the step A004, information theory coding operation is performed on the doctor user level characteristics to obtain the doctor end user characteristic information entropy; In the step A005, information theory coding operation is performed on the doctor user review and classification features to obtain the doctor user review feature information entropy; Input the doctor-side feature information entropy, the doctor-side user feature information entropy and the doctor-user review feature information entropy into a comparison information model for experimental simulation to obtain preliminary node feature data, and establish a doctor-user review tree based on the preliminary node feature data and the doctor classification database; The step A007, after obtaining the preliminary construction data, further includes: A0071: obtaining medical user terminal record information based on the basic information of the medical user terminal, obtaining medical user terminal related users based on the medical user terminal record information; obtaining medical user terminal related user information based on the basic information of the medical user terminal, obtaining medical user terminal related user information summary based on the medical user terminal related user information; A0072: Obtaining a predetermined related information database, inputting the medical user terminal record information and the predetermined related information database into an analysis test model to obtain preliminary analysis information; A0073: if the preliminary analysis information meets the preliminary set conditions, obtaining the user data of the medical user terminal according to the user related to the medical user terminal, and obtaining the medical user terminal related information according to the user data of the medical user terminal and the user information related to the medical user terminal; A0074: Determine whether the medical user terminal associated information is associated with the medical user terminal record information. If so, obtain associated prompt information.

2. The method for establishing a medical education platform based on Internet technology according to claim 1, characterized in that: In step A007, the first online consultation area includes a common common questions consultation area, an expert care consultation system area, and a surgical question and answer consultation area.

3. The method for establishing a medical education platform based on Internet technology according to claim 1, characterized in that: In the step A0072, the medical user terminal record information and the predetermined related information database are input into the analysis test model to obtain preliminary analysis information, specifically including: The medical user terminal record information and the predetermined related information database are input into the analysis test model, and the analysis test model is obtained by training multiple sets of training data until convergence, wherein each set of data in the multiple sets of training data has the medical user terminal record information, the predetermined related information database and identification features for confirming preliminary analysis information.

4. The method for establishing a medical education platform based on Internet technology according to claim 3, characterized in that: The step A0072 further includes: A generation result of the analysis test model is obtained, wherein the generation result includes the preliminary analysis information.

5. The method for establishing a medical education platform based on Internet technology according to claim 4, characterized in that: In the step A0073, obtaining the medical user terminal associated information according to the medical user terminal user data and the medical user terminal associated user information aggregation also includes: A00731: obtaining the time information of the medical user terminal according to the record information of the medical user terminal, and obtaining the user storage database according to the user related to the medical user terminal and the time information of the medical user terminal; A00732: Summarize user information associated with the medical user terminal to obtain user-related features, perform a feature comparison on the user storage database based on the user-related features to obtain comparison information, and obtain the medical user terminal-related information based on the comparison information.

6. The method for establishing a medical education platform based on Internet technology according to claim 5, characterized in that: In step A0074, determining whether the medical user terminal associated information is associated with the medical user terminal record information includes: A00741: obtaining recorded transaction data according to the medical user terminal recorded information, obtaining transaction time data according to the recorded transaction data, and obtaining associated recorded transaction information according to the medical user terminal associated information; A00742: The associated recorded transaction information has associated transaction time information. A time association feature is obtained based on the associated transaction time information and the transaction time data, and a judgment is made based on the time association feature and the association.

7. The method for establishing a medical education platform based on Internet technology according to claim 6, characterized in that: In the step A007, after obtaining the preliminary construction data, the following steps are included: Obtaining a basic information base of the education platform, obtaining an adaptation basic information type based on the service content information and the education platform basic information base, and obtaining matching basic feature information; obtaining medical user historical viewing data through big data based on the medical user terminal basic information, inputting the service content information and the adaptation basic information type into the extraction information prototype, and obtaining basic feature information; Using the basic feature information, perform a feature comparison on the historical viewing data of the medical user to obtain a basic information comparison result, and obtain basic recommendation information according to the basic information comparison result and the matching basic feature information; According to the basic recommendation information, selection information is obtained, and according to the selection information and the service content information, information for establishing a publicity and education platform is obtained.

8. The method for establishing a medical education platform based on Internet technology according to claim 7, characterized in that: The information for establishing the education platform is to establish the platform according to the selection information and the service content information.

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