A file data management system and method in a distributed environment

Abstract

The invention discloses an archive data management system and method in a distributed environment, and relates to the technical field of data analysis. Paper archives are converted into electronic archives by scanning instruments, an electronic archive system is established, and after user registration and identity authentication, query conditions are input to generate query records, historical query records are obtained, recommendation parameters are collected and recommendation scores are calculated, abnormal recommendation records are determined, user permissions are assigned, user information in abnormal recommendation records is collected, user personal scores are calculated, user levels are determined, abnormal frequencies of levels are calculated, abnormal user levels are determined, training cycles are set, recommendation success rates of abnormal user levels in a certain training time period are calculated, characteristic time periods of abnormal user levels are determined, and real-time recommendation success rates in characteristic time periods of abnormal user levels are calculated. If the rate is lower than a recommendation success rate threshold, management personnel are reminded to optimize recommendation methods and improve recommendation accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an archive data management system and method in a distributed environment. Background Art

[0002] With the continuous development of information and digital technologies, enterprises and organizations have put forward higher requirements for the management and utilization of archival data, especially in a distributed environment, involving a large number of historical documents, business records, legal documents and other fields;

[0003] As the amount of information continues to increase, the retrieval efficiency and accuracy of archive queries gradually decrease, so the archive recommendation strategy needs to be adjusted accordingly. However, the recommendation strategy adjustment method usually makes unified adjustments for all users based on a preset time interval. This method may lead to an improvement in the recommendation accuracy of some users, while the recommendation accuracy of other users decreases. Therefore, it is necessary to determine which users' recommendation strategies need to be optimized and achieve precise optimization, which can not only improve users' retrieval efficiency, but also reduce unnecessary optimization burden, thereby effectively improving resource utilization. Summary of the invention

[0004] The purpose of the present invention is to provide an archive data management system and method in a distributed environment to solve the problems raised in the prior art.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for managing archive data in a distributed environment, the method comprising:

[0006] Step S100: Establishing an electronic archive system, in which after a user registers and passes identity authentication, he or she enters query conditions, monitors user behavior, and generates query records;

[0007] Step S200: In the electronic archive system, collect recommendation parameters in the historical query records, calculate the recommendation effectiveness of the historical query records, and calculate the recommendation score of the historical query records based on the recommendation effectiveness, and determine abnormal recommendation records;

[0008] Step S300: assigning values ​​to user rights in the electronic archive system, collecting user information parameters in the abnormal recommendation record, calculating the personal score of the user in the abnormal recommendation record, determining the user level in the abnormal recommendation record, calculating the abnormal frequency of the user level, and determining the abnormal user level;

[0009] Step S400: setting a training cycle and dividing it into several training time periods, calculating the recommendation success rate of the abnormal user level in a certain training time period, and determining the characteristic time period of the abnormal user level;

[0010] Step S500: Calculate the real-time recommendation success rate within a characteristic time period of a certain abnormal user level, and if the real-time recommendation success rate is lower than the recommendation success rate threshold, alert the management personnel.

[0011] Furthermore, step S100 includes:

[0012] Step S101: Scanning paper files by a scanning device, extracting text information from the paper files, generating electronic files of the paper files, generating all paper files into electronic files, and establishing an electronic file system;

[0013] Step S102: The user registers through the registration page provided by the electronic archive system. The user enters personal information. After completing the registration, the user uses the user name and password to authenticate and successfully log in to the system. The user enters the query conditions, and a related archive recommendation list is generated for the user. The archives browsed by the user in the archive recommendation list are collected, the time taken by the user to browse any archive is monitored, and a query record is generated at the same time.

[0014] Further, step S200 includes:

[0015] Step S201: In the electronic archive system, historical query records are obtained, and recommendation parameters in a certain historical query record are collected. The recommendation parameters include the order of the archive selected by the user in the archive recommendation list, the time taken by the user to browse the archive, and the number of archives in the archive recommendation list browsed by the user. The recommendation performance is calculated according to the following formula:

[0016] ;

[0017] Where K represents the recommendation efficiency, t represents the time it takes for the user to browse the selected file, and t n It represents the time taken by the user to browse the nth file, m represents the number of files browsed by the user, and M represents the number of files in the file recommendation list. The recommendation score is calculated according to the following formula:

[0018] ;

[0019] Where A represents the recommendation score, N represents the rank of the archive selected by the user in the archive recommendation list, α represents the weight of the rank of the archive selected by the user in the archive recommendation list, and β represents the weight of the recommendation effectiveness;

[0020] Step S202: setting a recommendation score threshold, comparing the recommendation score corresponding to a certain historical query record with the recommendation score threshold, obtaining a recommendation result of the historical query record, if the recommendation score is greater than the recommendation score threshold, the recommendation result of the historical query record is failed, the historical query record is marked as an abnormal recommendation record, if the recommendation score is less than or equal to the recommendation score threshold, the recommendation result of the historical query record is successful, and the abnormal recommendation records are aggregated to obtain an abnormal recommendation record set;

[0021] The recommendation parameters include the order of the archive selected by the user in the archive recommendation list, the time the user spends browsing the archive, and the number of archives in the archive recommendation list that the user browses. The more such data is, the higher the recommendation score calculated will be. The higher the recommendation score is, the longer the user spends searching, and the lower the accuracy of the recommendation will be.

[0022] By analyzing the user's historical query records, the system can calculate the recommendation score, thereby more accurately identifying which files are more valuable to the user, and setting the recommendation score threshold so that the system can identify abnormal recommendation records in a timely manner.

[0023] Further, step S300 includes:

[0024] Step S301: Obtaining preset user rights in the electronic archive system, collecting the archive range that a certain user right allows access to, sorting the user rights from small to large according to the archive range that the user right allows access to, and assigning values ​​to the user rights to obtain the score of any user right;

[0025] Step S302: in the abnormal recommendation record set, obtain the user information parameters in a certain abnormal recommendation record, the information parameters including the user authority, the user age and the average daily system usage time of the user, and calculate the user's personal score according to the following formula:

[0026] ;

[0027] Among them, R represents the user's personal score, B represents the user's authority score, C represents the user's age, D represents the average daily system usage time of the user, and E represents the user's age. b 、E c 、E d They represent the weights of user authority score, user age, and average daily system usage time of users, respectively, and F represents the deviation value of personal score;

[0028] Step S303: Divide the user's personal score into several user levels, obtain the personal score range of a certain user level, and aggregate the abnormal recommendation records that meet the certain user level to obtain a set of abnormal recommendation records of the certain user level;

[0029] Step S304: Obtain the number of abnormal recommendation records in the abnormal recommendation record set of any user level, and calculate the abnormal frequency of the user level according to the following formula:

[0030] ;

[0031] Among them, H g represents the abnormal frequency of the g-th user level, G g It is represented as the number of abnormal recommendation records in the g-th user level, and G' is represented as the total number of abnormal recommendation records;

[0032] Step S305: setting an abnormal frequency threshold, comparing the abnormal frequency corresponding to a certain user level with the abnormal frequency threshold, if the abnormal frequency is greater than or equal to the abnormal frequency threshold, marking the user level as an abnormal user level, and summarizing the abnormal user levels to obtain an abnormal user level set;

[0033] By comprehensively considering the user's permissions, age, and length of time using the system, the system can generate a personalized score for each user, reflecting the user's usage behavior and potential needs.

[0034] Furthermore, step S400 includes:

[0035] Step S401: Selecting a number of consecutive days as a training cycle, setting a number of time points within the training cycle, and setting the time period corresponding to every two adjacent time points as a training time period;

[0036] Step S402: in a certain training time period, obtaining a query record set in the training time period, collecting user information parameters in a certain query record, calculating a personal score of the user in the query record, and determining a user level corresponding to the query record;

[0037] Step S403: Summarize the query records whose user level corresponding to the query records is a certain abnormal user level to obtain a query record set of a certain abnormal user level, obtain the recommendation parameter of a certain query record, calculate the recommendation score of the query record, obtain the recommendation result of the query record, collect the number of query records whose recommendation results of a certain abnormal user level are success and failure, and calculate the recommendation success rate of a certain abnormal user level according to the following formula:

[0038] ;

[0039] Among them, Y uy It is expressed as the recommendation success rate of the u-th abnormal user level in the y-th training period, X uy It is expressed as the number of query records with successful recommendation results for the u-th abnormal user level in the y-th training period, Z uy It is represented by the number of query records with failed recommendation results for the u-th target age group in the y-th training period;

[0040] Step S404: setting a recommendation success rate threshold, comparing the recommendation success rate of a certain abnormal user level in a certain training time period with the recommendation success rate threshold, and if the recommendation success rate is less than the recommendation success rate threshold, setting the training time period as a characteristic time period of the abnormal user level;

[0041] By analyzing the recommendation success rate during the training period, administrators can obtain data support to help them make more scientific decisions, adjust the recommendation model or improve user management measures, thereby improving the overall performance of the system. By setting the recommendation success rate threshold, the system can promptly identify time periods with poor recommendation effects and abnormal user groups, providing data support for subsequent analysis. By identifying abnormal user levels with low recommendation success rates, the system can concentrate resources for optimization and adjustment to avoid wasting resources and affecting recommendation methods with higher recommendation success rates.

[0042] Furthermore, step S500 includes:

[0043] Step S501: obtaining real-time user information parameters in the real-time query record, calculating the real-time user score of the real-time query record, and determining the real-time user level corresponding to the real-time query record;

[0044] Step S502: if the real-time user level is an abnormal user level, the query time point of the real-time query record is collected; if the query time point belongs to a characteristic time period of the abnormal user level, the real-time recommendation parameters in the real-time query record are collected to determine the recommendation result of the real-time query record;

[0045] Step S503: Calculate the real-time recommendation success rate within a characteristic time period of a certain abnormal user level, and when the real-time recommendation success rate is lower than the recommendation success rate threshold, remind the management personnel to optimize the recommendation method of the abnormal user level;

[0046] By timely adjusting the recommendation strategy, when the real-time recommendation success rate is found to be lower than the threshold, the system can actively optimize, thereby increasing the recommendation success rate, reducing invalid recommendations, and improving the recommendation effect of the system. By timely optimizing the recommendation method for abnormal users, the system can concentrate resources on areas that need improvement and avoid interfering with other normal operating parts of the system. Managers can make quick decisions during time periods with poor recommendation effects, thereby optimizing overall resource utilization and improving recommendation accuracy.

[0047] In order to better implement the above method, a file data management system in a distributed environment is also proposed, which includes an electronic file module, an abnormal recommendation record module, an abnormal user level module, a characteristic time period module and a real-time monitoring module;

[0048] Electronic archive module: establish an electronic archive system, in which users register and pass identity authentication, enter query conditions, monitor user behavior, and generate query records;

[0049] Abnormal recommendation record module: in the electronic archive system, collects recommendation parameters in the historical query records, calculates the recommendation effectiveness of the historical query records, and calculates the recommendation score of the historical query records based on the recommendation effectiveness, and determines the abnormal recommendation records;

[0050] Abnormal user level module: assigns user rights in the electronic archive system, collects user information parameters in abnormal recommendation records, calculates personal scores of users in the abnormal recommendation records, determines user levels in the abnormal recommendation records, calculates abnormal frequencies of user levels, and determines abnormal user levels;

[0051] Characteristic time period module: set the training cycle and divide it into several training time periods, calculate the recommendation success rate of abnormal user levels in a certain training time period, and determine the characteristic time period of abnormal user levels;

[0052] Real-time monitoring module: calculates the real-time recommendation success rate within a characteristic time period of a certain abnormal user level, and alerts the management personnel if the real-time recommendation success rate is lower than the recommendation success rate threshold.

[0053] Furthermore, the electronic archive module includes an electronic archive system unit and a user query unit:

[0054] Establishing an electronic archive system unit: Scanning paper archives through a scanning device, extracting text information from the paper archives, generating electronic archives of the paper archives, generating all paper archives into electronic archives, and establishing an electronic archive system;

[0055] User query unit: The user registers through the registration page provided by the electronic archive system. The user enters personal information. After completing the registration, the user uses the user name and password to authenticate and successfully log in to the system. The user enters the query conditions, and a related archive recommendation list is generated for the user. The archives browsed by the user in the archive recommendation list are collected, the time taken by the user to browse any archive is monitored, and a query record is generated at the same time.

[0056] Furthermore, the abnormal recommendation record module includes a recommendation score calculation unit and an abnormal recommendation record determination unit:

[0057] Calculating recommendation score unit: in the electronic archive system, obtaining historical query records, collecting recommendation parameters in a certain historical query record, wherein the recommendation parameters include the position of the archive selected by the user in the archive recommendation list, the time taken by the user to browse the archive, and the number of archives in the archive recommendation list browsed by the user, and calculating the recommendation score;

[0058] Determine abnormal recommendation record unit: set a recommendation score threshold, compare the recommendation score corresponding to a historical query record with the recommendation score threshold, and obtain the recommendation result of the historical query record. If the recommendation score is greater than the recommendation score threshold, the recommendation result of the historical query record is failed, and the historical query record is marked as an abnormal recommendation record. If the recommendation score is less than or equal to the recommendation score threshold, the recommendation result of the historical query record is successful. The abnormal recommendation records are summarized to obtain an abnormal recommendation record set.

[0059] Furthermore, the abnormal user grade module includes a user personal score calculation unit and an abnormal user grade determination unit:

[0060] A user personal score calculation unit: obtains user permissions preset in the electronic archive system, sorts the user permissions from small to large according to the range of permissions, assigns values ​​to the user permissions, obtains the score of any user permissions, obtains user information parameters in a certain abnormal recommendation record in the abnormal recommendation record set, the information parameters include user permissions, user age and the average daily system usage time of the user, and calculates the user's personal score;

[0061] Determine abnormal user level unit: divide the user's personal score into several user levels, obtain the personal score range of a certain user level, summarize the abnormal recommendation records that meet the certain user level, obtain the abnormal recommendation record set of a certain user level, obtain the number of abnormal recommendation records in the abnormal recommendation record set of any user level, calculate the abnormal frequency of the user level, set the abnormal frequency threshold, compare the abnormal frequency corresponding to a certain user level with the abnormal frequency threshold, and if the abnormal frequency is greater than or equal to the abnormal frequency threshold, mark the user level as the abnormal user level.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. By optimizing the processing flow of abnormal recommendation records, the occurrence of invalid recommendations can be reduced, thereby improving user satisfaction, increasing the effective utilization of system resources, and reducing the interference of too many irrelevant recommendations on users;

[0064] 2. Through multi-dimensional data analysis, we can continuously track and optimize abnormal user groups, recommendation strategies and recommendation success rates. This data-based iterative optimization process enables the system to continuously improve the quality of recommendations and adapt to changes in user behavior and needs. Through the introduction of automated scanning, data conversion and recommendation algorithms, we can achieve efficient management and intelligent recommendations of archive data. After users generate query records based on query conditions, the system can dynamically adjust the recommended content and provide personalized and accurate archive recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 The figure is a flow chart of a method for managing archive data in a distributed environment according to the present invention. DETAILED DESCRIPTION

[0066] 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.

[0067] See also Figure 1 The present invention provides a technical solution: a method for managing archive data in a distributed environment, the method comprising:

[0068] Step S100: Establishing an electronic archive system, in which after a user registers and passes identity authentication, he or she enters query conditions, monitors user behavior, and generates query records;

[0069] Wherein, step S100 includes:

[0070] Step S101: Scanning paper files by a scanning device, extracting text information from the paper files, generating electronic files of the paper files, generating all paper files into electronic files, and establishing an electronic file system;

[0071] Step S102: The user registers through the registration page provided by the electronic archive system. The user enters personal information. After completing the registration, the user uses the user name and password to authenticate and successfully log in to the system. The user enters the query conditions, and a related archive recommendation list is generated for the user. The archives browsed by the user in the archive recommendation list are collected, the time taken by the user to browse any archive is monitored, and a query record is generated at the same time.

[0072] Step S200: In the electronic archive system, collect recommendation parameters in the historical query records, calculate the recommendation effectiveness of the historical query records, and calculate the recommendation score of the historical query records based on the recommendation effectiveness, and determine abnormal recommendation records;

[0073] Wherein, step S200 includes:

[0074] Step S201: In the electronic archive system, historical query records are obtained, and recommendation parameters in a certain historical query record are collected. The recommendation parameters include the order of the archive selected by the user in the archive recommendation list, the time taken by the user to browse the archive, and the number of archives in the archive recommendation list browsed by the user. The recommendation performance is calculated according to the following formula:

[0075] ;

[0076] Where K represents the recommendation efficiency, t represents the time it takes for the user to browse the selected file, and t n It represents the time taken by the user to browse the nth file, m represents the number of files browsed by the user, and M represents the number of files in the file recommendation list. The recommendation score is calculated according to the following formula:

[0077] ;

[0078] Where A represents the recommendation score, N represents the rank of the archive selected by the user in the archive recommendation list, α represents the weight of the rank of the archive selected by the user in the archive recommendation list, and β represents the weight of the recommendation effectiveness;

[0079] Step S202: Set a recommendation score threshold, compare the recommendation score corresponding to a historical query record with the recommendation score threshold, and obtain the recommendation result of the historical query record. If the recommendation score is greater than the recommendation score threshold, the recommendation result of the historical query record is failed, and the historical query record is marked as an abnormal recommendation record. If the recommendation score is less than or equal to the recommendation score threshold, the recommendation result of the historical query record is successful, and the abnormal recommendation records are summarized to obtain an abnormal recommendation record set.

[0080] Step S300: assigning values ​​to user rights in the electronic archive system, collecting user information parameters in the abnormal recommendation record, calculating the personal score of the user in the abnormal recommendation record, determining the user level in the abnormal recommendation record, calculating the abnormal frequency of the user level, and determining the abnormal user level;

[0081] Wherein, step S300 includes:

[0082] Step S301: Obtaining preset user rights in the electronic archive system, collecting the archive range that a certain user right allows access to, sorting the user rights from small to large according to the archive range that the user right allows access to, and assigning values ​​to the user rights to obtain the score of any user right;

[0083] Step S302: in the abnormal recommendation record set, obtain the user information parameters in a certain abnormal recommendation record, the information parameters including the user authority, the user age and the average daily system usage time of the user, and calculate the user's personal score according to the following formula:

[0084] ;

[0085] Among them, R represents the user's personal score, B represents the user's authority score, C represents the user's age, D represents the average daily system usage time of the user, and E represents the user's age. b 、E c 、E d They represent the weights of user authority score, user age, and average daily system usage time of users, respectively, and F represents the deviation value of personal score;

[0086] Step S303: Divide the user's personal score into several user levels, obtain the personal score range of a certain user level, and aggregate the abnormal recommendation records that meet the certain user level to obtain a set of abnormal recommendation records of the certain user level;

[0087] Step S304: Obtain the number of abnormal recommendation records in the abnormal recommendation record set of any user level, and calculate the abnormal frequency of the user level according to the following formula:

[0088] ;

[0089] Among them, H g represents the abnormal frequency of the g-th user level, G g It is represented as the number of abnormal recommendation records in the g-th user level, and G' is represented as the total number of abnormal recommendation records;

[0090] Step S305: setting an abnormal frequency threshold, comparing the abnormal frequency corresponding to a certain user level with the abnormal frequency threshold, if the abnormal frequency is greater than or equal to the abnormal frequency threshold, marking the user level as an abnormal user level, and summarizing the abnormal user levels to obtain an abnormal user level set;

[0091] For example, in the first abnormal recommendation record, the user authority score is 2, the user age is 30 years old, the user uses the system for an average of 3 hours a day, and the user's personal score is calculated to be 10.7. The personal score of level 1 is set between 0 and 5, the personal score of level 2 is between 5 and 10, and the personal score of level 3 is between 10 and 15, then the user belongs to level 3.

[0092] Step S400: setting a training cycle and dividing it into several training time periods, calculating the recommendation success rate of the abnormal user level in a certain training time period, and determining the characteristic time period of the abnormal user level;

[0093] Wherein, step S400 includes:

[0094] Step S401: Selecting a number of consecutive days as a training cycle, setting a number of time points within the training cycle, and setting the time period corresponding to every two adjacent time points as a training time period;

[0095] Step S402: in a certain training time period, obtaining a query record set in the training time period, collecting user information parameters in a certain query record, calculating a personal score of the user in the query record, and determining a user level corresponding to the query record;

[0096] Step S403: Summarize the query records whose user level corresponding to the query records is a certain abnormal user level to obtain a query record set of a certain abnormal user level, obtain the recommendation parameter of a certain query record, calculate the recommendation score of the query record, obtain the recommendation result of the query record, collect the number of query records whose recommendation results of a certain abnormal user level are success and failure, and calculate the recommendation success rate of a certain abnormal user level according to the following formula:

[0097] ;

[0098] Among them, Y uyIt is expressed as the recommendation success rate of the u-th abnormal user level in the y-th training period, X uy It is expressed as the number of query records with successful recommendation results for the u-th abnormal user level in the y-th training period, Z uy It is represented by the number of query records with failed recommendation results for the u-th target age group in the y-th training period;

[0099] Step S404: Set a recommendation success rate threshold, compare the recommendation success rate of a certain abnormal user level in a certain training time period with the recommendation success rate threshold, and if the recommendation success rate is less than the recommendation success rate threshold, set the training time period as the characteristic time period of the abnormal user level.

[0100] Step S500: Calculate the real-time recommendation success rate within a characteristic time period of a certain abnormal user level. If the real-time recommendation success rate is lower than the recommendation success rate threshold, alert the management personnel.

[0101] Wherein, step S500 includes:

[0102] Step S501: obtaining real-time user information parameters in the real-time query record, calculating the real-time user score of the real-time query record, and determining the real-time user level corresponding to the real-time query record;

[0103] Step S502: if the real-time user level is an abnormal user level, the query time point of the real-time query record is collected; if the query time point belongs to a characteristic time period of the abnormal user level, the real-time recommendation parameters in the real-time query record are collected to determine the recommendation result of the real-time query record;

[0104] Step S503: Calculate the real-time recommendation success rate within a characteristic time period of a certain abnormal user level. When the real-time recommendation success rate is lower than the recommendation success rate threshold, remind the management personnel to optimize the recommendation method of the abnormal user level.

[0105] In order to better implement the above method, a file data management system in a distributed environment is also proposed, which includes an electronic file module, an abnormal recommendation record module, an abnormal user level module, a characteristic time period module and a real-time monitoring module;

[0106] Electronic archive module: establish an electronic archive system, in which users register and pass identity authentication, enter query conditions, monitor user behavior, and generate query records;

[0107] The electronic archive module includes the electronic archive system establishment unit and the user query unit:

[0108] Establishing an electronic archive system unit: Scanning paper archives through a scanning device, extracting text information from the paper archives, generating electronic archives of the paper archives, generating all paper archives into electronic archives, and establishing an electronic archive system;

[0109] User query unit: The user registers through the registration page provided by the electronic archive system. The user enters personal information. After completing the registration, the user uses the user name and password to authenticate and successfully log in to the system. The user enters the query conditions, and a related archive recommendation list is generated for the user. The archives browsed by the user in the archive recommendation list are collected, the time taken by the user to browse any archive is monitored, and a query record is generated at the same time.

[0110] Abnormal recommendation record module: in the electronic archive system, collects recommendation parameters in the historical query records, calculates the recommendation effectiveness of the historical query records, and calculates the recommendation score of the historical query records based on the recommendation effectiveness, and determines the abnormal recommendation records;

[0111] The abnormal recommendation record module includes a recommendation score calculation unit and an abnormal recommendation record determination unit:

[0112] Calculating recommendation score unit: in the electronic archive system, obtaining historical query records, collecting recommendation parameters in a certain historical query record, wherein the recommendation parameters include the position of the archive selected by the user in the archive recommendation list, the time taken by the user to browse the archive, and the number of archives in the archive recommendation list browsed by the user, and calculating the recommendation score;

[0113] Determine abnormal recommendation record unit: set a recommendation score threshold, compare the recommendation score corresponding to a historical query record with the recommendation score threshold, and obtain the recommendation result of the historical query record. If the recommendation score is greater than the recommendation score threshold, the recommendation result of the historical query record is failed, and the historical query record is marked as an abnormal recommendation record. If the recommendation score is less than or equal to the recommendation score threshold, the recommendation result of the historical query record is successful. The abnormal recommendation records are summarized to obtain an abnormal recommendation record set.

[0114] Abnormal user level module: assigns user rights in the electronic archive system, collects user information parameters in abnormal recommendation records, calculates personal scores of users in the abnormal recommendation records, determines user levels in the abnormal recommendation records, calculates abnormal frequencies of user levels, and determines abnormal user levels;

[0115] The abnormal user level module includes a user personal score calculation unit and an abnormal user level determination unit:

[0116] A user personal score calculation unit: obtains user permissions preset in the electronic archive system, sorts the user permissions from small to large according to the range of permissions, assigns values ​​to the user permissions, obtains the score of any user permissions, obtains user information parameters in a certain abnormal recommendation record in the abnormal recommendation record set, the information parameters include user permissions, user age and the average daily system usage time of the user, and calculates the user's personal score;

[0117] Determine abnormal user level unit: divide the user's personal score into several user levels, obtain the personal score range of a certain user level, summarize the abnormal recommendation records that meet the certain user level, obtain the abnormal recommendation record set of a certain user level, obtain the number of abnormal recommendation records in the abnormal recommendation record set of any user level, calculate the abnormal frequency of the user level, set the abnormal frequency threshold, compare the abnormal frequency corresponding to a certain user level with the abnormal frequency threshold, and if the abnormal frequency is greater than or equal to the abnormal frequency threshold, mark the user level as the abnormal user level.

[0118] Characteristic time period module: set the training cycle and divide it into several training time periods, calculate the recommendation success rate of abnormal user levels in a certain training time period, and determine the characteristic time period of abnormal user levels;

[0119] Real-time monitoring module: calculates the real-time recommendation success rate within a characteristic time period of a certain abnormal user level, and alerts the management personnel if the real-time recommendation success rate is lower than the recommendation success rate threshold.

[0120] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for managing archive data in a distributed environment, characterized in that: Step S100: Establishing an electronic archive system, in which after a user registers and passes identity authentication, he or she enters query conditions, monitors user behavior, and generates query records; Step S200: In the electronic archive system, collect recommendation parameters in the historical query records, calculate the recommendation effectiveness of the historical query records, and calculate the recommendation score of the historical query records based on the recommendation effectiveness, and determine abnormal recommendation records; Step S300: assigning values ​​to user rights in the electronic archive system, collecting user information parameters in the abnormal recommendation record, calculating the personal score of the user in the abnormal recommendation record, determining the user level in the abnormal recommendation record, calculating the abnormal frequency of the user level, and determining the abnormal user level; The step S300 includes: Step S301: Obtaining preset user rights in the electronic archive system, collecting the archive range that a certain user right allows access to, sorting the user rights from small to large according to the archive range that the user right allows access to, and assigning values ​​to the user rights to obtain the score of any user right; Step S302: in the abnormal recommendation record set, obtain the user information parameters in a certain abnormal recommendation record, the information parameters including the user authority, the user age and the average daily system usage time of the user, and calculate the user's personal score according to the following formula: ； Among them, R represents the user's personal score, B represents the user's authority score, C represents the user's age, D represents the average daily system usage time of the user, and E represents the user's age. b 、E c 、E d They represent the weights of user authority score, user age, and average daily system usage time of users, respectively, and F represents the deviation value of personal score; Step S303: Divide the user's personal score into several user levels, obtain the personal score range of a certain user level, and aggregate the abnormal recommendation records that meet the certain user level to obtain a set of abnormal recommendation records of the certain user level; Step S304: Obtain the number of abnormal recommendation records in the abnormal recommendation record set of any user level, and calculate the abnormal frequency of the user level according to the following formula: ； Among them, H g represents the abnormal frequency of the g-th user level, G g It is represented as the number of abnormal recommendation records in the g-th user level, and G' is represented as the total number of abnormal recommendation records; Step S305: setting an abnormal frequency threshold, comparing the abnormal frequency corresponding to a certain user level with the abnormal frequency threshold, if the abnormal frequency is greater than or equal to the abnormal frequency threshold, marking the user level as an abnormal user level, and summarizing the abnormal user levels to obtain an abnormal user level set; Step S400: setting a training cycle and dividing it into several training time periods, calculating the recommendation success rate of the abnormal user level in a certain training time period, and determining the characteristic time period of the abnormal user level; Step S500: Calculate the real-time recommendation success rate within a characteristic time period of a certain abnormal user level, and if the real-time recommendation success rate is lower than the recommendation success rate threshold, alert the management personnel.

2. The method for managing archive data in a distributed environment according to claim 1, characterized in that: The step S100 includes the following steps: Step S101: Scanning paper files by a scanning device, extracting text information from the paper files, generating electronic files of the paper files, generating all paper files into electronic files, and establishing an electronic file system; Step S102: The user registers through the registration page provided by the electronic archive system. The user enters personal information. After completing the registration, the user uses the user name and password to authenticate and successfully log in to the system. The user enters the query conditions, and a related archive recommendation list is generated for the user. The archives browsed by the user in the archive recommendation list are collected, the time taken by the user to browse any archive is monitored, and a query record is generated at the same time.

3. The method for managing archive data in a distributed environment according to claim 2, characterized in that: The step S200 includes the following steps: Step S201: In the electronic archive system, historical query records are obtained, and recommendation parameters in a certain historical query record are collected. The recommendation parameters include the order of the archive selected by the user in the archive recommendation list, the time taken by the user to browse the archive, and the number of archives in the archive recommendation list browsed by the user. The recommendation performance is calculated according to the following formula: ； Where K represents the recommendation efficiency, t represents the time it takes for the user to browse the selected file, and t n It represents the time taken by the user to browse the nth file, m represents the number of files browsed by the user, and M represents the number of files in the file recommendation list. The recommendation score is calculated according to the following formula: ； Where A represents the recommendation score, N represents the rank of the archive selected by the user in the archive recommendation list, α represents the weight of the rank of the archive selected by the user in the archive recommendation list, and β represents the weight of the recommendation effectiveness; Step S202: Set a recommendation score threshold, compare the recommendation score corresponding to a historical query record with the recommendation score threshold, and obtain the recommendation result of the historical query record. If the recommendation score is greater than the recommendation score threshold, the recommendation result of the historical query record is failed, and the historical query record is marked as an abnormal recommendation record. If the recommendation score is less than or equal to the recommendation score threshold, the recommendation result of the historical query record is successful, and the abnormal recommendation records are summarized to obtain an abnormal recommendation record set.

4. The method for managing archive data in a distributed environment according to claim 3, characterized in that: The step S400 includes the following steps: Step S401: Selecting a number of consecutive days as a training cycle, setting a number of time points within the training cycle, and setting the time period corresponding to every two adjacent time points as a training time period; Step S402: in a certain training time period, obtaining a query record set in the training time period, collecting user information parameters in a certain query record, calculating a personal score of the user in the query record, and determining a user level corresponding to the query record; Step S403: Summarize the query records whose user level corresponding to the query records is a certain abnormal user level to obtain a query record set of a certain abnormal user level, obtain the recommendation parameter of a certain query record, calculate the recommendation score of the query record, obtain the recommendation result of the query record, collect the number of query records whose recommendation results of a certain abnormal user level are success and failure, and calculate the recommendation success rate of a certain abnormal user level according to the following formula: ； Among them, Y iy It is expressed as the recommendation success rate of the i-th abnormal user level in the y-th training period, X iy It is expressed as the number of query records with successful recommendation results for the i-th abnormal user level in the y-th training period, Z iy It is represented as the number of query records whose recommendation results of the i-th abnormal user level are failed in the y-th training period; Step S404: Set a recommendation success rate threshold, compare the recommendation success rate of a certain abnormal user level in a certain training time period with the recommendation success rate threshold, and if the recommendation success rate is less than the recommendation success rate threshold, set the training time period as the characteristic time period of the abnormal user level.

5. The method for managing archive data in a distributed environment according to claim 4, characterized in that: The step S500 includes the following steps: Step S501: obtaining real-time user information parameters in the real-time query record, calculating the real-time user score of the real-time query record, and determining the real-time user level corresponding to the real-time query record; Step S502: if the real-time user level is an abnormal user level, the query time point of the real-time query record is collected; if the query time point belongs to a characteristic time period of the abnormal user level, the real-time recommendation parameters in the real-time query record are collected to determine the recommendation result of the real-time query record; Step S503: Calculate the real-time recommendation success rate within a characteristic time period of a certain abnormal user level. When the real-time recommendation success rate is lower than the recommendation success rate threshold, remind the management personnel to optimize the recommendation method of the abnormal user level.

6. An archive data management system in a distributed environment, used to implement an archive data management method in a distributed environment as claimed in any one of claims 1 to 5, characterized in that: The system includes an electronic archive module, an abnormal recommendation record module, an abnormal user level module, a characteristic time period module and a real-time monitoring module; The electronic archive module: establishes an electronic archive system, in which users register and pass identity authentication, enter query conditions, monitor user behavior, and generate query records; The abnormal recommendation record module: in the electronic archive system, collects recommendation parameters in the historical query records, calculates the recommendation effectiveness of the historical query records, and calculates the recommendation score of the historical query records based on the recommendation effectiveness, and determines the abnormal recommendation records; The abnormal user level module: assigns user rights in the electronic archive system, collects user information parameters in the abnormal recommendation record, calculates the personal score of the user in the abnormal recommendation record, determines the user level in the abnormal recommendation record, calculates the abnormal frequency of the user level, and determines the abnormal user level; The characteristic time period module: sets a training cycle and divides it into several training time periods, calculates the recommendation success rate of abnormal user levels in a certain training time period, and determines the characteristic time period of abnormal user levels; The real-time monitoring module calculates the real-time recommendation success rate within a characteristic time period of a certain abnormal user level, and alerts the management personnel if the real-time recommendation success rate is lower than the recommendation success rate threshold.

7. The archive data management system in a distributed environment according to claim 6, characterized in that: The electronic archive module includes an electronic archive system establishment unit and a user query unit: The electronic archive system establishment unit: uses a scanning device to scan paper archives, extracts text information from the paper archives, generates electronic archives of the paper archives, generates all paper archives into electronic archives, and establishes an electronic archive system; The user query unit: the user registers through the registration page provided by the electronic archive system, the user enters personal information, and after completing the registration, uses the user name and password to authenticate and successfully log in to the system. The user enters the query conditions, generates a related archive recommendation list for the user, collects the archives browsed by the user in the archive recommendation list, monitors the time the user spends browsing any archive, and generates a query record at the same time.

8. The archive data management system in a distributed environment according to claim 6, characterized in that: The abnormal recommendation record module includes a recommendation score calculation unit and an abnormal recommendation record determination unit: The unit for calculating the recommendation score: in the electronic archive system, obtains the historical query record, collects the recommendation parameters in a certain historical query record, wherein the recommendation parameters include the position of the archive selected by the user in the archive recommendation list, the time taken by the user to browse the archive, and the number of archives in the archive recommendation list browsed by the user, and calculates the recommendation score; The unit for determining abnormal recommendation records: sets a recommendation score threshold, compares the recommendation score corresponding to a historical query record with the recommendation score threshold, and obtains a recommendation result of the historical query record; if the recommendation score is greater than the recommendation score threshold, the recommendation result of the historical query record is a failure, and the historical query record is marked as an abnormal recommendation record; if the recommendation score is less than or equal to the recommendation score threshold, the recommendation result of the historical query record is a success, and the abnormal recommendation records are summarized to obtain an abnormal recommendation record set.

9. The archive data management system in a distributed environment according to claim 6, characterized in that: The abnormal user grade module includes a user personal score calculation unit and an abnormal user grade determination unit: The user personal score calculation unit: obtains user permissions preset in the electronic archive system, collects the archive range that a certain user permission allows to access, sorts the user permissions from small to large according to the archive range that the user permissions allow to access, and assigns values ​​to the user permissions to obtain the score of any user permission, obtains user information parameters in a certain abnormal recommendation record in the abnormal recommendation record set, and the information parameters include user permissions, user age and the average daily system usage time of the user, and calculates the user's personal score; The unit for determining abnormal user levels: divides the user's personal score into several user levels, obtains the personal score range of a certain user level, summarizes the abnormal recommendation records that meet the certain user level to obtain a set of abnormal recommendation records for a certain user level, obtains the number of abnormal recommendation records in the set of abnormal recommendation records for any user level, calculates the abnormal frequency of the user level, sets an abnormal frequency threshold, compares the abnormal frequency corresponding to a certain user level with the abnormal frequency threshold, and if the abnormal frequency is greater than or equal to the abnormal frequency threshold, marks the user level as the abnormal user level.

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