Health knowledge social platform management method and system based on block chain

Abstract

The invention discloses a health knowledge social platform management method and system based on a block chain, and relates to the technical field of social platform management, and the method comprises the steps: at a management method level, initially receiving and storing user registration and authorization information, constructing a user basic information matrix by means of one-hot coding, calculating a true index, encrypting the registration information according to the true index, and storing the encrypted registration information; and generating a unique authentication identifier. And then, locking health knowledge data published by the user by virtue of an identifier, auditing factors such as repetition and sensitivity by the system, judging whether the data can be published for the first time, collecting interactive information after publishing, establishing an influence matrix, and performing professional auditing. Meanwhile, interaction information is collected in real time to generate a real-time influence matrix, and the user is rewarded in combination with registration information. The system matched with the system comprises a user module, a knowledge module, an incentive module and a data management module, subdivision units of the modules cooperate in a division mode, force is generated from multiple links, the platform management efficiency is comprehensively improved, and the knowledge quality and the user experience are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of social platform management, and specifically relates to a method and system for managing a health knowledge social platform based on blockchain. Background Art

[0002] At present, people pay more and more attention to health. A vast amount of health knowledge floods the Internet but is of uneven quality. A method and system for managing a health knowledge social platform based on blockchain emerge as the times require. By utilizing the decentralized and immutable characteristics of blockchain, it ensures the authenticity of user information and the reliability of knowledge sources. On the one hand, it protects user privacy and rights, and on the other hand, it accurately screens and reviews health knowledge, promotes the effective dissemination of knowledge, helps the whole people improve their health literacy, and creates a trustworthy ecosystem for sharing health knowledge.

[0003] Due to the vast amount of health knowledge flooding the Internet and being of uneven quality, a large number of users will register on the health knowledge social platform. The existing methods and systems for managing a health knowledge social platform based on blockchain are difficult to accurately determine the authenticity of user information, and at the same time, it is difficult to ensure the security of user information and difficult to identify the authenticity of a vast amount of health knowledge. Therefore, a method and system for managing a health knowledge social platform based on blockchain are needed to solve the above-mentioned problems. Summary of the Invention

[0004] To solve the above technical problems, a method and system for managing a health knowledge social platform based on blockchain are provided. This technical solution solves the problems in the above background art that the existing methods and systems for managing a health knowledge social platform based on blockchain are difficult to accurately determine the authenticity of user information, and at the same time, it is difficult to ensure the security of user information and difficult to identify the authenticity of a vast amount of health knowledge.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for managing a health knowledge social platform based on blockchain, comprising:

[0007] S100. Receive and store user registration information and user authorization information, and then determine the user basic information matrix based on the user registration information, and combine the user authorization information to determine the user basic real index;

[0008] S200. Initially encrypt the user registration information based on the user basic real index, and generate unique authentication identification information based on the user registration information;

[0009] S300. Determine the health knowledge data published by the user based on the unique authentication identification information, and conduct system review on the health knowledge data published by the user to obtain the system review basic information matrix;

[0010] S400. Determine whether to initially publish the health knowledge data published by the user based on the system audit basic information matrix. If not, block the health knowledge data published by the user and send the system audit result to the user. If so, initially publish the health knowledge data published by the user and send system improvement suggestions to the user;

[0011] S500. Collect the user interaction information corresponding to the health knowledge data published by the user in the initial publication, and obtain the health knowledge impact matrix;

[0012] S600. Based on the health knowledge impact matrix and the health knowledge data published by the user, conduct a professional audit on the health knowledge data published by the user after the initial publication, and send the professional audit result and professional improvement suggestions to the user;

[0013] S700. Real-time obtain the real-time user interaction information corresponding to the health knowledge data published by the user, obtain the real-time health knowledge impact matrix, and issue reward information to the user based on the real-time health knowledge impact matrix and the user registration information.

[0014] In an optional embodiment, the receiving and storing the user registration information and the user authorization information, and then determining the user basic information matrix based on the user registration information, and combining the user authorization information to determine the user basic real index specifically includes:

[0015] Based on the user registration information, obtain the user registration timestamp information, and the user registration information includes obtaining the user age information, the user nickname information, the email information and the initial password information;

[0016] Based on one-hot encoding, perform data processing on the user registration information, generate user feature encoding information, and store the user feature encoding information in the blockchain;

[0017] According to the user age information, the user nickname information and the user feature encoding information, determine the first element, the second element and the third element of the user basic information matrix, and construct the user basic information matrix;

[0018] Based on the user nickname information and the user registration information, retrieve in the external health knowledge social platform to obtain the user heterogeneous published data;

[0019] Based on the user authorization information and the user registration information, screen the user heterogeneous published data to obtain the user heterogeneous health knowledge data;

[0020] Perform data processing on heterogeneous health knowledge data of users to obtain heterogeneous health knowledge video data, heterogeneous health knowledge audio data, and heterogeneous health knowledge text data of users, and sequentially obtain heterogeneous user interaction information corresponding to the heterogeneous health knowledge video data, heterogeneous health knowledge audio data, and heterogeneous health knowledge text data of users;

[0021] Based on audio extraction technology, extract the audio data in the heterogeneous health knowledge video data of users, and use speech recognition technology to convert the extracted audio data and the heterogeneous health knowledge audio data of users into text information to obtain first text information and second text information;

[0022] Based on natural language processing technology, perform semantic segmentation on the first text information, the second text information, and the heterogeneous health knowledge text data of users to obtain first text sub-information, second text sub-information, and third text sub-information;

[0023] According to the heterogeneous user interaction information, respectively obtain first heterogeneous user comment information, second heterogeneous user comment information, and third heterogeneous user comment information corresponding to the first text sub-information, the second text sub-information, and the third text sub-information;

[0024] Perform semantic segmentation on the first heterogeneous user comment information, the second heterogeneous user comment information, and the third heterogeneous user comment information to obtain fourth text sub-information, fifth text sub-information, and sixth text sub-information;

[0025] Respectively obtain the first matching degree, the second matching degree, and the third matching degree of the first text sub-information and the fourth text sub-information, the second text sub-information and the fifth text sub-information, and the third text sub-information and the sixth text sub-information;

[0026] Respectively obtain the first professionalism, the second professionalism, and the third professionalism of the first text sub-information, the second text sub-information, and the third text sub-information;

[0027] Quantify the first element, the second element, and the third element in the user basic information matrix to obtain a user basic information quantification matrix;

[0028] Based on the user basic information quantification matrix, the first matching degree, the second matching degree, the third matching degree, the first professionalism, the second professionalism, and the third professionalism, obtain a user basic true index;

[0029] The calculation formula of the user basic true index is:

[0030]

[0031] In the formula, U trueLet the user base true index be \(I\), \(M1\), \(M2\), and \(M3\) be the first matching degree, the second matching degree, and the third matching degree respectively, \(P1\), \(P2\), and \(P3\) be the first professionalism, the second professionalism, and the third professionalism respectively, \(R\) be the user base information quantization matrix, \(A\) text1 、 \(A\) text2 、 \(A\) text3 、 \(A\) text4 、 \(A\) text5 and \(A\) text6 are the numbers of phrases with semantic relationships in the first to sixth text sub-information respectively, \(C\) text14 、 \(C\) text25 and \(C\) text36 are the numbers of phrases with semantic relationships in the fourth to sixth text sub-information that are the same as those in the first to third text sub-information respectively, \(Z\) text1 、 \(Z\) text2 and \(Z\) text3 are the numbers of professional terms in the first to third text sub-information respectively, \(X1\), \(X2\), and \(X3\) are the first to third elements in the user base information quantization matrix, \(R\) T is the transpose matrix of the user base information quantization matrix.

[0032] In an optional embodiment, based on the user base true index, the user registration information is initially encrypted, and based on the user registration information, a unique authentication identification information is generated, specifically including:

[0033] Set the encryption level and determine the encryption level based on the user base true index;

[0034] According to the encryption level, select an encryption library and package the user registration information based on the encryption library to complete the initial encryption, and at the same time obtain and store the encryption certificate in the blockchain;

[0035] Based on the email information and the initial password information, perform secondary packaging on the encryption certificate stored in the blockchain;

[0036] Set prompt information during the secondary packaging according to the user registration information;

[0037] Based on the user registration information, determine the user age information and the user nickname information, and generate the first authentication identification information. At the same time, obtain the user initial image information in the user registration information, and then generate the second authentication identification information according to the user initial image information;

[0038] According to the user registration information, obtain the corresponding user base true index and determine the corresponding user feature coding information;

[0039] Generate the unique authentication identification information based on the first authentication identification information, the second authentication identification information, and the user feature coding information.

[0040] In an alternative embodiment, based on the unique authentication identification information, determining the health knowledge data published by the user and performing a system review on the health knowledge data published by the user to obtain a system review basic information matrix, specifically including:

[0041] Based on the unique authentication identification information, determining the user characteristic coding information;

[0042] Using the user characteristic coding information to determine the exclusive data storage space corresponding to the user in the blockchain, and at the same time obtaining the corresponding health knowledge data published by the user;

[0043] Extracting the metadata in the health knowledge data published by the user to obtain the submission time information, data format information, and estimated reading duration information;

[0044] Based on the submission time information, obtaining all the health knowledge data to be published submitted by the user within one month;

[0045] Obtaining the data type information of all the health knowledge data to be published, and classifying the health knowledge data to be published based on the data type information to obtain the sub-data of the health knowledge to be published;

[0046] Sequentially obtaining the text description information, picture information, and video information of the sub-data of the health knowledge to be published to obtain a plurality of first comparison information;

[0047] Obtaining the text description information, picture information, and video information in the health knowledge data published by the user to obtain basic comparison information;

[0048] Comparing the basic comparison information with the plurality of first comparison information sequentially to obtain a duplication index;

[0049] Based on natural language processing technology, performing sensitive word analysis on the basic comparison information, and obtaining a sensitivity index according to the sensitive word analysis result;

[0050] According to the duplication index, sensitivity index, data format information, and estimated reading duration information corresponding to the health knowledge data published by the user, performing a system review on the health knowledge data published by the user and establishing a system review basic information matrix.

[0051] In an alternative embodiment, collecting the user interaction information corresponding to the health knowledge data published by the user for the first time and obtaining a health knowledge impact matrix, specifically including:

[0052] Setting a collection time period and collecting the user interaction information corresponding to the health knowledge data published by the user for the first time based on the collection time period;

[0053] Obtain user interaction comment information, user like information, user favorite information, user forward information, and user citation information according to user interaction information;

[0054] Based on natural language processing technology, analyze the user interaction comment information, and construct a word frequency bar chart according to the analysis results;

[0055] Set a word frequency threshold based on the word frequency bar chart, and integrate the words with a word frequency higher than the word frequency threshold to obtain the user comment hot words;

[0056] Conduct sentiment analysis on the user comment hot words to obtain the user comment sentiment index, and at the same time determine the first influencing element;

[0057] Based on the user like information, user favorite information, and user forward information, perform a weighted sum of the user like count, user favorite count, and user forward count to obtain the second influencing element;

[0058] Obtain the user-published health knowledge data corresponding to the user citation information to obtain the cited health knowledge data, and obtain the user interaction information corresponding to the cited health knowledge data to obtain the third influencing element;

[0059] Construct a health knowledge influence matrix through the first influencing element, the second influencing element, and the third influencing element.

[0060] In an optional embodiment, the real-time user interaction information corresponding to the user-published health knowledge data is obtained in real time to obtain a real-time health knowledge influence matrix, and reward information is issued to the user based on the real-time health knowledge influence matrix and the user registration information. Specifically, it includes:

[0061] Based on the real-time user interaction information, obtain the first real-time influencing element, the second real-time influencing element, and the third real-time influencing element;

[0062] Use the first to third real-time influencing elements as the first to third elements of the real-time health knowledge influence matrix. Both the health knowledge influence matrix and the real-time health knowledge influence matrix are 1×3 row matrices;

[0063] Based on the real-time health knowledge influence matrix, determine the reward content information, determine the unique authentication identification information through the user registration information, and at the same time determine the user feature coding information;

[0064] Upgrade the reward content information according to the unique authentication identification information to obtain the upgraded reward content information;

[0065] Issue reward information to the user based on the upgraded reward content information and the user feature coding information.

[0066] Further, a health knowledge social platform management system based on blockchain is proposed for implementing the management method as described in any one of the above, including:

[0067] A user management module, which is used to receive and store user registration information and user authorization information, process the user registration information based on one-hot encoding, generate user feature encoding information and store it in the blockchain. At the same time, obtain the user registration timestamp information, construct a user basic information matrix based on the user's age, nickname and feature encoding information, retrieve the user's heterogeneous published data on an external platform based on the user's nickname and registration information, screen out the user's heterogeneous health knowledge data in combination with the authorization information, obtain different types of heterogeneous data and corresponding interaction information after processing, calculate the user's basic true index through a series of semantic analysis, determine the encryption level according to the user's basic true index, select an encryption library to encrypt the user registration information for the first time and obtain an encryption certificate to store in the blockchain, then repackage the encryption certificate based on the email address and the initial password, and set prompt information at the same time. Generate a unique authentication identification information using the user's age, nickname, initial image and feature encoding information;

[0068] A knowledge management module, which is used to determine the user feature encoding based on the unique authentication identification information, find the exclusive data storage space of the user in the blockchain, obtain the user's published health knowledge data, obtain the data to be published within one month and classify it. Obtain the duplication index by comparing the basic comparison information with multiple first comparison information, perform sensitive word analysis on the basic comparison information to obtain the sensitivity index, complete the system review in combination with the data format and the estimated reading duration information, establish a system review basic information matrix, and judge whether it is the first time to publish knowledge data. If it fails, block the data and inform the user of the review result. If it passes, publish it and give improvement suggestions. Set a collection time period to collect the user interaction information corresponding to the user's published health knowledge data for the first time, including comment, like, favorite, forward and citation information. Analyze the user interaction comment information to construct a word frequency histogram, set a word frequency threshold to obtain the user comment hot words, perform sentiment analysis to obtain the user comment sentiment index to determine the first influencing element, perform weighted summation on the like, favorite and forward quantities to obtain the second influencing element, obtain the knowledge data and interaction information corresponding to the citation information to determine the third influencing element, construct a health knowledge influence matrix, and perform professional review based on the health knowledge influence matrix and the user's published health knowledge data, and send the professional review result and professional improvement suggestions to the user;

[0069] Incentive management module, which is used to obtain the real-time user interaction information corresponding to the user's published health knowledge data in real time, obtain the first to third real-time influence elements of the real-time health knowledge influence matrix, construct the real-time health knowledge influence matrix, determine the reward content information according to the real-time health knowledge influence matrix, and combine the unique authentication identification information and feature coding information in the user registration information to upgrade the reward content and then issue the reward information to the user;

[0070] Data management module, which is used to store various data such as user feature coding information, encryption certificate, and user's published health knowledge data into the blockchain to ensure the security and immutability of the data, and assist other modules in performing various data analyses. For example, in the user management module, analyze the user's heterogeneous health knowledge data to determine the user's basic real index, in the knowledge management module, analyze the repetition degree, sensitivity, etc. of the user's published health knowledge data, and in the incentive management module, assist in analyzing the real-time user interaction information to determine the real-time health knowledge influence matrix.

[0071] In an alternative embodiment, the user management module includes:

[0072] Registration information processing unit, which is used to receive and store the user registration information and user authorization information, perform data processing on the user registration information based on one-hot encoding, generate user feature coding information and store it into the blockchain, and at the same time obtain the user registration timestamp information;

[0073] Basic information determination unit, which is used to construct a user basic information matrix based on the user's age, nickname, and feature coding information, retrieve the user's heterogeneous published data on an external platform based on the user's nickname and registration information, and screen out the user's heterogeneous health knowledge data in combination with the authorization information. After processing, different types of heterogeneous data and corresponding interaction information are obtained, and the user's basic real index is calculated through a series of semantic analyses;

[0074] Encryption authentication unit, which is used to determine the encryption level according to the user's basic real index, select an encryption library to perform primary encryption on the user registration information and obtain the encryption certificate and store it into the blockchain, then perform secondary packaging on the encryption certificate based on the email address and initial password, and at the same time set prompt information, and generate unique authentication identification information using the user's age, nickname, initial image, and feature coding information.

[0075] In an alternative embodiment, the knowledge management module includes:

[0076] A knowledge release review unit, which is used to determine the user feature code based on the unique authentication identification information, find the exclusive data storage space of the user in the blockchain, obtain the user's released health knowledge data, obtain the data to be released within one month and classify it, obtain the duplication index by comparing the basic comparison information with multiple first comparison information, perform sensitive word analysis on the basic comparison information to obtain the sensitivity index, complete the system review by combining the data format and the estimated reading duration information, establish the system review basic information matrix, determine whether it is the first time to release knowledge data, if not passed, block the data and inform the user of the review result, if passed, release it and give improvement suggestions;

[0077] An interactive information collection unit, which is used to set the collection time period, collect the user interactive information corresponding to the user's released health knowledge data for the first time, including comments, likes, collections, forwards and citation information, analyze the user interactive comment information to construct a word frequency histogram, set the word frequency threshold to obtain the user comment hot words, perform sentiment analysis to obtain the user comment sentiment index to determine the first influencing element, perform a weighted sum of the number of likes, collections and forwards to obtain the second influencing element, obtain the knowledge data and interactive information corresponding to the citation information to determine the third influencing element, and construct a health knowledge influence matrix;

[0078] A professional review unit, which is used to perform professional review based on the health knowledge influence matrix and the user's released health knowledge data, and send the professional review result and professional improvement suggestions to the user.

[0079] In an optional embodiment, the incentive management module includes:

[0080] A real-time influence acquisition unit, which is used to acquire the real-time user interactive information corresponding to the user's released health knowledge data in real time, obtain the first to third real-time influence elements of the real-time health knowledge influence matrix, and construct the real-time health knowledge influence matrix;

[0081] A reward distribution unit, which is used to determine the reward content information according to the real-time health knowledge influence matrix, combine the unique authentication identification information and the feature code information in the user registration information, and distribute the reward information to the user after upgrading the reward content.

[0082] Compared with the prior art, the beneficial effects of the present invention are:

[0083] A management method and system for a blockchain-based health knowledge social platform proposed in this solution determine the user's basic real index through multi-dimensional analysis, encrypt the user registration information accordingly, generate a unique authentication identifier, ensure that the user information is stored in the blockchain truly, reliably and securely, prevent information leakage and tampering, conduct system review and professional review on the health knowledge data published by users, evaluate from multiple aspects such as duplication and sensitivity, filter out low-quality and false information, improve the knowledge quality of the platform, construct an influence matrix by collecting user interaction information, quantify the knowledge dissemination effect, such as user comment sentiment, likes, collections and forwards, etc., provide a basis for knowledge optimization and dissemination, make up for the lack of evaluation of the knowledge dissemination effect in the existing platform, obtain the real-time interaction information of users in real time to determine the reward content, upgrade the reward in combination with the user identifier and feature coding, stimulate the enthusiasm of users to participate in and create high-quality content, and improve the situation that the incentive mechanism of the existing platform is single or lacks pertinence. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 is a flowchart of a management method for a blockchain-based health knowledge social platform proposed by the present invention;

[0085] Figure 2 and Figure 3 is a flowchart for obtaining the user's basic real index in the present invention;

[0086] Figure 4 is a system framework diagram of a management system for a blockchain-based health knowledge social platform proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0087] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0088] Referring to Figure 1 - Figure 4 shown, a management method for a blockchain-based health knowledge social platform includes:

[0089] S100. Receive and store the user registration information and user authorization information, and then determine the user basic information matrix based on the user registration information, and combine the user authorization information to determine the user basic real index;

[0090] S200. Based on the user basic real index, initially encrypt the user registration information, and generate a unique authentication identifier information based on the user registration information;

[0091] S300. Based on the unique authentication identifier information, determine the health knowledge data published by the user, and conduct a system review on the health knowledge data published by the user to obtain a system review basic information matrix;

[0092] S400. According to the system audit basic information matrix, determine whether to initially release the user-published health knowledge data. If not, block the user-published health knowledge data and send the system audit result to the user. If so, initially release the user-published health knowledge data and send system improvement suggestions to the user.

[0093] S500. Collect the user interaction information corresponding to the user-published health knowledge data initially released, and obtain the health knowledge impact matrix.

[0094] S600. Based on the health knowledge impact matrix and the user-published health knowledge data, conduct a professional audit on the user-published health knowledge data after the initial release, and send the professional audit result and professional improvement suggestions to the user.

[0095] S700. Real-time obtain the real-time user interaction information corresponding to the user-published health knowledge data, obtain the real-time health knowledge impact matrix, and issue reward information to the user based on the real-time health knowledge impact matrix and the user registration information.

[0096] Specifically, the user registration information includes user age information, user nickname information, etc. The user authorization information is used to screen the user heterogeneous published data obtained from the external health knowledge social platform to obtain user heterogeneous health knowledge data, which helps to determine the user's basic true index. The user heterogeneous published data is retrieved from the external health knowledge social platform based on the user nickname information and the user registration information. After being screened and processed, it is used to analyze the performance of the user's health knowledge-related data on other platforms, providing a reference for determining the user's basic true index. The user heterogeneous health knowledge data is screened from the user heterogeneous published data and further processed into user heterogeneous health knowledge video data, user heterogeneous health knowledge audio data, and user heterogeneous health knowledge text data, which are used to determine the user's basic true index by analyzing the corresponding heterogeneous user interaction information. The user-published health knowledge data is the health knowledge-related content published by the user on the platform, including metadata such as submission time information, data format information, and estimated reading duration information, as well as text description information, picture information, and video information, etc. The system audits and manages its publication and evaluates its influence based on the user interaction information. The to-be-published health knowledge data is all the health knowledge data submitted by the user and waiting for publication within one month. By classifying and comparing it, it helps to judge the duplication degree of the user-published health knowledge data, etc., for system audit. The real-time user interaction information is the user interaction content corresponding to the user-published health knowledge data obtained in real time, which is used to construct a real-time health knowledge influence matrix, and then determine the reward information for the user. The user feature encoding information is generated by processing the user registration information through one-hot encoding and stored in the blockchain. It is used to identify the user features and plays a key role in operations such as determining the unique authentication identification information and finding the user's exclusive data storage space. The encryption certificate is obtained after initially encrypting the user registration information based on the selected encryption library and stored in the blockchain. It is used to prove the encryption status and security of the user registration information. The prompt information is set during the secondary packaging of the encryption certificate based on the user registration information and may be used for the user to prompt or confirm encryption-related information in subsequent operations. The first to sixth text sub-information is obtained by semantic segmentation of the text information (the first to third text information) after converting the user heterogeneous health knowledge data and its corresponding heterogeneous user comment information. It is used to calculate the matching degree, professionalism, etc., to determine the user's basic true index. The first to third comparison information is respectively composed of the text description information, picture information, and video information of the to-be-published health knowledge sub-data and the user-published health knowledge data. By comparing, the duplication index is obtained, which helps the system audit the user-published health knowledge data. The user's basic true index is calculated by comprehensively considering the user's basic information quantization matrix, the matching degree and professionalism of different text information, etc. It is used to determine the encryption level of the user registration information and reflects the authenticity and professionalism of the user information and the health knowledge-related data on the external platform.The duplication index is obtained by comparing the basic comparison information of the user's published health knowledge data with multiple first comparison information of the health knowledge sub-data to be published, and is used for the system to review and judge the duplication situation of the user's published health knowledge data. The sensitivity index is obtained by analyzing sensitive words in the basic comparison information of the user's published health knowledge data based on natural language processing technology, and is used for the system to review and judge the sensitivity degree of the user's published health knowledge data. The user comment sentiment index is obtained by performing sentiment analysis on the hot words of user comments, and is used as the first influencing element for constructing the health knowledge influence matrix, reflecting the user's sentiment tendency towards the initially published health knowledge data. The user basic information matrix is constructed after determining its first element, second element, and third element according to the user age information, user nickname information, and user characteristic coding information, and is a form of quantifying the user's basic information, used for calculating the user's basic true index. The system review basic information matrix is established by combining the duplication index, sensitivity index, data format information, and estimated reading duration information corresponding to the user's published health knowledge data, and is used to judge whether to initially publish the user's published health knowledge data. The health knowledge influence matrix is constructed by the first influencing element (determined based on the user comment sentiment index), the second influencing element (the weighted sum of the user like count, user favorite count, and user share count), and the third influencing element (the relevant data corresponding to the user citation information), and is used to evaluate the influence of the user's published health knowledge data initially published, and based on this, professional review is carried out. The real-time health knowledge influence matrix is constructed with the first to third real-time influencing elements obtained based on real-time user interaction information as its first to third elements, and is used to determine the reward content information for the user.

[0097] Further, receiving and storing the user registration information and user authorization information, and then determining the user basic information matrix based on the user registration information, and combining the user authorization information to determine the user basic true index, specifically includes:

[0098] Based on the user registration information, obtain the user registration timestamp information, and the user registration information includes obtaining the user age information, user nickname information, email information, and initial password information;

[0099] Based on one-hot encoding, perform data processing on the user registration information to generate user characteristic coding information, and store the user characteristic coding information in the blockchain;

[0100] According to the user age information, user nickname information, and user characteristic coding information, determine the first element, second element, and third element of the user basic information matrix, and construct the user basic information matrix;

[0101] Based on the user nickname information and user registration information, retrieve in the external health knowledge social platform to obtain the user heterogeneous published data;

[0102] Filter the heterogeneous published data of users based on the user authorization information and user registration information to obtain the heterogeneous health knowledge data of users;

[0103] Process the heterogeneous health knowledge data of users to obtain the heterogeneous health knowledge video data, heterogeneous health knowledge audio data and heterogeneous health knowledge text data of users, and sequentially obtain the heterogeneous user interaction information corresponding to the heterogeneous health knowledge video data, heterogeneous health knowledge audio data and heterogeneous health knowledge text data of users;

[0104] Based on the audio extraction technology, extract the audio data from the heterogeneous health knowledge video data of users, and use the speech recognition technology to convert the extracted audio data and the heterogeneous health knowledge audio data of users into text information to obtain the first text information and the second text information;

[0105] Based on the natural language processing technology, perform semantic segmentation on the first text information, the second text information and the heterogeneous health knowledge text data of users to obtain the first text sub-information, the second text sub-information and the third text sub-information;

[0106] According to the heterogeneous user interaction information, respectively obtain the first heterogeneous user comment information, the second heterogeneous user comment information and the third heterogeneous user comment information corresponding to the first text sub-information, the second text sub-information and the third text sub-information;

[0107] Perform semantic segmentation on the first heterogeneous user comment information, the second heterogeneous user comment information and the third heterogeneous user comment information to obtain the fourth text sub-information, the fifth text sub-information and the sixth text sub-information;

[0108] Respectively obtain the first matching degree, the second matching degree and the third matching degree of the first text sub-information and the fourth text sub-information, the second text sub-information and the fifth text sub-information, and the third text sub-information and the sixth text sub-information;

[0109] Respectively obtain the first professionalism, the second professionalism and the third professionalism of the first text sub-information, the second text sub-information and the third text sub-information;

[0110] Quantify the first element, the second element and the third element in the user basic information matrix to obtain the user basic information quantization matrix;

[0111] Based on the user basic information quantization matrix, the first matching degree, the second matching degree, the third matching degree, the first professionalism, the second professionalism and the third professionalism, obtain the user basic true index;

[0112] The calculation formula of the user basic true index is:

[0113]

[0114] Wherein, U true is the user base true index, M1, M2, and M3 are the first matching degree, the second matching degree, and the third matching degree respectively, P1, P2, and P3 are the first professionalism, the second professionalism, and the third professionalism respectively, R is the user base information quantization matrix, A text1 , A text2 , A text3 , A text4 , A text5 , and A text6 are the numbers of phrases with semantic relationships in the first to sixth text sub-information respectively, C text14 , C text25 , and C text36 are the numbers of phrases with semantic relationships in the fourth to sixth text sub-information that are the same as those in the first to third text sub-information respectively, Z text1 , Z text2 , and Z text3 are the numbers of professional terms in the first to third text sub-information respectively, X1, X2, and X3 are the first to third elements in the user base information quantization matrix, and R T is the transpose matrix of the user base information quantization matrix.

[0115] Specifically, the timestamp is used to record the specific time of user registration, providing a time dimension reference for subsequent data processing and analysis. The one-hot encoding is used to process the user registration information and convert it into user feature encoding information. The one-hot encoding is a method of converting categorical variables into a form that is easy for machine learning algorithms to process. For example, the categorical information in the user registration information (such as different age ranges, different nickname types, etc.) is converted into binary vectors. The generated user feature encoding information is stored in the blockchain, and the immutable feature of the blockchain is used to ensure the security and traceability of the data. Combining the user authorization information and the user registration information, the user heterogeneous published data is screened to obtain the user heterogeneous health knowledge data. This is used to screen out the data related to health knowledge and exclude other irrelevant information to ensure the pertinence of subsequent analysis.

[0116] It is understandable that the first matching degree, the second matching degree, and the third matching degree of the first text sub - information and the fourth text sub - information, the second text sub - information and the fifth text sub - information, and the third text sub - information and the sixth text sub - information are calculated respectively. The matching degree can reflect the consistency or relevance between the health knowledge information published by the user and the user's comments. For example, whether the user's content matches what the comment expects. The first professionalism, the second professionalism, and the third professionalism of the first text sub - information, the second text sub - information, and the third text sub - information are obtained respectively. The professionalism can measure the professional level of the health knowledge information published by the user, such as whether it contains professional terms and whether it conforms to the expression of professional knowledge. The text description information, picture information, and video information of the health knowledge sub - data to be published are obtained in sequence to obtain multiple first comparison information. For example, different types of health knowledge sub - data to be published have different structures to obtain this information. The basic comparison information is compared with the multiple first comparison information in sequence to obtain a duplication index. Here, taking the simple string similarity comparison of text description information as an example, the difflib library is used to calculate the similarity. Based on natural language processing technology, the basic comparison information is analyzed for sensitive words, and according to the result of the sensitive word analysis, a sensitivity index is obtained. The pymupdf library is used to process the text, and the sensitive words can be stored in a list named sensitive_words.

[0117] Furthermore, the user heterogeneous published data is specifically the health knowledge data published by users obtained by the management system from other health knowledge social platforms. However, when obtaining data from other platforms, it may involve cooperation and competition relationships between platforms. Some platforms may be reluctant to provide user data or set restrictions on the use of data. The platform can establish cooperative relationships with other platforms, negotiate the ways and conditions of data sharing to achieve mutual benefit and win - win results. At the same time, other legal ways to obtain data can also be explored, such as through third - party data service providers, etc. In addition, a user submission module can be set up in the management system for users to provide fragments or all of the health knowledge data they published on other platforms.

[0118] Furthermore, based on the user - based real index, the user registration information is initially encrypted, and a unique authentication identification information is generated based on the user registration information, which specifically includes:

[0119] Set the encryption level, and determine the encryption level based on the user - based real index;

[0120] According to the encryption level, select an encryption library, and package the user registration information based on the encryption library to complete the initial encryption. At the same time, obtain and store the encryption certificate in the blockchain;

[0121] Based on the email information and the initial password information, perform secondary packaging on the encryption certificate stored in the blockchain;

[0122] Set prompt information during the secondary packaging process according to the user registration information;

[0123] Based on the user registration information, determine the user age information and the user nickname information, and generate the first authentication identification information. At the same time, obtain the user initial image information in the user registration information, and then generate the second authentication identification information according to the user initial image information;

[0124] Obtain the corresponding user basic truth index according to the user registration information, and determine the corresponding user feature coding information;

[0125] Generate the unique authentication identification information based on the first authentication identification information, the second authentication identification information and the user feature coding information.

[0126] Specifically, set the encryption level. Different encryption levels are predefined. For example, it can be simply divided into three levels: low-level encryption is applicable to general user information, medium-level encryption is used for more important information, and high-level encryption is used for highly sensitive information. It can also be further divided into more levels according to actual needs. For example, the encryption level is corresponding to the security risk assessment indicators, and different security intensities and applicable scenarios are set for each level. Determine the encryption level, specifically by establishing a mapping relationship between the range of the user's basic real index and the encryption level. For example, if the range of the user's basic real index is 0 - 100, when the index is between 0 - 30, it corresponds to low-level encryption; 31 - 60 corresponds to medium-level encryption; 61 - 100 corresponds to high-level encryption. Select the encryption library. According to different encryption levels, configure the corresponding encryption library. For example, for low-level encryption, some simple and high-performance symmetric encryption algorithm libraries can be selected, such as the DES algorithm in the pycryptodome library; for medium-level encryption, the AES algorithm can be selected (the pycryptodome library can also be used); for high-level encryption, more complex asymmetric encryption algorithm libraries may be adopted, such as the RSA algorithm (which can also be implemented in the pycryptodome library). Initial encryption. Taking AES encryption as an example, for instance, the user registration information is stored in a dictionary user_registration_info and has been converted into a string format data_to_encrypt. The encryption key key needs to be generated in advance and properly stored. Obtain and store the encryption certificate. After encryption is completed, obtain the encryption certificate. The encryption certificate usually contains information such as the encryption algorithm, key length, validity period, etc. In practical applications, some certificate generation tools, such as OpenSSL, can be used to generate the encryption certificate. After generating the certificate, store it in the blockchain. If the Ethereum blockchain is used, the storage of the certificate can be achieved through smart contracts. Secondary packaging implementation. Secondary packaging can be understood as further obfuscating or encrypting the encryption certificate. For example, the email information and the initial password information can be subjected to a certain hash operation (such as using the SHA-256 algorithm), and then the hash result is concatenated or XOR-operated with the encryption certificate and other operations to enhance the security of the encryption certificate.

[0127] Further, based on the unique authentication identification information, determine the user's published health knowledge data, and conduct system review on the user's published health knowledge data to obtain the system review basic information matrix, which specifically includes:

[0128] Based on the unique authentication identification information, determine the user feature coding information;

[0129] Based on the user feature coding information, determine the exclusive data storage space corresponding to the user in the blockchain, and at the same time obtain the corresponding user's published health knowledge data;

[0130] Extract the metadata from the user-published health knowledge data to obtain the submission time information, data format information, and estimated reading duration information;

[0131] Based on the submission time information, obtain all the to-be-published health knowledge data submitted by the user within one month;

[0132] Obtain the data type information of all the to-be-published health knowledge data, and classify the to-be-published health knowledge data based on the data type information to obtain the to-be-published health knowledge sub-data;

[0133] Successively obtain the text description information, picture information, and video information of the to-be-published health knowledge sub-data to obtain multiple first comparison information;

[0134] Obtain the text description information, picture information, and video information in the user-published health knowledge data to obtain the basic comparison information;

[0135] Compare the basic comparison information with multiple first comparison information successively to obtain the duplication index;

[0136] Based on natural language processing technology, perform sensitive word analysis on the basic comparison information, and obtain the sensitivity index according to the sensitive word analysis results;

[0137] According to the duplication index, sensitivity index, data format information, and estimated reading duration information corresponding to the user-published health knowledge data, conduct a systematic review of the user-published health knowledge data and establish a systematic review basic information matrix.

[0138] Specifically, based on the unique authentication identification information, user characteristic coding information is determined. The unique authentication identification information is stored in a database and is associated with the user characteristic coding information. The corresponding user characteristic coding information can be obtained by querying the database. Using the user characteristic coding information, a dedicated data storage space corresponding to the user is determined in the blockchain, and at the same time, the user's published health knowledge data is obtained. Here, for example, there is already a function (get_user_data_from_blockchain) to obtain data from the blockchain. This function receives the user characteristic coding information as a parameter and returns the user's published health knowledge data. Extract the metadata from the user's published health knowledge data to obtain the submission time information, data format information, and estimated reading duration information. For example, the user's published health knowledge data is a dictionary containing this metadata. Based on the submission time information, all the unpublished health knowledge data submitted by the user within a month is obtained. For example, all the unpublished health knowledge data is stored in a list, and each element is a dictionary containing the submission time. Obtain the data type information of all the unpublished health knowledge data, and based on the data type information, classify the unpublished health knowledge data to obtain the unpublished health knowledge sub-data. For example, there is a data_type field in each unpublished health knowledge data dictionary to represent the data type.

[0139] Further, collecting the user interaction information corresponding to the user's published health knowledge data for the first time and obtaining the health knowledge influence matrix specifically includes:

[0140] Set the collection time period, and collect the user interaction information corresponding to the user's published health knowledge data for the first time based on the collection time period;

[0141] According to the user interaction information, obtain the user interaction comment information, user like information, user favorite information, user forward information, and user citation information;

[0142] Based on natural language processing technology, analyze the user interaction comment information, and based on the analysis results, construct a word frequency histogram;

[0143] Set a word frequency threshold based on the word frequency histogram, and integrate the words with a word frequency higher than the word frequency threshold to obtain the user comment hot words;

[0144] Perform sentiment analysis on the user comment hot words to obtain the user comment sentiment index, and at the same time determine the first influencing element;

[0145] Based on the user like information, user favorite information, and user forward information, perform a weighted sum of the user like count, user favorite count, and user forward count to obtain the second influencing element;

[0146] Obtain the user-published health knowledge data corresponding to the user reference information to get the referenced health knowledge data, and obtain the user interaction information corresponding to the referenced health knowledge data, and obtain the third influencing element;

[0147] Construct a health knowledge influence matrix through the first influencing element, the second influencing element, and the third influencing element.

[0148] Specifically, setting the collection time period requires determining the period unit: according to the platform business requirements and data characteristics, select a suitable time unit, such as seconds, minutes, hours, days, weeks, or months, etc. For example, if the platform data is updated frequently and has high real-time requirements, minutes or hours may be selected as the unit; if the data changes relatively slowly, days or weeks are more appropriate as the unit. Set the specific duration: After selecting the time unit, determine the specific duration. For example, set it to the last 7 days, 30 days, or the past 24 hours, etc. This requires considering the timeliness of the data and the analysis objectives comprehensively. A shorter period can reflect the immediate feedback of users recently, while a longer period can show longer trends and stability. To collect user interaction information, it is necessary to determine the data source: clarify from which channels to obtain user interaction information, which may include databases, log files, message queues, etc. For example, there may be a dedicated table in the database to record operations such as user comments and likes on health knowledge data; the log file details various user behaviors and their timestamps. Formulate collection rules: Based on the set time period, formulate rules for filtering data. If the database is used as the data source, SQL query statements can be written. By comparing the interaction time field with the current time, records that meet the time period can be filtered out. For example, to collect interaction information in the last 7 days, an SQL statement like SELECT*FROM user_interactions WHERE interaction_time>=CURDATE()-INTERVAL 7DAY can be used. Based on natural language processing technology, analyze the user interaction comment information, and based on the analysis results, construct a word frequency histogram. First, special characters, HTML tags, stop words, and punctuation marks in the comments need to be removed, then all texts are converted into a unified form, and then stemming is performed. Use statistical tools or dictionaries to count the word frequency, and finally use a drawing tool to construct the word frequency histogram. Conduct sentiment analysis on the hot words in the user comments to obtain the user comment sentiment index, and at the same time determine the first influencing element. First, a method for sentiment analysis needs to be selected, including dictionary-based methods and machine learning methods. For example, use a predefined sentiment dictionary, such as the VADER dictionary in NLTK, which has good results in sentiment analysis of social media texts. This dictionary assigns positive, negative, or neutral sentiment scores to each word, and by matching and summarizing the scores of the words in the text, the overall sentiment tendency can be obtained.

[0149] It can be understood that when calculating the sentiment index, machine learning methods can be used to input the text composed of hot words into a trained model. The model outputs the probabilities of positive, negative, or neutral, and the sentiment index is calculated based on these probabilities. For example, if the model outputs a positive probability of 0.7 and a negative probability of 0.3, the sentiment index can be defined as 0.7 - 0.3 = 0.4, indicating that the overall sentiment tendency is positive.

[0150] Based on the user's like information, user's favorite information, and user's forward information, the number of user likes, the number of user favorites, and the number of user forwards are weighted and summed to obtain the second influencing element. First, the weights need to be determined, and the weights are determined according to the platform's emphasis on different interaction behaviors. For example, if the platform believes that the like behavior indicates the user's immediate recognition of the content, the favorite indicates the long-term value of the content, and the forward means the spread of the content, the weights can be adjusted according to the business objectives. If it is desired to emphasize the spread of the content, a higher weight may be given to the forward behavior, such as a like weight of 0.3, a favorite weight of 0.3, and a forward weight of 0.4. The formula Second influencing element = number of likes × like weight + number of favorites × favorite weight + number of forwards × forward weight is used for calculation.

[0151] Furthermore, the real-time user interaction information corresponding to the user's published health knowledge data is obtained in real time to obtain a real-time health knowledge influence matrix, and reward information is issued to the user based on the real-time health knowledge influence matrix and the user registration information. Specifically, it includes:

[0152] Based on the real-time user interaction information, the first real-time influencing element, the second real-time influencing element, and the third real-time influencing element are obtained;

[0153] Using the first to third real-time influencing elements as the first to third elements of the real-time health knowledge influence matrix, both the health knowledge influence matrix and the real-time health knowledge influence matrix are 1×3 row matrices;

[0154] Based on the real-time health knowledge influence matrix, the reward content information is determined, and the unique authentication identification information is determined through the user registration information, and at the same time, the user feature coding information is determined;

[0155] According to the unique authentication identification information, the reward content information is upgraded to obtain the upgraded reward content information;

[0156] Based on the upgraded reward content information and the user feature coding information, reward information is issued to the user.

[0157] Furthermore, a health knowledge social platform management system based on blockchain is proposed for implementing the management method as described in any one of the above, including:

[0158] User management module, which is used to receive and store user registration information and user authorization information, process the user registration information based on one-hot encoding to generate user feature encoding information and store it in the blockchain. At the same time, obtain the user registration timestamp information, construct a user basic information matrix based on the user's age, nickname, and feature encoding information, retrieve the user's heterogeneous published data on an external platform based on the user's nickname and registration information, screen out the user's heterogeneous health knowledge data in combination with the authorization information, and obtain different types of heterogeneous data and corresponding interaction information after processing. Calculate the user's basic true index through a series of semantic analyses, determine the encryption level according to the user's basic true index, select an encryption library to encrypt the user registration information for the first time and obtain an encryption certificate to store in the blockchain, then repackage the encryption certificate based on the email address and initial password, and set prompt information at the same time. Generate a unique authentication identification information using the user's age, nickname, initial image, and feature encoding information;

[0159] Knowledge management module, which is used to determine the user feature encoding based on the unique authentication identification information, find the exclusive data storage space of the user in the blockchain, obtain the user's published health knowledge data, obtain the data to be published within one month and classify it, obtain the duplication index by comparing the basic comparison information with multiple first comparison information, perform sensitive word analysis on the basic comparison information to obtain the sensitivity index, complete system review in combination with the data format and estimated reading duration information, establish a system review basic information matrix, determine whether it is the first time to publish knowledge data. If it fails, block the data and inform the user of the review result. If it passes, publish it and give improvement suggestions. Set a collection time period to collect the user interaction information corresponding to the user's published health knowledge data for the first time, including comment, like, favorite, forward, and citation information. Analyze the user interaction comment information to construct a word frequency histogram, set a word frequency threshold to obtain the user comment hot words, perform sentiment analysis to obtain the user comment sentiment index to determine the first influencing element, perform weighted summation on the like, favorite, and forward quantities to obtain the second influencing element, obtain the knowledge data and interaction information corresponding to the citation information to determine the third influencing element, construct a health knowledge influence matrix, perform professional review based on the health knowledge influence matrix and the user's published health knowledge data, and send the professional review result and professional improvement suggestions to the user;

[0160] Incentive management module, which is used to obtain the real-time user interaction information corresponding to the user's published health knowledge data in real time, obtain the first to third real-time influencing elements of the real-time health knowledge influence matrix, construct the real-time health knowledge influence matrix, determine the reward content information according to the real-time health knowledge influence matrix, and combine the unique authentication identification information and feature encoding information in the user registration information to upgrade the reward content and then send the reward information to the user;

[0161] A data management module, which is used to store various types of data such as user feature encoding information, encryption certificates, and user-published health knowledge data into the blockchain to ensure the security and immutability of the data, and assist other modules in performing various data analyses. For example, in the user management module, it analyzes heterogeneous health knowledge data of users to determine the user's basic true index; in the knowledge management module, it analyzes the duplication degree, sensitivity, etc. of user-published health knowledge data; in the incentive management module, it assists in analyzing real-time user interaction information to determine the real-time health knowledge influence matrix.

[0162] Furthermore, the user management module includes:

[0163] A registration information processing unit, which is used to receive and store user registration information and user authorization information, perform data processing on the user registration information based on one-hot encoding, generate user feature encoding information and store it into the blockchain, and at the same time obtain the user registration timestamp information;

[0164] A basic information determination unit, which is used to construct a user basic information matrix based on the user's age, nickname, and feature encoding information, retrieve the user's heterogeneous published data on an external platform based on the user's nickname and registration information, screen out the user's heterogeneous health knowledge data in combination with the authorization information, obtain different types of heterogeneous data and corresponding interaction information after processing, and calculate the user's basic true index through a series of semantic analyses;

[0165] An encryption authentication unit, which is used to determine the encryption level according to the user's basic true index, select an encryption library to perform primary encryption on the user registration information and obtain an encryption certificate to store it into the blockchain, then perform secondary packaging on the encryption certificate based on the email address and initial password, and at the same time set prompt information, and generate a unique authentication identification information by using the user's age, nickname, initial image, and feature encoding information.

[0166] Furthermore, the knowledge management module includes:

[0167] A knowledge publishing review unit, which is used to determine the user feature encoding based on the unique authentication identification information, find the exclusive data storage space of the user in the blockchain, obtain the user-published health knowledge data, obtain the data to be published within one month and classify it, obtain the duplication degree index by comparing the basic comparison information with multiple first comparison information, perform sensitive word analysis on the basic comparison information to obtain the sensitivity index, complete the system review in combination with the data format and estimated reading duration information, establish a system review basic information matrix, judge whether it is the first time to publish knowledge data, if not passed, then block the data and inform the user of the review result, if passed, then publish and give improvement suggestions;

[0168] An interactive information collection unit, which is used to set a collection time period, collect user interaction information corresponding to the user's published health knowledge data for the first time, including comments, likes, collections, forwards, and citation information, analyze the user interaction comment information to construct a word frequency histogram, set a word frequency threshold to obtain hot words in user comments, perform sentiment analysis to obtain a user comment sentiment index to determine the first influencing element, perform a weighted sum of the number of likes, collections, and forwards to obtain a second influencing element, obtain the knowledge data and interaction information corresponding to the citation information to determine a third influencing element, and construct a health knowledge influence matrix;

[0169] A professional review unit, which is used to conduct a professional review based on the health knowledge influence matrix and the user's published health knowledge data, and send the professional review results and professional improvement suggestions to the user.

[0170] Further, the incentive management module includes:

[0171] A real-time influence acquisition unit, which is used to acquire real-time user interaction information corresponding to the user's published health knowledge data in real time, obtain the first to third real-time influence elements of the real-time health knowledge influence matrix, and construct a real-time health knowledge influence matrix;

[0172] A reward distribution unit, which is used to determine reward content information according to the real-time health knowledge influence matrix, combine the unique authentication identification information and feature coding information in the user registration information, and distribute reward information to the user after upgrading the reward content.

[0173] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for managing a health knowledge social platform based on blockchain, characterized in that: include: S100, receiving and storing user registration information and user authorization information, and then determining a user basic information matrix based on the user registration information, and determining a user basic real index in combination with the user authorization information; S200, based on the user's basic real index, initially encrypt the user's registration information, and generate unique authentication identification information based on the user's registration information; S300, based on the unique authentication identification information, determine the health knowledge data released by the user, and conduct a system audit on the health knowledge data released by the user to obtain a system audit basic information matrix; S400, judging whether to publish the health knowledge data published by the user for the first time according to the system review basic information matrix, if not, shielding the health knowledge data published by the user and sending the system review result to the user, if yes, publishing the health knowledge data published by the user for the first time and sending the system improvement suggestions to the user; S500, collecting user interaction information corresponding to health knowledge data released by users for the first time, and obtaining a health knowledge influence matrix; S600, based on the health knowledge impact matrix and the health knowledge data released by the user, professionally review the health knowledge data released by the user after the initial release, and send the professional review results and professional improvement suggestions to the user; S700, real-time user interaction information corresponding to the health knowledge data released by the user is obtained in real time, a real-time health knowledge influence matrix is ​​obtained, and reward information is issued to the user based on the real-time health knowledge influence matrix and user registration information.

2. According to claim 1, a method for managing a health knowledge social platform based on blockchain is characterized in that: The receiving and storing of user registration information and user authorization information, and then determining a user basic information matrix based on the user registration information, and determining a user basic real index in combination with the user authorization information, specifically includes: Based on the user registration information, obtaining the user registration timestamp information, wherein the user registration information includes obtaining the user age information, the user nickname information, the email address information and the initial password information; Based on one-hot encoding, the user registration information is processed to generate user feature encoding information, and the user feature encoding information is stored in the blockchain; According to the user age information, the user nickname information and the user feature coding information, the first element, the second element and the third element of the user basic information matrix are determined, and the user basic information matrix is ​​constructed; Based on user nickname information and user registration information, search on external health knowledge social platforms to obtain heterogeneous user publishing data; Based on user authorization information and user registration information, the user's heterogeneous published data is screened to obtain the user's heterogeneous health knowledge data; Processing the user's heterogeneous health knowledge data to obtain the user's heterogeneous health knowledge video data, the user's heterogeneous health knowledge audio data, and the user's heterogeneous health knowledge text data, and sequentially obtaining heterogeneous user interaction information corresponding to the user's heterogeneous health knowledge video data, the user's heterogeneous health knowledge audio data, and the user's heterogeneous health knowledge text data; Based on the audio extraction technology, the audio data in the user's heterogeneous health knowledge video data is extracted, and the extracted audio data and the user's heterogeneous health knowledge audio data are converted into text information using the speech recognition technology to obtain the first text information and the second text information; Based on natural language processing technology, semantic segmentation is performed on the first text information, the second text information and the user's heterogeneous health knowledge text data to obtain the first text sub-information, the second text sub-information and the third text sub-information; According to the heterogeneous user interaction information, first heterogeneous user comment information, second heterogeneous user comment information, and third heterogeneous user comment information corresponding to the first text sub-information, the second text sub-information, and the third text sub-information are respectively obtained; Performing semantic segmentation on the first heterogeneous user comment information, the second heterogeneous user comment information, and the third heterogeneous user comment information to obtain fourth text sub-information, fifth text sub-information, and sixth text sub-information; Respectively obtaining a first matching degree, a second matching degree, and a third matching degree of the first text sub-information and the fourth text sub-information, the second text sub-information and the fifth text sub-information, and the third text sub-information and the sixth text sub-information; Obtaining a first degree of expertise, a second degree of expertise, and a third degree of expertise of the first text sub-information, the second text sub-information, and the third text sub-information, respectively; Quantize the first element, the second element, and the third element in the user basic information matrix to obtain a user basic information quantization matrix; Based on the user basic information quantification matrix, the first matching degree, the second matching degree, the third matching degree, the first professional degree, the second professional degree and the third professional degree, a user basic real index is obtained; The calculation formula of the user base real index is: Where U true is the user basic real index, M1, M2 and M3 are the first matching degree, the second matching degree and the third matching degree respectively, P1, P2 and P3 are the first professional degree, the second professional degree and the third professional degree respectively, R is the user basic information quantification matrix, A text1 , A text2 , A text3 , A text4 , A text5 and A text6 are the number of phrases with semantic relationship in the first to sixth text sub-information, C text14 , C text25 and C text36 are the same number of phrases with semantic relationship in the fourth to sixth text sub-information as the phrases with semantic relationship in the first to third text sub-information, Z text1 , Z text2 and Z text3 are the number of professional terms in the first to third text sub-information, X1, X2 and X3 are the first to third elements in the user basic information quantization matrix, R T It is the transposed matrix of the user basic information quantization matrix.

3. A method for managing a health knowledge social platform based on blockchain according to claim 2, characterized in that: The method of performing initial encryption on the user registration information based on the user basic real index and generating unique authentication identification information based on the user registration information specifically includes: Set the encryption level and determine the encryption level based on the user base real index; According to the encryption level, an encryption library is selected, and the user registration information is packaged based on the encryption library to complete the initial encryption, and the encryption certificate is obtained and stored in the blockchain; Based on the email information and initial password information, the encrypted certificate stored in the blockchain is repackaged; According to the user registration information, set prompt information during the secondary packaging process; Based on the user registration information, determine the user's age information and the user's nickname information, and generate the first authentication identification information, and at the same time obtain the user's initial image information in the user registration information, and then generate the second authentication identification information according to the user's initial image information; According to the user registration information, the corresponding user basic real index is obtained, and the corresponding user feature coding information is determined; Unique authentication identification information is generated based on the first authentication identification information, the second authentication identification information and the user feature coding information.

4. A method for managing a health knowledge social platform based on blockchain according to claim 3, characterized in that: Based on the unique authentication identification information, the health knowledge data released by the user is determined, and the health knowledge data released by the user is systematically audited to obtain the system audit basic information matrix, which specifically includes: Determine user feature coding information based on the unique authentication identification information; Encode information with user characteristics, determine the exclusive data storage space corresponding to the user in the blockchain, and obtain the corresponding user-published health knowledge data; Extract metadata from health knowledge data published by users to obtain submission time information, data format information, and estimated reading time information; Based on the submission time information, obtain all the health knowledge data to be published submitted by users within one month; Obtaining data type information of all health knowledge data to be published, and classifying the health knowledge data to be published based on the data type information to obtain health knowledge sub-data to be published; Sequentially obtain text description information, picture information, and video information of the health knowledge sub-data to be published to obtain a plurality of first comparison information; Obtain text description information, picture information and video information from the health knowledge data published by users to obtain basic comparison information; Comparing the basic comparison information with the plurality of first comparison information in sequence to obtain a repetition index; Based on natural language processing technology, sensitive word analysis is performed on basic comparative information, and the sensitivity index is obtained based on the sensitive word analysis results; Based on the repetition index, sensitivity index, data format information and estimated reading time information corresponding to the health knowledge data published by users, a systematic review of the health knowledge data published by users is conducted, and a basic information matrix for the system review is established.

5. According to claim 4, a method for managing a health knowledge social platform based on blockchain is characterized in that: The collecting of user interaction information corresponding to the health knowledge data released by the user for the first time and obtaining the health knowledge influence matrix specifically includes: Setting a collection time period, and collecting user interaction information corresponding to health knowledge data released by users for the first time based on the collection time period; According to user interaction information, obtain user interactive comment information, user like information, user collection information, user forwarding information and user citation information; Based on natural language processing technology, analyze user interactive comment information and construct a word frequency bar chart based on the analysis results; A word frequency threshold is set based on the word frequency bar graph, and words with a frequency higher than the word frequency threshold are integrated to obtain hot words in user comments; Conduct sentiment analysis on hot words in user comments to obtain the sentiment index of user comments and determine the first influencing factor; Based on the user like information, user collection information and user forwarding information, the number of user likes, the number of user collections and the number of user forwardings are weighted and summed to obtain the second influencing element; Obtain the user-published health knowledge data corresponding to the user reference information, obtain the referenced health knowledge data, and obtain the user interaction information corresponding to the referenced health knowledge data, and obtain the third influencing element; The health knowledge impact matrix is ​​constructed through the first impact element, the second impact element and the third impact element.

6. A method for managing a health knowledge social platform based on blockchain according to claim 5, characterized in that: The real-time acquisition of real-time user interaction information corresponding to the health knowledge data released by the user, obtaining a real-time health knowledge influence matrix, and issuing reward information to the user based on the real-time health knowledge influence matrix and user registration information specifically includes: Based on the real-time user interaction information, obtaining a first real-time influencing element, a second real-time influencing element, and a third real-time influencing element; The first to third real-time impact elements are used as the first to third elements of the real-time health knowledge impact matrix, and the health knowledge impact matrix and the real-time health knowledge impact matrix are both 1×3 row matrices; Based on the real-time health knowledge influence matrix, determine the reward content information, and determine the unique authentication identification information through the user registration information, and determine the user feature coding information; According to the unique authentication identification information, the reward content information is upgraded to obtain the upgraded reward content information; Based on the upgrade reward content information and user feature coding information, reward information is issued to the user.

7. A health knowledge social platform management system based on blockchain, used to implement the management method according to any one of claims 1 to 6, characterized in that: include: A user management module, which is used to receive and store user registration information and user authorization information, perform data processing on the user registration information based on unique hot coding, generate user feature coding information and store it in the blockchain, obtain user registration timestamp information at the same time, build a user basic information matrix based on user age, nickname and feature coding information, retrieve user heterogeneous publishing data on an external platform based on user nickname and registration information, filter out user heterogeneous health knowledge data in combination with authorization information, obtain different types of heterogeneous data and corresponding interactive information after processing, calculate the user basic real index through a series of semantic analysis, determine the encryption level according to the user basic real index, select an encryption library to perform initial encryption on the user registration information and obtain an encryption certificate to store in the blockchain, then repackage the encryption certificate based on the email address and initial password, set prompt information at the same time, and generate unique authentication identification information using user age, nickname, initial image and feature coding information; The knowledge management module is used to determine the user feature code based on the unique authentication identification information, find the user's exclusive data storage space in the blockchain, obtain the health knowledge data released by the user, obtain and classify the data to be released within one month, obtain the repetition index by comparing the basic comparison information with multiple first comparison information, perform sensitive word analysis on the basic comparison information to obtain the sensitivity index, complete the system review in combination with the data format and estimated reading time information, establish the system review basic information matrix, determine whether the knowledge data is released for the first time, if it fails, block the data and inform the user of the review result, if it passes, release it and give improvement suggestions, set the collection time week During the period, user interaction information corresponding to the health knowledge data released by users for the first time was collected, including comments, likes, favorites, forwarding and citation information. The user interaction comment information was analyzed to construct a word frequency bar chart. The word frequency threshold was set to obtain the hot words in user comments. The sentiment analysis was performed to obtain the user comment sentiment index to determine the first influencing element. The weighted sum of the number of likes, favorites and forwarding was used to obtain the second influencing element. The knowledge data and interaction information corresponding to the citation information were obtained to determine the third influencing element. A health knowledge influence matrix was constructed. Professional review was conducted based on the health knowledge influence matrix and the health knowledge data released by users, and professional review results and professional improvement suggestions were sent to users. An incentive management module, which is used to obtain real-time user interaction information corresponding to the health knowledge data released by the user, obtain the first to third real-time influence elements of the real-time health knowledge influence matrix, construct the real-time health knowledge influence matrix, determine the reward content information based on the real-time health knowledge influence matrix, and distribute reward information to the user after the reward content is upgraded in combination with the unique authentication identification information and feature coding information in the user registration information; Data management module, which is used to store various types of data such as user feature coding information, encryption certificates, and user-published health knowledge data in the blockchain to ensure the security and non-tamperability of the data, and assist other modules in performing various types of data analysis. For example, in the user management module, the user's heterogeneous health knowledge data is analyzed to determine the user's basic real index, and in the knowledge management module, the duplication and sensitivity of the user's published health knowledge data are analyzed, and in the incentive management module, the real-time user interaction information is assisted in the analysis to determine the real-time health knowledge impact matrix.

8. A blockchain-based health knowledge social platform management system according to claim 7, characterized in that: The user management module comprises: A registration information processing unit, which is used to receive and store user registration information and user authorization information, perform data processing on the user registration information based on one-hot encoding, generate user feature encoding information and store it in the blockchain, and obtain user registration timestamp information at the same time; A basic information determination unit, the basic information determination unit is used to construct a user basic information matrix according to the user's age, nickname and feature coding information, retrieve the user's heterogeneous published data on the external platform based on the user's nickname and registration information, filter out the user's heterogeneous health knowledge data in combination with the authorization information, obtain different types of heterogeneous data and corresponding interactive information after processing, and calculate the user's basic real index through a series of semantic analysis; The encryption authentication unit is used to determine the encryption level according to the user's basic authenticity index, select the encryption library to perform the initial encryption on the user's registration information and obtain the encryption certificate to store it in the blockchain, and then repackage the encryption certificate based on the email address and initial password, and set prompt information at the same time, and use the user's age, nickname, initial image and feature coding information to generate unique authentication identification information.

9. A blockchain-based health knowledge social platform management system according to claim 7, characterized in that: The knowledge management module includes: A knowledge release review unit, which is used to determine the user feature code based on the unique authentication identification information, find the user's exclusive data storage space in the blockchain, obtain the user's health knowledge data, obtain and classify the data to be released within one month, obtain the repetition index by comparing the basic comparison information with multiple first comparison information, perform sensitive word analysis on the basic comparison information to obtain the sensitivity index, complete the system review in combination with the data format and the estimated reading time information, establish the system review basic information matrix, determine whether the knowledge data is released for the first time, if not, block the data and inform the user of the review result, if passed, release and give improvement suggestions; An interactive information collection unit, the interactive information collection unit is used to set a collection time period, collect user interactive information corresponding to the health knowledge data released by the user for the first time, including comments, likes, collections, forwarding and citation information, analyze the user interactive comment information to construct a word frequency bar chart, set a word frequency threshold to obtain hot words in user comments, perform sentiment analysis to obtain the user comment sentiment index to determine the first influencing element, perform weighted summation of the number of likes, collections and forwarding to obtain the second influencing element, obtain the knowledge data and interactive information corresponding to the citation information to determine the third influencing element, and construct a health knowledge influence matrix; A professional review unit is used to perform professional review based on the health knowledge impact matrix and the health knowledge data published by the user, and send professional review results and professional improvement suggestions to the user.

10. A blockchain-based health knowledge social platform management system according to claim 7, characterized in that: The incentive management module includes: A real-time influence acquisition unit, which is used to acquire real-time user interaction information corresponding to the health knowledge data released by the user in real time, obtain the first to third real-time influence elements of the real-time health knowledge influence matrix, and construct the real-time health knowledge influence matrix; The reward issuing unit is used to determine the reward content information based on the real-time health knowledge influence matrix, and to issue reward information to the user after upgrading the reward content in combination with the unique authentication identification information and feature coding information in the user registration information.

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