A blockchain-based health knowledge social platform management method and system

Abstract

The application discloses a kind of based on health knowledge social platform management method and system of blockchain, it is related to social platform management technical field, including: in management method level, initially receive and store user registration, authorization information, with one-hot coding to build user basic information matrix, calculate real index, in this way encrypt registration information, generate unique authentication identification.Subsequently, health knowledge data of user release is locked with identification, system audits its repeatability, sensitivity and other elements, determines whether can be initially published, after publishing, interactive information is collected to form influence matrix, and professional audit is carried out.At the same time, real-time collection interactive information generates real-time influence matrix, and combines registration information to reward user.The system adapted thereto contains user, knowledge, incentive, data management module, and each module subdivides unit division of labour, and exerts force from multiple links, and improves platform management efficiency in all directions, and guarantees knowledge quality and user experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of social platform management, in particular to a health knowledge social platform management method and system based on blockchain. BACKGROUND

[0002] At present, people pay more and more attention to health, and a large amount of health knowledge is flooding the network, but the quality is uneven. The health knowledge social platform management method and system based on blockchain emerge as the times require. It uses the decentralized and tamper-proof characteristics of blockchain to ensure 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 filters and audits health knowledge, promotes the effective dissemination of knowledge, helps the public to improve health literacy, and creates a credible ecology of health knowledge sharing.

[0003] Because a large amount of health knowledge is flooding the network and the quality is uneven, a large number of users will register on the health knowledge social platform. The existing health knowledge social platform management method and system based on blockchain cannot accurately determine the authenticity of user information, cannot ensure the security of user information, and cannot identify the authenticity of a large amount of health knowledge. Therefore, a health knowledge social platform management method and system based on blockchain is needed to solve the above problems. SUMMARY

[0004] To solve the above technical problems, a health knowledge social platform management method and system based on blockchain are provided. The technical solution solves the problem that the existing health knowledge social platform management method and system based on blockchain cannot accurately determine the authenticity of user information, cannot ensure the security of user information, and cannot identify the authenticity of a large amount of health knowledge.

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

[0006] A health knowledge social platform management method based on blockchain, comprising:

[0007] S100, receiving and storing user registration information and user authorization information, then determining a user basic information matrix based on the user registration information, and determining a user basic authenticity index in combination with the user authorization information;

[0008] S200, based on the user basic authenticity index, initially encrypting the user registration information, and generating unique authentication identification information based on the user registration information;

[0009] S300, based on the unique authentication identification information, determining user published health knowledge data, and performing system audit on the user published health knowledge data to obtain a system audit basic information matrix;

[0010] S400, judging whether to perform initial publishing on the health knowledge data published by the user according to the system audit basic information matrix, if not, shielding the health knowledge data published by the user, and sending the system audit result to the user, if yes, performing initial publishing on the health knowledge data published by the user, and sending the system improvement suggestion to the user;

[0011] S500, collecting user interaction information corresponding to the health knowledge data published by the user, and obtaining a health knowledge influence matrix;

[0012] S600, performing professional audit on the health knowledge data published by the user after initial publishing based on the health knowledge influence matrix and the health knowledge data published by the user, and sending the professional audit result and the professional improvement suggestion to the user;

[0013] S700, obtaining real-time user interaction information corresponding to the health knowledge data published by the user in real time, 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 the user registration information.

[0014] In an optional embodiment, the user registration information and the user authorization information are received and stored, and then the user basic information matrix is determined based on the user registration information, and the user basic real index is determined in combination with the user authorization information, specifically including:

[0015] Based on the user registration information, user registration timestamp information is obtained, and the user registration information includes obtaining user age information, user nickname information, electronic mail information and initial password information;

[0016] Based on the 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;

[0017] According to the user age information, the user nickname information and the user feature encoding information, a first element, a second element and a third element of the user basic information matrix are determined, and the user basic information matrix is constructed;

[0018] Based on the user nickname information and the user registration information, the user heterogeneous publishing data is obtained by searching in an external health knowledge social platform;

[0019] Based on the user authorization information and the user registration information, the user heterogeneous publishing data is screened to obtain user heterogeneous health knowledge data;

[0020] The user heterogeneous health knowledge data is processed to obtain user heterogeneous health knowledge video data, user heterogeneous health knowledge audio data and user heterogeneous health knowledge text data, and heterogeneous user interaction information corresponding to the user heterogeneous health knowledge video data, the user heterogeneous health knowledge audio data and the user heterogeneous health knowledge text data is obtained in sequence;

[0021] Based on the audio extraction technology, the audio data in the user heterogeneous health knowledge video data is extracted, and the extracted audio data and the user heterogeneous health knowledge audio data are converted into text information by using the speech recognition technology to obtain first text information and second text information;

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

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

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

[0025] 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 respectively obtained;

[0026] The first professional degree, the second professional degree and the third professional degree of the first text sub-information, the second text sub-information and the third text sub-information are respectively obtained;

[0027] The first element, the second element and the third element in the user basic information matrix are quantized to obtain a user basic information quantization matrix;

[0028] Based on the user basic information quantization 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, the user basic real index is obtained;

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

[0030]

[0031] In the formula, U trueThe user base authenticity index is represented by M1, M2, and M3, which are the first, second, and third matching degrees, respectively. P1, P2, and P3 are the first, second, and third professional levels, respectively. R is the user base information quantification matrix. A text1 A text2 A text3 A text4 A text5 and A text6 C represents the number of phrases with semantic relationships in the first to sixth text sub-information, respectively. text14 C text25 and C text36 Z represents the number of phrases with semantic relationships in the fourth to sixth text sub-information that are the same as the number of phrases with semantic relationships in the first to third text sub-information. text1 Z text2 and Z text3 X1, X2, and X3 represent the number of technical terms in the first to third text sub-information, respectively, and X1, X2, and X3 are the first to third elements in the user basic information quantification matrix, respectively. T The transpose of the quantization matrix for user basic information.

[0032] In an optional embodiment, the initial encryption of user registration information based on the user's basic authenticity index, and the generation of unique authentication identifier information based on the user registration information, specifically includes:

[0033] Set the encryption level and determine the encryption level based on the user's basic real index;

[0034] Based on 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. At the same time, the encryption certificate is obtained and stored in the blockchain.

[0035] The encrypted certificate stored on the blockchain is repackaged based on email address information and initial password information;

[0036] Based on user registration information, set prompt messages during the secondary packaging process;

[0037] Based on user registration information, determine user age information and user nickname information, and generate first authentication identifier information. At the same time, obtain user initial image information from user registration information, and then generate second authentication identifier information based on user initial image information.

[0038] Based on user registration information, obtain the corresponding user base authenticity index and determine the corresponding user feature coding information;

[0039] A unique authentication identifier is generated based on the first authentication identifier information, the second authentication identifier information, and the user feature encoding information.

[0040] In an optional embodiment, the determining the user to publish health knowledge data based on the unique authentication identification information, and the system auditing the user to publish health knowledge data, obtaining the system auditing basic information matrix, specifically comprising:

[0041] Based on the unique authentication identification information, the user feature code information is determined;

[0042] With the user feature code information, the user's corresponding exclusive data storage space is determined in the blockchain, and the corresponding user to publish health knowledge data is obtained;

[0043] Extracting the metadata in the user to publish health knowledge data obtains the submission time information, data format information and estimated reading time information;

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

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

[0046] Obtaining the text description information, picture information and video information of the health knowledge data to be published in turn, obtaining a plurality of first comparison information;

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

[0048] Comparing the basic comparison information with the plurality of first comparison information in turn, obtaining the repetition index;

[0049] Based on the natural language processing technology, the sensitive word analysis is carried out on the basic comparison information, and the sensitive degree index is obtained according to the sensitive word analysis result;

[0050] According to the repetition index, the sensitive degree index, the data format information and the estimated reading time information corresponding to the user to publish health knowledge data, the system audits the user to publish health knowledge data, and establishes the system auditing basic information matrix.

[0051] In an optional embodiment, the user interaction information corresponding to the user to publish health knowledge data is collected, and the health knowledge influence matrix is obtained, specifically comprising:

[0052] Setting the collection time period, and collecting the user interaction information corresponding to the user to publish health knowledge data based on the collection time period;

[0053] According to the user interaction information, user interaction comment information, user like information, user collection information, user forwarding information and user reference information are obtained;

[0054] Based on natural language processing technology, the user interaction comment information is analyzed, and a word frequency column chart is constructed according to the analysis result;

[0055] Based on the word frequency column chart, a word frequency threshold is set, and words with a word frequency higher than the word frequency threshold are integrated to obtain user comment hot words;

[0056] The user comment hot words are subjected to sentiment analysis to obtain a user comment sentiment index, and a first influence element is determined;

[0057] Based on the user like information, the user collection information and the user forwarding information, the user like number, the user collection number and the user forwarding number are weighted and summed to obtain a second influence element;

[0058] The user reference information corresponding to the user published health knowledge data is obtained to obtain reference health knowledge data, and the user interaction information corresponding to the reference health knowledge data is obtained to obtain a third influence element;

[0059] A health knowledge influence matrix is constructed through the first influence element, the second influence element and the third influence 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 including:

[0061] Based on the real-time user interaction information, a first real-time influence element, a second real-time influence element and a third real-time influence element are obtained;

[0062] The first to third real-time influence elements are used as the first to third elements of the real-time health knowledge influence matrix, and the health knowledge influence matrix and the real-time health knowledge influence matrix are both 1x3 row matrices;

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

[0064] According to the unique authentication identification information, the reward content information is upgraded to obtain upgraded reward content information;

[0065] Based on the upgraded reward content information and the user feature coding information, reward information is issued to the user.

[0066] Further, a health knowledge social platform management system based on a blockchain is proposed, which is used to implement the management method of any one of the above, comprising:

[0067] A user management module is configured 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, obtain user registration timestamp information, construct a user basic information matrix according to user age, nickname and feature encoding information, retrieve user heterogeneous published data based on user nickname and registration information on an external platform, filter out user heterogeneous health knowledge data combined with authorization information, obtain different types of heterogeneous data and corresponding interaction information after processing, calculate a user basic real index through a series of semantic analysis, determine an 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 stored in the blockchain, perform secondary packaging of the encryption certificate based on an email and an initial password, and set a prompt information, generate a unique authentication identification information using user age, nickname, initial image and feature encoding information;

[0068] A knowledge management module is configured to determine user feature encoding based on the unique authentication identification information, find a user's exclusive data storage space in the blockchain, obtain user published health knowledge data, obtain data to be published within a month and classify them, obtain a repetition index by comparing the basic comparison information with a plurality of first comparison information, obtain a sensitivity index by performing sensitive word analysis on the basic comparison information, complete system audit combined with data format and estimated reading time information, establish a system audit basic information matrix, determine whether to initially publish knowledge data, if not, shield the data and inform the user of the audit result, if yes, publish and give improvement suggestions, set a collection time period, collect user interaction information corresponding to the user published health knowledge data including comments, likes, collections, forwards and reference information, analyze the user interaction comment information to construct a word frequency column chart, set a word frequency threshold to obtain user comment hot words, perform sentiment analysis to obtain a user comment sentiment index to determine a first influence element, weight and sum the number of likes, collections and forwards to obtain a second influence element, obtain knowledge data and interaction information corresponding to the reference information to determine a third influence element, construct a health knowledge influence matrix, perform professional audit based on the health knowledge influence matrix and the user published health knowledge data, and send the professional audit result and professional improvement suggestions to the user;

[0069] The incentive management module is used for obtaining real-time user interaction information corresponding to user published health knowledge data in real time, obtaining first to third real-time influence elements of a real-time health knowledge influence matrix, constructing the real-time health knowledge influence matrix, determining reward content information according to the real-time health knowledge influence matrix, and issuing reward information to the user after upgrading the reward content in combination with unique authentication identification information and feature coding information in user registration information;

[0070] The data management module is used for storing user feature coding information, encrypted certificates, user published health knowledge data and various types of data to a block chain, ensuring the safety and non-tamperability of the data, and assisting other modules in various types of data analysis, such as analyzing user heterogeneous health knowledge data in the user management module to determine a user basic authenticity index, analyzing the repetition, sensitivity and the like of user published health knowledge data in the knowledge management module, and assisting in analyzing real-time user interaction information in the incentive management module to determine a real-time health knowledge influence matrix.

[0071] In an optional embodiment, the user management module comprises:

[0072] The registration information processing unit is used for receiving and storing user registration information and user authorization information, performing data processing on the user registration information based on one-hot coding, generating user feature coding information and storing the user feature coding information to a block chain, and simultaneously obtaining user registration timestamp information.

[0073] The basic information determination unit is used for constructing a user basic information matrix according to user age, nickname and feature coding information, retrieving user heterogeneous published data on an external platform based on the user nickname and registration information, screening out user heterogeneous health knowledge data in combination with authorization information, obtaining different types of heterogeneous data and corresponding interaction information after processing, and obtaining a user basic authenticity index through a series of semantic analysis calculations.

[0074] The encryption authentication unit is used for determining an encryption level according to the user basic authenticity index, selecting an encryption library to perform initial encryption on the user registration information and obtain encrypted certificates stored to a block chain, performing secondary packaging on the encrypted certificates based on an electronic mailbox and an initial password, simultaneously setting prompt information, and generating unique authentication identification information by using user age, nickname, initial images and feature coding information.

[0075] In an optional embodiment, the knowledge management module comprises:

[0076] The knowledge publishing auditing unit is used for determining a user feature code based on unique authentication identification information, finding a user's exclusive data storage space in a blockchain, obtaining user published health knowledge data, obtaining data to be published within one month and classifying the data, obtaining a repetition index by comparing basic comparison information with a plurality of first comparison information, obtaining a sensitivity index by performing sensitive word analysis on the basic comparison information, completing system auditing in combination with data format and estimated reading time information, establishing a system auditing basic information matrix, judging whether the knowledge data is published for the first time, shielding the data and informing the user of the auditing result if the knowledge data is not published for the first time, publishing the data and giving improvement suggestions if the knowledge data is published for the first time;

[0077] The interactive information collecting unit is used for setting a collection time period, collecting user interactive information corresponding to the user published health knowledge data published for the first time, including comment, like, collection, forwarding and reference information, analyzing the user interactive comment information to construct a word frequency column chart, setting a word frequency threshold to obtain user comment hot words, performing sentiment analysis to obtain a user comment sentiment index to determine a first influence element, performing weighted summation on the like, collection and forwarding quantity to obtain a second influence element, obtaining knowledge data and interactive information corresponding to the reference information to determine a third influence element, and constructing a health knowledge influence matrix.

[0078] The professional auditing unit is used for performing professional auditing based on the health knowledge influence matrix and the user published health knowledge data, and sending professional auditing results and professional improvement suggestions to the user.

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

[0080] The real-time influence obtaining unit is used for obtaining real-time user interactive information corresponding to the user published health knowledge data in real time, obtaining first to third real-time influence elements of a real-time health knowledge influence matrix, and constructing the real-time health knowledge influence matrix.

[0081] The reward issuing unit is used for determining reward content information according to the real-time health knowledge influence matrix, issuing reward information to the user after upgrading the reward content in combination with unique authentication identification information and feature code information in user registration information.

[0082] Compared with the prior art, the present application has the following beneficial effects:

[0083] The scheme provides a health knowledge social platform management method and system based on a blockchain. The user basic real index is determined through multi-dimensional analysis, and the user registration information is encrypted based on the user basic real index. A unique authentication identifier is generated to ensure that the user information is real, reliable and securely stored in the blockchain, preventing information leakage and tampering. The health knowledge data published by the user is subjected to system audit and professional audit, and is evaluated from aspects such as repeatability and sensitivity to filter low-quality and false information and improve the knowledge quality of the platform. An influence matrix is constructed by collecting user interaction information to quantify the knowledge dissemination effect, such as user comment sentiment, likes, collections and forwarding, to provide a basis for knowledge optimization and dissemination, make up for the lack of evaluation of the knowledge dissemination effect of the existing platform, and determine the reward content in real time by acquiring real-time interaction information of the user, and upgrade the reward by combining the user identifier and the feature code to stimulate the enthusiasm of the user in participating in and creating high-quality content and improve the single or lack of targeted condition of the existing platform incentive mechanism. BRIEF DESCRIPTION OF DRAWINGS

[0084] Figure 1 A flowchart of a health knowledge social platform management method based on a blockchain is provided for the present application.

[0085] Figure 2 And Figure 3 The combination is the acquisition flowchart of the user basic real index in the present application.

[0086] Figure 4 A system framework diagram of a health knowledge social platform management system based on a blockchain is provided for the present application. DETAILED DESCRIPTION

[0087] The following description is used to disclose the present application so that those skilled in the art can implement the present application. The preferred embodiments in the following description are only as examples, and other obvious modifications can be made by those skilled in the art.

[0088] Referring to Figure 1 - Figure 4 As shown in the figure, a health knowledge social platform management method based on a blockchain comprises:

[0089] 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;

[0090] S200, based on the user basic real index, the user registration information is initially encrypted, and based on the user registration information, a unique authentication identifier information is generated;

[0091] S300, based on the unique authentication identifier information, the user publishes health knowledge data, and the user publishes health knowledge data is subjected to system audit to obtain a system audit basic information matrix;

[0092] S400, judging whether to perform initial publishing on the health knowledge data published by the user according to the system audit basis information matrix, if not, shielding the health knowledge data published by the user, and sending the system audit result to the user, if yes, performing initial publishing on the health knowledge data published by the user, and sending the system improvement suggestion to the user;

[0093] S500, collecting user interaction information corresponding to the health knowledge data published by the user, and obtaining a health knowledge influence matrix;

[0094] S600, performing professional audit on the health knowledge data published by the user after initial publishing based on the health knowledge influence matrix and the health knowledge data published by the user, and sending the professional audit result and the professional improvement suggestion to the user;

[0095] S700, obtaining real-time user interaction information corresponding to the health knowledge data published by the user in real time, 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 the user registration information.

[0096] In particular, the user registration information includes user age information, user nickname information, etc. The user authorization information is used to filter the user heterogeneous publishing data obtained from the external health knowledge social platform to obtain user heterogeneous health knowledge data, and assist in determining the user basic real index. The user heterogeneous publishing data is obtained by searching based on the user nickname information and the user registration information on the external health knowledge social platform. After filtering processing, it is used to analyze the user's health knowledge related data performance on other platforms, and provides a reference for determining the user basic real index. The user heterogeneous health knowledge data is filtered from the user heterogeneous publishing data, and is 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 basic real index by analyzing the corresponding heterogeneous user interaction information. The user publishes health knowledge data, which 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 time information, as well as text description information, picture information and video information. The system audits, publishes and manages it, and evaluates its influence according to the user interaction information. The to-be-published health knowledge data is all the health knowledge data submitted by the user within a month and waiting to be published. By classifying and comparing, it assists in judging the repetition degree of the user published health knowledge data for system audit. Real-time user interaction information, real-time user interaction content corresponding to the user published health knowledge data, is used to construct a real-time health knowledge influence matrix to determine the reward information for the user. User feature encoding information is generated by data processing of user registration information through one-hot encoding and stored in a blockchain, which is used to identify user features and plays a key role in determining unique authentication identifier information and finding user exclusive data storage space. Encryption certificate, obtained after the initial encryption of user registration information based on a selected encryption library, is stored in a blockchain and used to prove the encryption status and security of user registration information. Prompt information, based on user registration information, is set during the secondary packaging of the encryption certificate and may be used 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 (first to third text information) converted from the user heterogeneous health knowledge data and its corresponding heterogeneous user comment information. It is used to calculate the matching degree, professional degree, etc. to determine the user basic real index. The first to third comparison information is 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, and the repetition index is obtained by comparison to assist the system in auditing the user published health knowledge data. The user basic real index is calculated by integrating the user basic information quantitative matrix, the matching degree and the professional degree 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 repetition index is obtained by comparing the basic comparison information of the health knowledge data published by the user with the plurality of first comparison information of the health knowledge sub-data to be published, and is used for the system to judge the repetition of the health knowledge data published by the user. The sensitivity index is obtained based on the natural language processing technology to analyze the sensitive words of the basic comparison information of the health knowledge data published by the user, and is used for the system to judge the sensitivity of the health knowledge data published by the user. The user comment sentiment index is obtained by performing sentiment analysis on the user comment hot words, and is used as the first influence element of the health knowledge influence matrix to reflect the emotional tendency of the user to the health knowledge data published for the first time. The user basic information matrix is constructed according to the first element, the second element and the third element determined according to the user age information, the user nickname information and the user feature coding information, and is a form of quantifying the user basic information, and is used to calculate the user basic real index. The system audit basic information matrix is established in combination with the repetition index, the sensitivity index, the data format information and the estimated reading time information of the health knowledge data published by the user, and is used to judge whether to publish the health knowledge data published by the user for the first time. The health knowledge influence matrix is constructed by the first influence element (determined based on the user comment sentiment index), the second influence element (the weighted sum of the number of user likes, the number of user collections and the number of user forwards) and the third influence element (related data corresponding to the user reference information), and is used to evaluate the influence of the health knowledge data published by the user for the first time, and is used for professional audit based on the influence. The real-time health knowledge influence matrix is constructed by taking the first to third real-time influence elements obtained based on real-time user interaction information as the first to third elements, and is used to determine the reward content information for the user.

[0097] Further, the user registration information and the user authorization information are received and stored, and then the user basic information matrix is determined based on the user registration information, and the user basic real index is determined in combination with the user authorization information, specifically including:

[0098] Based on the user registration information, the user registration timestamp information is obtained, and the user registration information includes obtaining the user age information, the user nickname information, the electronic mail information and the initial password information;

[0099] Based on one-hot coding, the user registration information is processed to generate user feature coding information, and the user feature coding information is stored in the blockchain;

[0100] 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;

[0101] Based on the user nickname information and the user registration information, the user heterogeneous published data is obtained by searching in the external health knowledge social platform;

[0102] Based on the user authorization information and the user registration information, the user heterogeneous published data is screened to obtain user heterogeneous health knowledge data;

[0103] The user heterogeneous health knowledge data is processed to obtain user heterogeneous health knowledge video data, user heterogeneous health knowledge audio data and user heterogeneous health knowledge text data, and the corresponding heterogeneous user interaction information of the user heterogeneous health knowledge video data, the user heterogeneous health knowledge audio data and the user heterogeneous health knowledge text data is obtained in sequence;

[0104] Based on the audio extraction technology, the audio data in the user heterogeneous health knowledge video data is extracted, and the extracted audio data and the user heterogeneous health knowledge audio data are converted into text information by using the voice recognition technology to obtain first text information and second text information;

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

[0106] According to the heterogeneous user interaction information, the first text sub-information, the second text sub-information and the third text sub-information are respectively obtained to 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] The first heterogeneous user comment information, the second heterogeneous user comment information and the third heterogeneous user comment information are subjected to semantic segmentation to obtain fourth text sub-information, fifth text sub-information and sixth text sub-information;

[0108] 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 respectively obtained;

[0109] The first professional degree, the second professional degree and the third professional degree of the first text sub-information, the second text sub-information and the third text sub-information are respectively obtained;

[0110] The first element, the second element and the third element in the user basic information matrix are quantized to obtain a 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 professional degree, the second professional degree and the third professional degree, the user basic real index is obtained;

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

[0113]

[0114] In the formula, U true is a user base real index, M1, M2 and M3 are respectively a first matching degree, a second matching degree and a third matching degree, P1, P2 and P3 are respectively a first professional degree, a second professional degree and a third professional degree, R is a user base information quantization matrix, A text1 , A text2 , A text3 , A text4 , A text5 and A text6 are respectively the number of phrases with semantic relationship in the first to sixth text sub-information, C text14 , C text25 and C text36 are respectively the number of phrases with semantic relationship in the fourth to sixth text sub-information that are the same as the number of phrases with semantic relationship in the first to third text sub-information, Z text1 , Z text2 and Z text3 are respectively the number of professional terms in the first to third text sub-information, X1, X2 and X3 are respectively the first to third elements in the user base information quantization matrix, R T is a 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 user registration information is processed using one-hot encoding to convert it into user feature encoding information. One-hot encoding is a method of converting categorical variables into a form that is easy for machine learning algorithms to process, such as converting category information in user registration information (such as different age ranges, different nickname types, etc.) into a binary vector. The generated user feature encoding information is stored in the blockchain, and the non-tamperable characteristics of the blockchain are used to ensure the security and traceability of the data. Combined with user authorization information and user registration information, user heterogeneous published data is screened to obtain user heterogeneous health knowledge data. The data related to health knowledge is screened out, and other irrelevant information is excluded to ensure the pertinence of subsequent analysis.

[0116] It can be understood 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 correlation between the health knowledge information published by the user and the user's comments, such as whether the user's content is consistent with the expectation of the comments. The first professional degree, the second professional degree and the third professional degree of the first text sub-information, the second text sub-information and the third text sub-information are obtained respectively, and the professional degree can measure the professional level of the health knowledge information published by the user, for example, whether it contains professional terms, whether it conforms to the expression of professional knowledge, etc. The text description information, the picture information and the video information of the to-be-published health knowledge sub-data are obtained in turn to obtain a plurality of first comparison information, for example, different types of to-be-published health knowledge sub-data have different structures to obtain these information. The basic comparison information is compared with the plurality of first comparison information in turn to obtain a repetition index. Here, the text description information is compared by using a simple string similarity, and the difflib library is used to calculate the similarity. Based on the natural language processing technology, the sensitive word analysis is performed on the basic comparison information, and the sensitive degree index is obtained according to the sensitive word analysis result. The pymupdf library is used to process the text, and the sensitive words can be stored in a list sensitive_words.

[0117] Further, the user heterogeneous published data is specifically the health knowledge data published by the user and obtained by the management system from other health knowledge social platforms. However, when obtaining the data of other platforms, cooperation and competition relationships between platforms may be involved. Some platforms may not be willing to provide user data or set restrictions on the use of data. The platform can establish cooperation with other platforms, negotiate the mode and conditions of data sharing, and realize mutual benefit and win-win. At the same time, other legal ways to obtain data can also be explored, such as through third-party data service providers. In addition, a user submission module can also be set in the management system for users to provide fragments or all of the health knowledge data published on other platforms.

[0118] Further, based on the user basic real index, the user registration information is initially encrypted, and based on the user registration information, a unique authentication identification information is generated, specifically including:

[0119] An encryption level is set, and the encryption level is determined based on the user basic real index;

[0120] 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 an encryption certificate is obtained and stored to a block chain;

[0121] The encryption certificate stored to the block chain is packaged again based on the electronic mail information and the initial password information.

[0122] According to the user registration information, prompt information is set in the secondary packaging process;

[0123] Based on the user registration information, the user age information and the user nickname information are determined, and the first authentication identification information is generated, and the user initial image information in the user registration information is obtained, and then the second authentication identification information is generated according to the user initial image information;

[0124] According to the user registration information, the corresponding user basic real index is obtained, and the corresponding user feature coding information is determined;

[0125] Based on the first authentication identification information, the second authentication identification information and the user feature coding information, the unique authentication identification information is generated.

[0126] Specifically, the encryption level is set, and different encryption levels are predefined, for example, it can be simply divided into three levels: low-level encryption is suitable for general user information, medium-level encryption is used for more important information, and high-level encryption is used for highly sensitive information. More levels can also be subdivided according to actual needs, such as corresponding the encryption level to the security risk assessment index, and setting different security strengths and application scenarios for each level. Determine the encryption level, specifically, establish a mapping relationship between the range of user basic real index and the encryption level. For example, the user basic real index range is 0-100, when the index is 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, configure the corresponding encryption library according to different encryption levels. For example, for low-level encryption, some simple and high-performance symmetric encryption algorithm library can be selected, such as DES algorithm in pycryptodome library; for medium-level encryption, AES algorithm (also available in pycryptodome library) can be selected; for high-level encryption, more complex asymmetric encryption algorithm library can be used, such as RSA algorithm (also available in pycryptodome library). Initial encryption, taking AES encryption as an example, for example, user registration information is stored in a dictionary user_registration_info, and it has been converted into a string format data_to_encrypt, and the encryption key key needs to be generated in advance and properly stored. Obtain and store the encryption certificate, after encryption, obtain the encryption certificate. The encryption certificate usually contains information such as encryption algorithm, key length, validity period, etc. In practical application, some certificate generation tools can be used, such as OpenSSL, to generate encryption certificates. After generating the certificate, store it in the blockchain. If Ethereum blockchain is used, the storage of the certificate can be realized through a smart contract. Secondary packaging implementation, secondary packaging can be understood as further confusion or encryption processing of the encryption certificate. For example, the email information and the initial password information can be subjected to some hash operation (such as using SHA-256 algorithm), and then the hash result is spliced or exclusive-OR operated with the encryption certificate, etc., to increase the security of the encryption certificate.

[0127] Further, the unique authentication identification information is used to determine the user to publish health knowledge data, and the user to publish health knowledge data is audited by the system to obtain a system audit basic information matrix, specifically including:

[0128] Based on the unique authentication identification information, the user feature code information is determined;

[0129] The user feature code information is used to determine the corresponding exclusive data storage space of the user in the blockchain, and the corresponding user to publish health knowledge data is obtained;

[0130] Extracting metadata in the health knowledge data published by the user to obtain submission time information, data format information and estimated reading time information;

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

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

[0133] Obtain the text description information, picture information and video information of the health knowledge sub-data to be published in sequence to obtain a plurality of first comparison information;

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

[0135] Compare the basic comparison information with the plurality of first comparison information in sequence to obtain a repetition index;

[0136] Based on natural language processing technology, sensitive word analysis is performed on the basic comparison information, and based on the sensitive word analysis result, a sensitivity index is obtained;

[0137] According to the repetition index, the sensitivity index, the data format information and the estimated reading time information corresponding to the health knowledge data published by the user, the health knowledge data published by the user is audited by the system, and a system audit basic information matrix is established.

[0138] Specifically, based on the unique authentication identification information, the user feature code information is determined, the unique authentication identification information is stored in the database, and is associated with the user feature code information. The corresponding user feature code information can be obtained by querying the database. With the user feature code information, the corresponding exclusive data storage space of the user is determined in the blockchain, and the corresponding user published health knowledge data is obtained. Here, for example, there is a function (get_user_data_from_blockchain) to obtain data from the blockchain, which receives the user feature code information as a parameter and returns the user published health knowledge data. Take the metadata in the user published health knowledge data to get the submission time information, data format information and estimated reading time information, for example, the user published health knowledge data is a dictionary containing these metadata. Based on the submission time information, all the user-submitted health knowledge data within a month is obtained, for example, all the user-submitted health knowledge data is stored in a list, and each element is a dictionary containing submission time information. Get the data type information of all the user-submitted health knowledge data, and based on the data type information, classify the user-submitted health knowledge data to obtain the user-submitted health knowledge sub-data, for example, each user-submitted health knowledge data dictionary has a data_type field indicating the data type.

[0139] Further, the user interaction information corresponding to the user published health knowledge data is collected, and the health knowledge influence matrix is obtained, specifically including:

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

[0141] According to the user interaction information, the user interaction comment information, the user like information, the user collection information, the user forwarding information and the user reference information are obtained;

[0142] Based on the natural language processing technology, the user interaction comment information is analyzed, and a word frequency column chart is constructed according to the analysis result;

[0143] Based on the word frequency column chart, set a word frequency threshold, and integrate the words with a word frequency higher than the word frequency threshold to obtain the user comment hot words;

[0144] The user comment hot words are subjected to sentiment analysis to obtain the user comment sentiment index, and a first influence element is determined;

[0145] Based on the user like information, the user collection information and the user forwarding information, the user like number, the user collection number and the user forwarding number are weighted and summed to obtain a second influence element;

[0146] obtaining user interaction information corresponding to the user publishing health knowledge data, obtaining the third influence element;

[0147] The health knowledge influence matrix is constructed through the first influence element, the second influence element, and the third influence element.

[0148] Specifically, the collection time period needs to determine the period unit: according to the platform business requirements and data characteristics, a suitable time unit is selected, such as seconds, minutes, hours, days, weeks, or months, etc. For example, if the platform data is updated frequently and the real-time requirement is high, minutes or hours can be selected as the unit; if the data changes relatively slowly, days or weeks are more appropriate. Set the specific length of time: after selecting the time unit, determine the specific length of time. For example, set to the last 7 days, 30 days, or the past 24 hours, etc. This needs to consider the timeliness of the data and the goal of analysis, a shorter period can reflect the immediate feedback of recent users, and a longer period can show longer trends and stability. Collecting user interaction information needs to determine the data source: determine which channels to obtain user interaction information, which may include databases, log files, message queues, etc. For example, the database may have a special table to record user comments, likes, and other operations on health knowledge data; log files detail various user behaviors and their timestamps. Develop collection rules: based on the set time period, develop rules to filter data. If the database is used as the data source, SQL query statements can be written to filter out records that meet the time period by comparing the interaction time field with the current time. For example, to collect the interaction information of the last 7 days, a SQL statement similar to SELECT*FROM user_interactions WHERE interaction_time>=CURDATE()-INTERVAL 7DAY can be used. Based on natural language processing technology, analyze user interaction comment information, and based on the analysis results, construct a word frequency column chart. First, remove special characters, HTML tags, stop words, and punctuation marks from the comments, then convert all text to a uniform form, then perform stem extraction, use statistical tools or dictionaries to count word frequency, and finally use a drawing tool to construct a word frequency column chart. Perform sentiment analysis on user comment hot words to obtain user comment sentiment index and determine the first influence element. First, select a sentiment analysis method, including dictionary-based methods and machine learning methods, such as using a pre-defined sentiment dictionary, such as the VADER dictionary in NLTK, which has good performance in sentiment analysis of social media text. The dictionary assigns a positive, negative, or neutral sentiment score to each word, and the overall sentiment tendency is obtained by matching and aggregating the scores of the words in the text.

[0149] It can be understood that in the calculation of the sentiment index, the hot word composed text can be input into the trained model by using the machine learning method, the model outputs the positive, negative or neutral probability, and the sentiment index is calculated according to the probability. For example, the model outputs the positive probability of 0.7 and the negative probability of 0.3, and 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 like information, the user collection information and the user forwarding information, the user like quantity, the user collection quantity and the user forwarding quantity are weighted and summed to obtain the second influence element. First, the weight needs to be determined according to the importance of different interactive behaviors of the platform. For example, if the platform considers that the like behavior represents the immediate recognition of the user to the content, the collection represents the long-term value of the content, and the forwarding means the propagation of the content, the weight can be adjusted according to the business target. If it is desired to emphasize the propagation of the content, a higher weight can be given to the forwarding behavior, such as a like weight of 0.3, a collection weight of 0.3, and a forwarding weight of 0.4. The formula second influence element = like quantity x like weight + collection quantity x collection weight + forwarding quantity x forwarding weight is used for calculation.

[0151] Further, the real-time user interaction information corresponding to the user published health knowledge data is obtained 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 including:

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

[0153] The first to third real-time influence elements are used as the first to third elements of the real-time health knowledge influence matrix, and the health knowledge influence matrix and the real-time health knowledge influence matrix are both 1x3 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 the user feature coding information is determined;

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

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

[0157] Further, a health knowledge social platform management system based on a blockchain is proposed, which is used to realize the management method of any one of the above, including:

[0158] A user management module is configured 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 to a blockchain, obtain user registration timestamp information, construct a user basic information matrix according to user age, nickname and feature encoding information, retrieve user heterogeneous published data on an external platform based on the user nickname and registration information, filter out user heterogeneous health knowledge data in combination with the authorization information, obtain different types of heterogeneous data and corresponding interaction information after processing, obtain a user basic real index through a series of semantic analysis calculations, determine an 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 stored to the blockchain, perform secondary packaging on the encryption certificate based on an email address and an initial password, and set prompt information, and generate unique authentication identification information using the user age, nickname, initial image and feature encoding information;

[0159] A knowledge management module is configured to determine user feature encoding based on the unique authentication identification information, find a user's exclusive data storage space in the blockchain, obtain user published health knowledge data, obtain data to be published within a month and classify them, obtain a repetition index by comparing the basic comparison information with a plurality of first comparison information, obtain a sensitivity index by performing sensitive word analysis on the basic comparison information, complete system audit in combination with data format and estimated reading time information, establish a system audit basic information matrix, determine whether to initially publish knowledge data, shield the data and inform the user of the audit result if the data does not pass the audit, publish the data and give improvement suggestions if the data passes the audit, set a collection time period, collect user interaction information corresponding to the user published health knowledge data initially published, including comment, like, collection, forwarding and reference information, analyze the user interaction comment information to construct a word frequency column chart, set a word frequency threshold to obtain user comment hot words, perform sentiment analysis to obtain a user comment sentiment index to determine a first influence element, weight and sum the like, collection and forwarding quantities to obtain a second influence element, obtain knowledge data and interaction information corresponding to the reference information to determine a third influence element, construct a health knowledge influence matrix, perform professional audit based on the health knowledge influence matrix and the user published health knowledge data, and send the professional audit result and professional improvement suggestions to the user;

[0160] An incentive management module is configured to obtain real-time user interaction information corresponding to the user published health knowledge data in real time, obtain first to third real-time influence elements of a real-time health knowledge influence matrix, construct the real-time health knowledge influence matrix, determine reward content information according to the real-time health knowledge influence matrix, and issue reward information to the user after upgrading the reward content in combination with the unique authentication identification information and the feature encoding information in the user registration information;

[0161] A data management module is configured to store user feature encoding information, encryption certificates, and user published health knowledge data into a blockchain, ensure the security and tamper resistance of the data, and assist other modules in various data analysis, such as analyzing user heterogeneous health knowledge data in the user management module to determine a user basic authenticity index, analyzing the repetition and sensitivity of user published health knowledge data in the knowledge management module, and assisting in analyzing real-time user interaction information in the incentive management module to determine a real-time health knowledge influence matrix.

[0162] Further, the user management module comprises:

[0163] A registration information processing unit is configured 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 into the blockchain, and obtain user registration timestamp information;

[0164] A basic information determination unit is configured to construct a user basic information matrix according to user age, nickname, and feature encoding information, retrieve user heterogeneous published data on an external platform based on the 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 interaction information after processing, and obtain a user basic authenticity index through a series of semantic analysis calculations.

[0165] An encryption authentication unit is configured to determine an encryption level according to the user basic authenticity index, select an encryption library to perform initial encryption on the user registration information and obtain an encryption certificate stored into the blockchain, perform secondary packaging on the encryption certificate based on an email address and an initial password, set prompt information, and generate unique authentication identification information using the user age, nickname, initial image, and feature encoding information.

[0166] Further, the knowledge management module comprises:

[0167] A knowledge publishing review unit is configured to determine user feature encoding based on the unique authentication identification information, find a dedicated data storage space of the user in the blockchain, obtain user published health knowledge data, obtain data to be published within one month and classify the data, obtain a repetition index by comparing basic comparison information with a plurality of first comparison information, obtain a sensitivity index by performing sensitive word analysis on the basic comparison information, complete system review in combination with data format and estimated reading time information, establish a system review basic information matrix, determine whether the knowledge data is published for the first time, shield the data and inform the user of the review result if the knowledge data is not published for the first time, publish the data and give improvement suggestions if the knowledge data is published for the first time.

[0168] An interactive information collection unit is configured to set a collection time period, collect user interaction information corresponding to the user-published health knowledge data, including comments, likes, collections, forwards and reference information, analyze the user interaction comment information to construct a word frequency column chart, set a word frequency threshold to obtain user comment hot words, perform sentiment analysis to obtain a user comment sentiment index to determine a first influence element, sum the number of likes, collections and forwards to obtain a second influence element, obtain knowledge data and interaction information corresponding to the reference information to determine a third influence element, and construct a health knowledge influence matrix.

[0169] A professional review unit is configured to perform professional review based on the health knowledge influence matrix and the user-published health knowledge data, and send professional review results and professional improvement suggestions to the user.

[0170] Further, the incentive management module comprises:

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

[0172] A reward issuing unit is configured to determine reward content information according to the real-time health knowledge influence matrix, combine unique authentication identifier information and feature code information in user registration information, upgrade the reward content, and issue reward information to the user.

[0173] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection claimed by the present application is defined by the appended claims and their equivalents.

Claims

1. A management method for a blockchain-based health knowledge social platform, characterized in that, include: S100: Receive and store user registration information and user authorization information, then determine the user basic information matrix based on the user registration information, and determine the user basic authenticity index in combination with the user authorization information; S200: Based on the user's basic authenticity index, the user registration information is initially encrypted, and a unique authentication identifier is generated based on the user registration information. S300: Based on the unique authentication identifier information, determine the health knowledge data published by the user, and conduct system review of the health knowledge data published by the user to obtain the system review basic information matrix; S400. Based on the system's basic information matrix, determine whether to publish the user's health knowledge data for the first time. If not, block the user's health knowledge data and send the system review result to the user. If yes, publish the user's health knowledge data for the first time and send the user system improvement suggestions. S500: Collect user interaction information corresponding to the initial release of health knowledge data by users, and obtain the health knowledge influence matrix; S600: Based on the health knowledge influence matrix and user-published health knowledge data, conduct professional review of user-published health knowledge data after the initial publication, and send professional review results and professional improvement suggestions to users. S700: Real-time acquisition of real-time user interaction information corresponding to user-published health knowledge data, obtaining a real-time health knowledge influence matrix, and issuing reward information to users based on the real-time health knowledge influence matrix and user registration information; The process of receiving and storing user registration information and user authorization information, then determining a user basic information matrix based on the user registration information, and determining a user basic authenticity index based on the user authorization information, specifically includes: Based on user registration information, obtain user registration timestamp information, including user age information, user nickname information, email address information, and initial password information; Based on one-hot encoding, user registration information is processed to generate user feature encoding information, which is then stored in the blockchain. Based on user age information, user nickname information, and user feature coding information, determine the first, second, and third elements of the user basic information matrix, and construct the user basic information matrix. Based on user nicknames and registration information, we searched on external health knowledge social platforms to obtain heterogeneous user posting data. Based on user authorization information and user registration information, heterogeneous user-published data is filtered to obtain heterogeneous user health knowledge data; Data processing is performed 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. 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 is then obtained in sequence. Based on audio extraction technology, audio data is extracted from heterogeneous health knowledge video data of users, and speech recognition technology is used to convert the extracted audio data and heterogeneous health knowledge audio data of users into text information to obtain first text information and second text information. Based on natural language processing technology, semantic segmentation is performed 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; Based on heterogeneous user interaction information, 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, respectively; Semantic segmentation is performed 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; Obtain the first matching degree, second matching degree, and 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, respectively; Obtain the first level of professionalism, the second level of professionalism, and the third level of professionalism from the first text sub-information, the second text sub-information, and the third text sub-information, respectively; The first, second, and third elements of the user basic information matrix are quantized to obtain the user basic information quantization matrix. Based on the user basic information quantification matrix, first matching degree, second matching degree, third matching degree, first professionalism, second professionalism and third professionalism, obtain the user basic real index; The formula for calculating the user base authenticity index is as follows: In the formula, U true The user base authenticity index is represented by M1, M2, and M3, which are the first, second, and third matching degrees, respectively. P1, P2, and P3 are the first, second, and third professional levels, respectively. R is the user base information quantification matrix. A text1 A text2 A text3 A text4 A text5 and A text6 C represents the number of phrases with semantic relationships in the first to sixth text sub-information, respectively. text14 C text25 and C text36 Z represents the number of phrases with semantic relationships in the fourth to sixth text sub-information that are the same as the number of phrases with semantic relationships in the first to third text sub-information. text1 Z text2 and Z text3 X1, X2, and X3 represent the number of technical terms in the first to third text sub-information, respectively, and X1, X2, and X3 are the first to third elements in the user basic information quantification matrix, respectively. T The transpose of the quantization matrix for user basic information.

2. The management method for a blockchain-based health knowledge social platform according to claim 1, characterized in that, The process of initially encrypting user registration information based on a user base authenticity index and generating a unique authentication identifier based on that information includes: Set the encryption level and determine the encryption level based on the user's basic real index; Based on 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. At the same time, the encryption certificate is obtained and stored in the blockchain. The encrypted certificate stored on the blockchain is repackaged based on email address information and initial password information; Based on user registration information, set prompt messages during the secondary packaging process; Based on user registration information, determine user age information and user nickname information, and generate first authentication identifier information. At the same time, obtain user initial image information from user registration information, and then generate second authentication identifier information based on user initial image information. Based on user registration information, obtain the corresponding user base authenticity index and determine the corresponding user feature coding information; A unique authentication identifier is generated based on the first authentication identifier information, the second authentication identifier information, and the user feature encoding information.

3. The management method for a blockchain-based health knowledge social platform according to claim 2, characterized in that, The process involves using unique authentication information to identify user-published health knowledge data, and then conducting a system review of this data to obtain a basic information matrix for system review. This matrix specifically includes: Based on the unique authentication identifier information, determine the user feature encoding information; Information is encoded using user characteristics, and a dedicated data storage space for each user is determined in the blockchain. At the same time, the health knowledge data published by the user is also obtained. Extract metadata from user-posted health knowledge data to obtain submission time information, data format information, and estimated reading time information; Based on the submission time information, retrieve all health knowledge data submitted by users within one month that is yet to be published. Obtain the data type information of all health knowledge data to be published, and classify the health knowledge data to be published based on the data type information to obtain the health knowledge sub-data to be published; The text description information, image information, and video information of the health knowledge sub-data to be published are obtained sequentially to obtain multiple first comparison information; Obtain basic comparative information by extracting text descriptions, images, and videos from user-posted health knowledge data; The basic comparison information is compared with multiple first comparison information in turn to obtain the repetition index; Based on natural language processing technology, sensitive word analysis is performed on basic comparison information, and a sensitivity index is obtained based on the results of the sensitive word analysis. Based on the repetition index, sensitivity index, data format information, and estimated reading time information of the health knowledge data published by users, the system conducts a review of the health knowledge data published by users and establishes a basic information matrix for system review.

4. The management method for a blockchain-based health knowledge social platform according to claim 3, characterized in that, The process of collecting user interaction information corresponding to the initial posting of health knowledge data by users to obtain a health knowledge influence matrix specifically includes: Set a collection time period, and collect user interaction information corresponding to the user's health knowledge data published for the first time based on the collection time period; Based on user interaction information, obtain user interaction comment information, user like information, user collection information, user forwarding information, and user citation information; Based on natural language processing technology, user interaction comment information is analyzed, and a word frequency histogram is constructed based on the analysis results; A word frequency threshold is set based on a word frequency histogram, and words with frequencies higher than the threshold are integrated to obtain popular words in user comments; Sentiment analysis is performed on trending keywords in user comments to obtain a sentiment index, and the primary influencing factor is determined. Based on user likes, user favorites, and user shares, the number of user likes, user favorites, and user shares are weighted and summed to obtain the second influencing element; Obtain the user-published health knowledge data corresponding to the user citation information, obtain the cited health knowledge data, and obtain the user interaction information corresponding to the cited health knowledge data to obtain the third-party influence element; A health knowledge influence matrix is ​​constructed by using the first, second, and third influencing elements.

5. The management method for a blockchain-based health knowledge social platform according to claim 4, characterized in that, The process of acquiring real-time user interaction information corresponding to user-published health knowledge data, obtaining a real-time health knowledge influence matrix, and distributing reward information to users based on the real-time health knowledge influence matrix and user registration information specifically includes: Based on 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; The first to third real-time influencing elements are used 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. Based on the real-time health knowledge impact matrix, the reward content information is determined, and the unique authentication identifier information is determined through user registration information, while user feature coding information is also determined. Based on the unique authentication identifier information, the reward content information is upgraded to obtain upgraded reward content information; Reward information is distributed to users based on upgrade reward content information and user characteristic coding information.

6. A blockchain-based health knowledge social platform management system, used to implement the management method as described in any one of claims 1-5, characterized in that, include: The user management module receives and stores user registration information and user authorization information. It processes user registration information based on one-hot encoding to generate user feature encoding information and stores it in the blockchain. It also obtains user registration timestamp information, constructs a user basic information matrix based on user age, nickname, and feature encoding information, retrieves heterogeneous user-published data from external platforms based on user nickname and registration information, filters heterogeneous user health knowledge data based on authorization information, and obtains different types of heterogeneous data and corresponding interactive information after processing. It calculates the user basic authenticity index through a series of semantic analyses, determines the encryption level based on the user basic authenticity index, selects an encryption library to perform initial encryption on user registration information and obtains an encryption certificate to store in the blockchain, and then repackages the encryption certificate based on the email address and initial password. At the same time, it sets prompt information and generates unique authentication identifier information using user age, nickname, initial image, and feature encoding information. The knowledge management module is used to determine user feature codes based on unique authentication identifiers, locate the user's exclusive data storage space in the blockchain, acquire user-published health knowledge data, acquire and categorize data to be published within one month, obtain a repetition index by comparing basic comparison information with multiple first comparison information, obtain a sensitivity index by performing sensitive word analysis on the basic comparison information, complete system review by combining data format and estimated reading time information, establish a system review basic information matrix, determine whether the knowledge data is being published for the first time, if it fails, the data is blocked and the user is informed of the review result, if it passes, it is published and improvement suggestions are given, and a collection period is set weekly. In the initial phase, user interaction information corresponding to the health knowledge data published by users for the first time was collected, including comments, likes, favorites, forwards, and citations. User interaction comment information was analyzed to construct a word frequency histogram. A word frequency threshold was set to obtain the hot words in user comments. Sentiment analysis was performed to obtain the user comment sentiment index to determine the first influencing element. The number of likes, favorites, and forwards was weighted and summed 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. Based on the health knowledge influence matrix and the health knowledge data published by users, professional review was carried out, and professional review results and professional improvement suggestions were sent to users. The incentive management module is used to acquire real-time user interaction information corresponding to user-published health knowledge data, 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 reward content information based on the real-time health knowledge influence matrix, and, in combination with the unique authentication identifier information and feature coding information in the user registration information, upgrade the reward content and issue reward information to the user. The data management module stores various types of data, such as user feature encoding information, encrypted certificates, and user-published health knowledge data, on the blockchain to ensure data security and immutability. It also assists other modules in performing various data analyses, such as analyzing heterogeneous user health knowledge data in the user management module to determine the user's basic authenticity index, analyzing the repetition and sensitivity of user-published health knowledge data in the knowledge management module, and assisting in analyzing real-time user interaction information in the incentive management module to determine the real-time health knowledge influence matrix.

7. The health knowledge social platform management system based on blockchain according to claim 6, characterized in that, The user management module includes: The registration information processing unit 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, and obtain user registration timestamp information. The basic information determination unit is used to construct a user basic information matrix based on user age, nickname and feature coding information, retrieve heterogeneous user published data on external platforms based on user nickname and registration information, filter out heterogeneous user health knowledge data in combination with authorization information, obtain different types of heterogeneous data and corresponding interactive information after processing, and calculate the user basic authenticity index through a series of semantic analyses. The encryption authentication unit is used to determine the encryption level based on the user's basic authenticity index, select an encryption library to perform initial encryption on the user's 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, and generate unique authentication identifier information using the user's age, nickname, initial image and feature encoding information.

8. A blockchain-based health knowledge social platform management system according to claim 6, characterized in that, The knowledge management module includes: The knowledge publishing review unit is used to determine the user's feature code based on the unique authentication identifier information, find the user's exclusive data storage space in the blockchain, obtain the user's published health knowledge data, obtain and classify the data to be published within one month, obtain the repetition index by comparing the basic comparison information with multiple first comparison information, obtain the sensitivity index by performing sensitive word analysis on the basic comparison information, complete the system review by combining the data format and estimated reading time information, establish a system review basic information matrix, determine whether the knowledge data is published for the first time, if it fails, the data is blocked and the user is informed of the review result, if it passes, it is published and improvement suggestions are given. An interactive information collection unit is used to set a collection time period, collect user interaction information corresponding to the user's initial release of health knowledge data, including comments, likes, favorites, forwards, and citations. The user interaction comment information is analyzed to construct a word frequency histogram, a word frequency threshold is set to obtain the hot words in user comments, sentiment analysis is performed to obtain the user comment sentiment index to determine the first influencing element, the number of likes, favorites, and forwards is weighted and summed to obtain the second influencing element, and the knowledge data and interaction information corresponding to the citation information are obtained to determine the third influencing element, thus constructing a health knowledge influence matrix. The professional review unit is used to conduct professional reviews based on the health knowledge influence matrix and user-published health knowledge data, and send professional review results and professional improvement suggestions to users.

9. A blockchain-based health knowledge social platform management system according to claim 6, characterized in that, The incentive management module includes: Real-time impact acquisition unit, which is used to acquire real-time user interaction information corresponding to user-published health knowledge data, obtain the first to third real-time impact elements of the real-time health knowledge impact matrix, and construct the real-time health knowledge impact matrix. The reward distribution unit is used to determine the reward content information based on the real-time health knowledge influence matrix, and to distribute the reward information to the user after upgrading the reward content by combining the unique authentication identifier information and feature coding information in the user registration information.

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