A data information intelligent management system and method based on blockchain

Abstract

The present invention discloses a data information intelligent management system and method based on blockchain, which belongs to the field of data information management. The data information management system includes an information group monitoring module, a data management module, a data information analysis module and an information display module. The information group monitoring module is used to monitor the data of the information group, the data management module is used to encrypt and store the collected information group data and the results of analysis and processing, the data information analysis module is used to analyze and process the data of the information group, and the information display module is used to display the analyzed and processed group information to the user and remind him. The present invention monitors group messages and user behaviors in real time, builds a user correlation model, analyzes the correlation degree of group information, and displays and reminds users according to the correlation degree from large to small, thereby ensuring the timeliness of user processing of information, avoiding users from being unable to quickly find group information or forgetting to process, and improving the user's experience.

Description

Technical Field

[0001] The present invention relates to the field of data information management, and specifically to a data information intelligent management system and method based on blockchain. Background Art

[0002] With the development of science and technology, the emergence and popularization of computers have made people's lives more convenient and efficient. People can obtain information, communicate, shop and entertain at any time and anywhere through the Internet, which has greatly enriched people's lives, realized automated, intelligent and efficient working methods, and improved work efficiency and quality. Through computers, people can easily publish information, share resources, discuss issues, etc., which promotes interpersonal communication and information transmission, has a profound impact on people's lives and work, and promotes the development and progress of society.

[0003] In the process of people using computers, information groups play a huge role. Group chat information can improve work efficiency. Necessary exchanges and communication at work can improve work efficiency, increase social circles, and make more friends through group chats to expand one's social circle, which can help everyone learn from each other, help each other, and make progress together. However, with the rapid development of society, when people use a large number of information groups, there are often multiple group messages that need to be processed. In the prior art, information groups are sorted by the time when the information is sent or the groups are classified according to keywords. Groups with the same keywords are sorted according to time. If multiple group messages need to be processed at the same time, it is difficult to process them in time. At the same time, as time changes, group messages may not be viewed by users in time.

[0004] Therefore, it is very important to manage user information groups intelligently and ensure that users can process group information more quickly and efficiently. Therefore, a data information intelligent management system and method based on blockchain is needed. Summary of the invention

[0005] The purpose of the present invention is to provide a data information intelligent management system and method based on blockchain to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a data information intelligent management method based on blockchain, comprising the following steps:

[0007] S1. Monitor the user's group messages in real time, extract content keyword information, enter basic keywords into the database, monitor the user's operation behavior, and encrypt and store them through a private blockchain;

[0008] S2. Analyze and process the group message according to the collected group message content information and the entered keyword information;

[0009] S3, analyzing the relevance of group messages according to the analysis results in S2 and the user's operation behavior;

[0010] S4. Based on the analysis results in S3, the processed group message is displayed to the user.

[0011] Further, in step S2, the basic keyword information entered into the database forms a set Q, and according to the group messages of the collected users, the keyword information in the group messages is extracted, and the collected keyword information is searched and matched in the set Q;

[0012] Set level weights for keywords in the database to form a set R = {r1, r2, ..., r n}, where n represents the number of keywords stored in the database. The extracted keywords are matched one by one with all the keywords in the database to obtain the level weight of the corresponding extracted keywords.

[0013] According to the collected historical chat records, the chat messages sent by user g containing the keywords in the database and the user's own reply messages closest to the time of the messages are extracted, and the number of chat messages containing the keywords in the database is counted as n 发 , the number of user reply messages is n 回 , then n 发 ≥n 回 , the time when the user replies to the message and the time interval closest to the message containing the keyword form a set If there is no keyword-containing information from other users between the time when the user replies to the message and the closest keyword-containing information sent by user g, the communication degree ω between user g and the user himself is calculated by the following formula:

[0014]

[0015] Among them, ε T It is expressed as the variance of the time intervals in the set T, T max Represented as the maximum value of the elements in the set T, T min It is represented as the minimum value of the elements in the set T, α and β are represented as weights, which are preset by relevant technical personnel, and e is represented as a constant, which is preset by relevant technical personnel. The communication degree between all users in all information groups and the user himself is calculated and updated in real time, and saved in the database. When it is monitored that a user in the information group sends a message with a keyword, the communication degree ω of the user in the group is retrieved.

[0016] Furthermore, in step S3, the following steps are included:

[0017] S301, according to the analysis results in S2, in the case where the keyword level weights are consistent, the user's information group level is analyzed according to the user's operating behavior habits in historical data;

[0018] S302: construct a user association model to analyze the association between the user and the group message;

[0019] S303. Compare and sort the relevance of group messages, and automatically adjust the order of group messages according to the relevance, so as to ensure that users can process group messages more efficiently and avoid the situation where user messages cannot be processed in time and are replaced by other messages.

[0020] Furthermore, in step S301, the following steps are included:

[0021] S301-1. Information groups with consistent keyword-level weights form a set S = {s1, s2, ..., s m}, where m represents the number of information groups with consistent keyword-level weights. The user's operation behavior when using the information group is monitored in real time. According to the user's operation behavior habits in historical data, the number of times the user clicks on the information group within time t is obtained to form a set U = {u1, u2, ..., u m};

[0022] S301-2, the information group s is calculated by the following formula i Level Index Z i Perform the calculation:

[0023]

[0024] Among them, u i Represented as information group s i The total number of clicks within time t, the user clicks on information group s i The number of speeches formed a set in Represented as u i The number of times the information group is clicked, Δt k It is expressed as the interval between adjacent speeches; when When i In this click, the user did not speak, and the interval time Δt k =0; when When i In this click, the user speaks once, with an interval of Δt k =0; when When i In this case, the number of times the user speaks is greater than 1, and the interval time Δt k=t k+1 -t k , where t k+1 It is represented by the time corresponding to the k+1th speech among the speeches corresponding to the corresponding number of clicks, t k It is represented by the time corresponding to the kth speech among the speeches corresponding to the corresponding number of clicks. c1 and c2 are constants, which are preset by relevant technical personnel; c1 and c2 are constants; the greater the number of clicks, the greater the number of speeches and the smaller the speech interval of the user, the greater the level index of the information group; conversely, the smaller the number of clicks, the smaller the number of speeches and the larger the speech interval of the user, the smaller the level index of the information group;

[0025] S301-3. Calculate the level index of all information groups with the same keyword level weights, compare them with each other, and sort the information groups from large to small according to the level index to obtain the information group set S * ={s1 * , s2 * ,…,s m *}.

[0026] Further, in step S302:

[0027] According to the level weights extracted in S2 and the analysis results in S301, a user association model is constructed, and the following formula is used to analyze the information group s i The degree of association γ i Perform the calculation:

[0028] γ i =(r i a +Z i b +ω c )Δμ;

[0029] Among them, a and b are expressed as coefficients, which are preset by relevant technicians, and r i Represented as information group s i The level weight of the included keywords, a>b; Δμ represents the maximum time interval between the time when the information group receives the keyword that the user has not viewed and the current time; when the level weight of the group message is high, the level index is high, and the communication level is high, the correlation is high and the priority is high; conversely, when the level weight of the group message is low, the level index is low, and the communication level is low, the correlation is low and the priority is low;

[0030] The correlation degree of all information groups is calculated and compared, and the information groups are sorted from large to small according to the correlation degree.

[0031] Furthermore, in step S4, based on the analysis results of S3, the information group is displayed to the user in the sorted order through the screen, so as to avoid the important information being replaced by other information due to failure to check the group messages in time when sorting by time. At the same time, when the most relevant information group appears, the user is reminded by voice, which ensures that the user can obtain important information in the information group in time, avoids losses caused by missing information, and improves the user's experience.

[0032] A data information intelligent management system based on blockchain, the data information management system includes: an information group monitoring module and a data information analysis module;

[0033] The output end of the information group monitoring module is connected to the input end of the data information analysis module;

[0034] The information group monitoring module is used to monitor the data of the information group, and includes a data entry unit, an information monitoring unit and a user operation monitoring unit; the data entry unit is used to enter standard keyword information and preset parameters in the system, the information monitoring unit is used to monitor the user's group messages in real time and extract content keyword information, and the user operation monitoring unit is used to monitor the user's operation behavior in real time;

[0035] The data information analysis module is used to analyze and process the data of the information group, and includes an information matching unit and an association sorting unit; the information matching unit is used to match the collected keyword information with the standard keywords entered into the database to obtain the level weight of the keywords, and the association sorting unit is used to analyze the information group containing keywords with the same level weight, and to build a user association model based on the collected user operation behavior and historical data, and to sort the groups from large to small according to the degree of association, thereby ensuring that users can process group information more efficiently and avoiding the situation where user information cannot be processed in time and is replaced by other information.

[0036] Further, the data information management system also includes: a data management module, the input end of the data management module is connected to the output end of the information group monitoring module, the input end of the data management module is connected to the output end of the data information analysis module, and the output end of the data management module is connected to the input end of the data information analysis module;

[0037] The data management module is used to encrypt and store the collected information group data and the results of analysis and processing, including a security encryption unit and a storage unit. The security encryption unit securely encrypts the data through the ZUC Zu Chongzhi algorithm to avoid losses caused by the leakage of user chat information and ensure the information security of users. The ZUC Zu Chongzhi algorithm is a kind of national secret algorithm, which is a synchronous sequence cipher. The Zu Chongzhi algorithm set is an encryption and integrity algorithm independently designed by Chinese scholars, including the Zu Chongzhi algorithm, the encryption algorithm 128-EEA3 and the integrity algorithm 128-EIA3. The LFSR design of the ZUC algorithm adopts the m sequence of the prime field GF (231-1) for the first time. This type of sequence has a long period and good statistical characteristics. In addition, since multiplication on the prime field GF(231-1) can be implemented quickly, LFSR fully considers both security and efficiency issues when designing, and can have very efficient software and hardware implementation while achieving high security goals. In the bit reorganization part, data is carefully selected so that the reorganized data has good randomness and the probability of duplication is small enough; the storage unit stores data through a private blockchain. A private blockchain refers to a blockchain controlled by an organization or entity, and its write permission is only granted to specific users or entities. Compared with a public chain, a private chain has higher security because the qualifications of participants are restricted and the influence of malicious nodes will be smaller. It has the characteristics of strong control, strong data privacy, fast transactions, high efficiency, authenticity and decentralization. Moreover, since a private blockchain does not rely on a centralized organization or entity, it can better realize scalability and distributed characteristics, thereby better adapting to application scenarios of different scales.

[0038] Furthermore, the data information management system further comprises: an information display module, the input end of the information display module being connected in single phase to the output end of the data information analysis module;

[0039] The information display module is used to display the analyzed and processed group information to the user and to give reminders, and includes a screen display unit and a voice reminder unit. The screen display unit displays the sorted group information from large to small according to the degree of relevance of the group information through the screen. The voice reminder unit is used to give information reminders to the user through voice when the information group with the highest degree of relevance appears, so as to avoid the situation where the group information is replaced by other information before the user has processed it, thereby ensuring the timeliness of the user's processing of information and improving the user's usage experience.

[0040] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0041] The present invention monitors the user's group messages in real time, extracts content keyword information in the group messages, enters basic keywords in a database, monitors the user's operating behavior, and compares the real-time collected keywords with the keywords in the database, so as to obtain the level weight of the keyword. It only needs to compare and calculate the keywords without semantic analysis, and the analysis speed is faster and more convenient. When the level weight of the keyword is consistent, the user's information group level is analyzed according to the user's operating behavior habits in historical data to obtain the level index of the information group. By constructing a user association model, the degree of association between the user and the group message is analyzed, and the unprocessed information group is sorted from large to small according to the degree of association and then displayed to the user, and the user is reminded by voice, thereby ensuring the timeliness of the user's processing of information, avoiding the situation that the user cannot quickly find the group information or forgets to process it after not paying attention to the information group for a long time, and improving the user's usage experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0043] Figure 1 This is a schematic diagram of the module composition of a data information intelligent management system based on blockchain in the present invention;

[0044] Figure 2 It is a flowchart of the steps of a data information intelligent management method based on blockchain of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] See also Figure 1-Figure 2 The present invention provides a technical solution: a data information intelligent management method based on blockchain, comprising the following steps:

[0047] S1. Monitor the user's group messages in real time, extract content keyword information, enter basic keywords into the database, such as "@everyone" or user nicknames, monitor the user's operation behavior, and encrypt and store it through a private blockchain;

[0048] S2. Analyze and process the group message according to the collected group message content information and the entered keyword information;

[0049] In step S2, the basic keyword information entered in the database forms a set Q, and the keyword information in the group message is extracted according to the collected user's group message, and the collected keyword information is searched and matched in the set Q;

[0050] Set level weights for keywords in the database to form a set R = {r1, r2, ..., r n}, where n represents the number of keywords stored in the database. The extracted keywords are matched one by one with all the keywords in the database to obtain the level weight of the corresponding extracted keywords.

[0051] According to the collected historical chat records, the chat messages sent by user g containing the keywords in the database and the user's own reply messages closest to the time of the messages are extracted, and the number of chat messages containing the keywords in the database is counted as n 发 , the number of user reply messages is n 回 , then n 发 ≥n 回 , the time when the user replies to the message and the time interval closest to the message containing the keyword form a set If there is no keyword-containing information from other users between the time when the user replies to the message and the closest keyword-containing information sent by user g, the communication degree ω between user g and the user himself is calculated by the following formula:

[0052]

[0053] Among them, ε T It is expressed as the variance of the time intervals in the set T, T max Represented as the maximum value of the elements in the set T, T min It is represented as the minimum value of the elements in the set T, α and β are represented as weights, which are preset by relevant technical personnel, and e is represented as a constant, which is preset by relevant technical personnel. The communication degree between all users in all information groups and the user himself is calculated and updated in real time, and saved in the database. When it is monitored that a user in the information group sends a message with a keyword, the communication degree ω of the user in the group is retrieved.

[0054] S3, analyzing the relevance of group messages according to the analysis results in S2 and the user's operation behavior;

[0055] In step S3, the following steps are included:

[0056] S301, according to the analysis results in S2, in the case where the keyword level weights are consistent, the user's information group level is analyzed according to the user's operating behavior habits in historical data;

[0057] In step S301, the following steps are included:

[0058] S301-1. Information groups with consistent keyword-level weights form a set S = {s1, s2, ..., s m}, where m represents the number of information groups with consistent keyword-level weights. The user's operation behavior when using the information group is monitored in real time. According to the user's operation behavior habits in historical data, the number of times the user clicks on the information group within time t is obtained to form a set U = {u1, u2, ..., u m};

[0059] S301-2, the information group s is calculated by the following formula i Level Index Z i Perform the calculation:

[0060]

[0061] Among them, u i Represented as information group s i The total number of clicks within time t, the user clicks on information group s i The number of speeches formed a set in Represented as u i The number of times the information group is clicked, Δt k It is expressed as the interval between adjacent speeches; when When i In this click, the user did not speak, and the interval time Δt k =0; when When i In this click, the user speaks once, with an interval of Δt k =0; when When i In this case, the number of times the user speaks is greater than 1, and the interval time Δt k =t k+1 -t k , where t k+1 It is represented by the time corresponding to the k+1th speech among the speeches corresponding to the corresponding number of clicks, t k It is represented by the time corresponding to the kth speech among the speeches corresponding to the corresponding number of clicks. c1 and c2 are constants, which are preset by relevant technical personnel; c1 and c2 are constants; the greater the number of clicks, the greater the number of speeches and the smaller the speech interval of the user, the greater the level index of the information group; conversely, the smaller the number of clicks, the smaller the number of speeches and the larger the speech interval of the user, the smaller the level index of the information group;

[0062] S301-3. Calculate the level index of all information groups with the same keyword level weights, compare them with each other, and sort the information groups from large to small according to the level index to obtain the information group set S * ={s1 * , s2 * ,…,s m *}.

[0063] S302: construct a user association model to analyze the association between the user and the group message;

[0064] In step S302:

[0065] According to the level weights extracted in S2 and the analysis results in S301, a user association model is constructed, and the following formula is used to analyze the information group s i The degree of association γ i Perform the calculation:

[0066] γ i =(r i a +Z i b +ω c )Δμ;

[0067] Among them, a and b are expressed as coefficients, which are preset by relevant technical personnel, and r i Represented as information group s i The level weight of the included keywords, a>b; Δμ represents the maximum time interval between the time when the information group receives the keyword that the user has not viewed and the current time; when the level weight of the group message is high, the level index is high, and the communication level is high, the correlation is high and the priority is high; conversely, when the level weight of the group message is low, the level index is low, and the communication level is low, the correlation is low and the priority is low;

[0068] Calculate the degree of correlation for all information groups and compare them.

[0069] S303. Compare and sort the relevance of group messages, and automatically adjust the order of group messages according to the relevance, so as to ensure that users can process group information more efficiently and avoid the situation where user information cannot be processed in time and is replaced by other information. For example, there are two work groups, one is a department information group used daily, and the other is an overall company information group that is occasionally viewed. When similar notification information is sent to the two groups and "@ everyone", the relevance between the group information and the user is analyzed through the relevance model, and the group information with a high relevance is displayed first, without affecting the priority display of other group information with a high relevance, so as to avoid the situation where it is difficult to judge the relevance between the information group and the user based on the analysis of keywords and time, and even affects the display of other important group information.

[0070] S4. Based on the analysis results in S3, the processed group message is displayed to the user.

[0071] In step S4, according to the analysis results of S3, the information group is displayed to the user in the sorted order through the screen, so as to avoid the important information being replaced by other information due to failure to check the group messages in time when sorting by time. At the same time, when the most relevant information group appears, the user is reminded by voice, which ensures that the user can obtain important information in the information group in time, avoids losses caused by missing information, and improves the user experience.

[0072] A data information intelligent management system based on blockchain, the data information management system includes: an information group monitoring module and a data information analysis module;

[0073] The output end of the information group monitoring module is connected to the input end of the data information analysis module;

[0074] The information group monitoring module is used to monitor the data of the information group, and includes a data entry unit, an information monitoring unit and a user operation monitoring unit; the data entry unit is used to enter standard keyword information and preset parameters in the system, such as "@everyone" or nicknames set by users in the group, etc., the information monitoring unit is used to monitor the group messages of users in real time and extract content keyword information, and the user operation monitoring unit is used to monitor the user's operation behavior in real time;

[0075] The data information analysis module is used to analyze and process the data of the information group, including an information matching unit and an association sorting unit; the information matching unit is used to match the collected keyword information with the standard keywords entered in the database to obtain the level weight of the keyword; the association sorting unit is used to analyze the information group containing the keyword with the same level weight, build a user association model according to the collected user operation behavior and historical data, and sort the groups from large to small according to the degree of association, so as to ensure that the user can process the group information more efficiently and avoid the situation where the user information cannot be processed in time and is replaced by other information. For example, there are two work groups, one is the department information group used daily, and the other is the company's overall information group that is occasionally viewed. When similar notification information is sent in the two groups and "@ everyone", the association model is used to analyze the association between the group information and the user, and the group information with a high degree of association is displayed first, and the group information with a high degree of association is not affected. The priority display of other group information with a high degree of association is avoided, so as to avoid the situation where it is difficult to judge the association between the information group and the user based on only keyword and time analysis, and even affect the display of other important group information.

[0076] The data information management system also includes: a data management module, the input end of the data management module is connected to the output end of the information group monitoring module, the input end of the data management module is connected to the output end of the data information analysis module, and the output end of the data management module is connected to the input end of the data information analysis module;

[0077] The data management module is used to encrypt and store the collected information group data and the results of analysis and processing, including a security encryption unit and a storage unit. The security encryption unit securely encrypts the data through the ZUC Zu Chongzhi algorithm to avoid losses caused by the leakage of user chat information and ensure the information security of users. The ZUC Zu Chongzhi algorithm is a kind of national secret algorithm, which is a synchronous sequence cipher. The Zu Chongzhi algorithm set is an encryption and integrity algorithm independently designed by Chinese scholars, including the Zu Chongzhi algorithm, the encryption algorithm 128-EEA3 and the integrity algorithm 128-EIA3. The LFSR design of the ZUC algorithm adopts the m sequence of the prime field GF (231-1) for the first time. This type of sequence has a long period and good statistical characteristics. In addition, since multiplication on the prime field GF(231-1) can be implemented quickly, LFSR fully considers both security and efficiency issues when designing, and can have very efficient software and hardware implementation while achieving high security goals. In the bit reorganization part, data is carefully selected so that the reorganized data has good randomness and the probability of duplication is small enough; the storage unit stores data through a private blockchain. A private blockchain refers to a blockchain controlled by an organization or entity, and its write permission is only granted to specific users or entities. Compared with a public chain, a private chain has higher security because the qualifications of participants are restricted and the influence of malicious nodes will be smaller. It has the characteristics of strong control, strong data privacy, fast transactions, high efficiency, authenticity and decentralization. Moreover, since a private blockchain does not rely on a centralized organization or entity, it can better realize scalability and distributed characteristics, thereby better adapting to application scenarios of different scales.

[0078] The data information management system further comprises: an information display module, the input end of the information display module being connected in single phase to the output end of the data information analysis module;

[0079] The information display module is used to display the analyzed and processed group information to the user and to give reminders, and includes a screen display unit and a voice reminder unit. The screen display unit displays the sorted group information from large to small according to the degree of relevance of the group information through the screen. The voice reminder unit is used to give information reminders to the user through voice when the information group with the highest degree of relevance appears, so as to avoid the situation where the group information is replaced by other information before the user has processed it, thereby ensuring the timeliness of the user's processing of information and improving the user's usage experience.

[0080] Embodiment 1:

[0081] If the keyword correlation coefficient of a group Set the correlation coefficient threshold α 阈 =0.9, then |α|>α 阈 , the extracted keywords are consistent with the keywords in the database, and the level weights of the corresponding keywords are obtained;

[0082] If the keyword level weights of information groups s1, s2, and s3 are all 0.5, coefficient a is 2, coefficient b is 0.5, and coefficient c is 1, then the communication level of users sending messages in information group s1 is Information group s1 level index Δμ1=1 minute, then the degree of association γ1=(r i a +Z i b +ω c )Δμ1=2.56;

[0083] If the communication level of users sending messages in information group s2 Information group s2 level index Δμ2=3 minutes, then the degree of association γ2=(r i a +Z i b +ω c )Δμ2=9.26;

[0084] If the communication level of users sending messages in information group s3 Information group s3 level index Δμ3=0.5 minutes, then the correlation degree γ3=(r i a +Z i b +ω c )Δμ3=0.675;

[0085] Then γ2>γ1>γ3, and they are arranged in the order of information group s2, information group s1, and information group s3;

[0086] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0087] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data information intelligent management method based on blockchain, characterized by: The following steps are involved: S1. Monitor the user's group messages in real time, extract content keyword information, enter basic keywords and historical chat content into the database, monitor the user's operation behavior, and encrypt and store it through a private blockchain; S2. Preprocess the group messages according to the collected group message content information, the entered keyword information and the historical chat content; S3, based on the analysis results in S2, and according to the user's clicks and speeches on the information group in the historical data, analyzing the relevance of the group messages; S4. Based on the analysis results in S3, the processed group message is displayed to the user and a voice reminder is given; In step S2, the basic keyword information entered in the database forms a set Q, and the keyword information in the group message is extracted according to the collected user's group message, and the collected keyword information is searched and matched in the set Q; Set level weights for keywords in the database to form a set R = {r1, r2, ..., r n }, where n represents the number of keywords stored in the database, and the extracted keywords are matched one by one with all the keywords in the database to obtain the level weight of the corresponding extracted keywords; According to the collected historical chat records, the chat messages sent by user g containing the keywords in the database and the user's own reply messages closest to the time of the messages are extracted, and the number of chat messages containing the keywords in the database is counted as n 发 , the number of user reply messages is n 回 , the time when the user replies to the message and the time interval closest to the message containing the keyword form a set The communication degree ω between user g and the user himself is calculated by the following formula: Among them, ε T It is expressed as the variance of the time intervals in the set T, T max Represented as the maximum value of the elements in the set T, T min It is represented as the minimum value of the elements in the set T, α and β are weights, and e is a constant. The communication degree between all users in all information groups and the user himself is calculated and updated in real time, and saved in the database. When it is detected that a user in the information group sends a message with a keyword, the communication degree ω of the user in the group is retrieved. In step S3, the following steps are included: S301, according to the analysis results in S2, in the case where the keyword level weights are consistent, the user's information group level is analyzed according to the user's operating behavior habits in historical data; S302: construct a user association model to analyze the association between the user and the group message; S303, comparing and sorting the relevance levels of the group messages, and automatically adjusting the order of the group messages according to the relevance levels; In step S301, the following steps are included: S301-1. Information groups with consistent keyword-level weights form a set S = {s1, s2, ..., s m }, where m represents the number of information groups with consistent keyword-level weights. The user's operation behavior when using the information group is monitored in real time. According to the user's operation behavior habits in historical data, the number of times the user clicks on the information group within time t is obtained to form a set U = {u1, u2, ..., u m }; S301-2, the information group s is calculated by the following formula i Level Index Z i Perform the calculation: Among them, u i Represented as information group s i The total number of clicks within time t, the user clicks on information group s i The number of speeches formed a set in Represented as u i The number of times the information group is clicked, Δt k It is represented as the interval between adjacent speeches; c1 and c2 are represented as constants; S301-3. Calculate the level index of all information groups with the same keyword level weights, compare them with each other, and sort the information groups from large to small according to the level index to obtain the information group set S * ={s1 * , s2 * ,…,s m * }; In step S302: According to the level weights extracted in S2 and the analysis results in S301, a user association model is constructed, and the following formula is used to analyze the information group s i The degree of association γ i Perform the calculation: c i =(r i a +Z i b +oh c )Dm; Among them, a, b and c are coefficients, r i Represented as information group s i The level weight of the included keyword, Δμ is represented by the maximum time interval between the time when the information group receives the keyword that the user has not viewed and the current time; The correlation degree of all information groups is calculated and compared, and the information groups are sorted from large to small according to the correlation degree.

2. According to claim 1, a data information intelligent management method based on blockchain is characterized in that: In step S4, according to the analysis result of S3, the information groups are displayed to the user in the sorted order through the screen, and when the information group with the greatest correlation appears, the user is reminded by voice.

3. A data information intelligent management system based on blockchain, applied to a data information intelligent management method based on blockchain as claimed in claim 1, characterized in that: The data information management system includes: an information group monitoring module and a data information analysis module; The output end of the information group monitoring module is connected to the input end of the data information analysis module; The information group monitoring module is used to monitor the data of the information group, and includes a data entry unit, an information monitoring unit and a user operation monitoring unit; the data entry unit is used to enter standard keyword information and preset parameters in the system, the information monitoring unit is used to monitor the user's group messages in real time and extract content keyword information, and the user operation monitoring unit is used to monitor the user's operation behavior in real time; The data information analysis module is used to analyze and process the data of the information group, and includes an information matching unit and an association sorting unit; the information matching unit is used to match the collected keyword information with the standard keywords entered into the database to obtain the level weight of the keywords, and the association sorting unit is used to analyze the information group containing keywords with the same level weight, build a user association model based on the collected user operation behavior and historical data, and sort the groups from large to small according to the degree of association.

4. According to claim 3, a data information intelligent management system based on blockchain is characterized in that: The data information management system also includes: a data management module, the input end of the data management module is connected to the output end of the information group monitoring module, the input end of the data management module is connected to the output end of the data information analysis module, and the output end of the data management module is connected to the input end of the data information analysis module; The data management module is used to encrypt and store the collected information group data and the results of analysis and processing, and includes a security encryption unit and a storage unit. The security encryption unit securely encrypts the data using the ZUC Zu Chongzhi algorithm; the storage unit stores the data through a private blockchain.

5. According to claim 3, a data information intelligent management system based on blockchain is characterized in that: The data information management system further comprises: an information display module, the input end of the information display module being connected in single phase to the output end of the data information analysis module; The information display module is used to display the analyzed and processed group information to the user and to give reminders, and includes a screen display unit and a voice reminder unit. The screen display unit displays the group information sorted from large to small according to the degree of association of the group information through the screen. The voice reminder unit is used to give information reminders to the user through voice when the information group with the highest degree of association appears.

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