Team ledger management system

Abstract

The invention discloses a team ledger management system, and relates to the technical field of ledger management. According to the main technical scheme, the system comprises a storage module, a first information input module and a second information input module, a database is constructed in the storage module, and a plurality of classification directories are arranged in the database; the first information input module is used for acquiring standing book information of a standing book to be stored and giving corresponding classification information to the standing book to be stored according to the standing book information, and the first information input module is further used for storing the standing book to be stored carrying the classification information to a corresponding classification directory according to the standing book information; and the second information input module is used for acquiring user information of the user, and storing the user information and consulting information to the classification catalog according to the consulting information of the user consulting the standing book under the classification catalog. Risks of disordered, repeated storage, omission and the like of machine account information are reduced, so that the error rate when teams acquire learning contents is reduced, and the training effect and the management efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ledger management, and in particular to a team ledger management system. Background Art

[0002] Traditional team learning and activities primarily rely on offline centralized training and textbook materials. These methods are relatively monotonous, with limited presentation options and time and space constraints. Furthermore, training content often comes from a relatively fixed source, often consisting of universal, generalized courseware. This lacks customized content for specific positions and types of work. This results in insufficiently targeted and specialized training, making it difficult to meet the new requirements of modern enterprises for information-based and data-driven management of team learning.

[0003] However, the existing team safety knowledge targeted service system has difficulty in achieving efficient and accurate classification and storage when faced with newly entered ledger information, resulting in chaotic learning resource management, greatly increasing the error rate of teams in obtaining learning content, and reducing training effectiveness and management efficiency. Summary of the Invention

[0004] The purpose of the present invention is to provide a team ledger management system, which solves the problem that the existing team safety knowledge directional service system is difficult to achieve efficient and accurate classification and storage when faced with newly entered ledger information, resulting in chaotic learning resource management, greatly increasing the error rate of the team in obtaining learning content, and reducing training effectiveness and management efficiency.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0006] A team ledger management system is provided, comprising a storage module, a first information entry module and a second information entry module. A database is constructed in the storage module, and a plurality of classification directories are arranged in the database. The first information entry module is used to obtain the ledger information of the ledger to be stored, and assign the corresponding classification information to the ledger to be stored according to the ledger information, and the first information entry module is also used to store the ledger to be stored with the classification information in the corresponding classification directory according to the ledger information; the second information entry module is used to obtain the user information of the user, and store the user information and the reference information in the classification directory according to the user's reference to the ledger under the classification directory.

[0007] A further solution is: the ledger information includes the ledger name; the first information entry module is used to obtain the target correlation between the ledger name and the directory name of the classification directory, and store the ledger to be stored in the classification directory with the largest target correlation.

[0008] A further solution is: a method for obtaining the target correlation between the ledger name and the directory name of the classification directory, comprising: constructing a first data set; the first data set contains the characters in the ledger name; matching the characters in the first data set with the target name of the classification directory to obtain a first matching number; calculating the number of characters in the first data set and the first matching number to obtain a first correlation; constructing a second data set; the second data set contains words composed of the characters in the first data set; matching the words in the second data set with the target name of the classification directory to obtain a second matching number; calculating the number of words in the second data set and the second matching number to obtain a second correlation; calculating the first weight coefficient, the second weight coefficient, the first correlation and the second correlation to obtain a target correlation; wherein, the first weight coefficient is the correlation between the characters in the first data set and the target name of the classification directory; the second weight coefficient is the correlation between the words in the second data set and the target name of the classification directory.

[0009] A further solution is: the construction process of the second data set includes: S100, presetting the initial value of the number of extracted characters to M; wherein M=2; S200, extracting M characters from the first data set; S300, combining the extracted M characters to obtain words; S400, repeating S200 and S300 until the first data set is traversed, and jumping to S500; S500, setting the loop value of the number of extracted characters to N; wherein N=M+1; S600, replacing the initial value M with the loop value N, and jumping to S200; S700, repeating S500 and S600 until the loop value N is greater than the number of characters in the first data set, and obtaining the second data set.

[0010] A further solution is: before constructing the first data set, it also includes: cleaning the target characters in the ledger name.

[0011] A further solution is: the classification information includes the department to which the ledger belongs; the team ledger management system also includes a first judgment module, which is used to determine whether the user information belongs to the department to which it belongs. If so, the user is allowed to view the ledger under the classification directory and store the user information in the classification directory; if not, the user is not allowed to view the ledger under the classification directory.

[0012] A further solution is: the review information includes the review start time and the review end time; the team ledger management system also includes a calculation module and a second judgment module; the calculation module is used to calculate the user's review time based on the review start time and the review end time; the second judgment module is used to judge whether the review time is greater than or equal to the time threshold. If so, the user information and the review end time are stored in the classification directory and assigned a first review identifier; if not, the user information and the review time are stored in the classification directory and assigned a second review identifier.

[0013] A further solution is: the classification information also includes the learning type of the ledger; the team ledger management system also includes a third judgment module, and the third judgment module is used to judge whether the learning type of the ledger is a compulsory course. If so, a prompt message is generated to prompt the user corresponding to the second review identifier; if not, no prompt message is generated.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] On the one hand, by identifying ledger information and automatically classifying and archiving it, manual intervention is reduced, improving the efficiency and accuracy of ledger information entry. This is expected to reduce risks such as information confusion, duplicate storage, and omissions, thereby lowering the error rate when teams access learning content, thereby improving training effectiveness and management efficiency. On the other hand, by tracking and recording user access information, the error rate when teams access learning content is further reduced, thereby improving training effectiveness and management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a block diagram of a team ledger management system in this embodiment. DETAILED DESCRIPTION

[0017] The present invention will be further described below with reference to the accompanying drawings.

[0018] Example 1: This example provides a team record management system, such as Figure 1 As shown, it includes a storage module, a first information entry module and a second information entry module. A database is constructed in the storage module, and multiple classification directories are arranged in the database; the first information entry module is used to obtain the ledger information of the ledger to be stored, and assign the corresponding classification information to the ledger to be stored according to the ledger information, and the first information entry module is also used to store the ledger to be stored with the classification information in the corresponding classification directory according to the ledger information; the second information entry module is used to obtain the user information of the user, and store the user information and the reference information in the classification directory according to the user's reference to the ledger under the classification directory.

[0019] For example, during the implementation process, a database is constructed in the storage module, and the database is provided with classification directories such as safety and environmental protection, management quality, cleanliness and conservation, learning and innovation, and humanistic harmony, which are used to store corresponding team ledgers.

[0020] The first information entry module is used to obtain the ledger information of the ledger to be stored. Among them, the ledger information may include the ledger name, ledger content, ledger file format, etc. After obtaining the ledger information of the ledger, the first information entry module identifies the content of the ledger information and assigns the corresponding classification information to the ledger to be stored according to the content of the ledger information. Among them, the classification information includes the department to which the ledger belongs and the type of study. The type of study includes types such as unspecified study, compulsory courses, and elective courses. After receiving the classification information of the ledger, the first information entry module matches the corresponding classification directory in the database according to the ledger information, and stores the ledger carrying the classification information in the corresponding classification directory in the database.

[0021] The second information entry module is used to obtain user information. This information includes the user's name, position, department, etc. After obtaining the user's information, the second information entry module tracks the user's access information for the ledger under the category directory. This information includes the time of access, the content of the access, and whether the access information was downloaded. The user information corresponding to the ledger is then bound to the access information and stored in the corresponding category directory.

[0022] During the specific implementation process, after uploading a "Safety Operating Procedures for Electric Welding Operations" ledger to the team ledger management system. First, the first information entry module uses natural language processing (NLP) technology to analyze the document content and identify the ledger names "electric welding" and "safety operation". Secondly, the first information entry module assigns classification information that the ledger belongs to the mechanical second shift and the learning type is a compulsory course based on the ledger name. Then, after the first information entry module determines that the ledger should be stored in the safety and environmental protection classification directory based on the ledger name, it stores the ledger in the safety and environmental protection classification directory and attaches the classification information that the department belongs to the mechanical second shift and the learning type is a compulsory course. When user A (position: welder) logs in to the system, the second information entry module obtains user A's user information and tracks the information that user A consults the ledger under the classification target. For example, after the second information entry module tracks that user A consulted the "Electric Welding Operation Safety Procedures" ledger under the safety and environmental protection category at 14:20 on March 15, 2024, it binds user A's name and the time of consultation, and records the name and time of consultation under the "Electric Welding Operation Safety Procedures" ledger under the safety and environmental protection category. On the one hand, by identifying the ledger information of the ledger, automatic classification and archiving are performed to reduce manual intervention and improve the efficiency and accuracy of ledger information entry. In order to reduce the risks of ledger information confusion, duplicate storage, omissions, etc., thereby reducing the error rate when the team obtains learning content, so as to improve the training effect and management efficiency. On the other hand, by tracking and recording user consultation information, it is expected to further reduce the error rate when the team obtains learning content, so as to improve the training effect and management efficiency.

[0023] Example 2: To improve the accuracy of storing ledgers in corresponding classification directories. Based on the above Example 1, in this example, the ledger information includes the ledger name; the first information entry module is used to obtain the target correlation between the ledger name and the directory name of the classification directory, and store the ledger to be stored in the classification directory with the highest target correlation.

[0024] For example, during implementation, the first information entry module obtains the name of the ledger to be stored and calculates the target correlation between the ledger name and the directory names of each category. The ledger to be stored is then stored in the category with the highest target correlation. This semantic similarity-based classification method can more accurately identify the semantic correlation between the ledger and the category, thereby improving the accuracy of storing the ledger in the corresponding category.

[0025] In this embodiment, the method for obtaining the target correlation between the ledger name and the directory name of the classification directory includes: constructing a first data set; the first data set includes each character in the ledger name;

[0026] For example, during the implementation process, each individual character in the ledger name is extracted. For example, if the ledger name is "Electric Welding Operation Safety Operation Procedures", the extraction result is "Electric, Welding, Operation, Safety, Operation, Procedures", and the extraction result is used as the first data set.

[0027] Matching the characters in the first data set with the target name of the classification directory to obtain a first matching number;

[0028] Exemplarily, during the implementation process, each character in the first data set is matched with the target name of the classification directory, and the number of successful matches is counted to obtain a first matching number.

[0029] Calculating the number of characters in the first data set and the first matching number to obtain a first relevance;

[0030] Exemplarily, during implementation, the first degree of association is calculated based on the number of characters in the first data set and the first matching number using a specific algorithm (eg, a proportional method or other custom algorithms).

[0031] Constructing a second data set; the second data set includes words composed of the characters in the first data set;

[0032] In this embodiment, the process of constructing the second data set includes: S100, presetting an initial value of the number of extracted characters to M; wherein M=2;

[0033] Exemplarily, during the implementation process, the initial value of the preset number of extracted characters is M, which means that two consecutive characters are extracted and combined each time from the first data set consisting of the ledger name.

[0034] S200, extracting M characters from the first data set;

[0035] Exemplarily, during implementation, M=2 characters are randomly extracted from the first data set. For example, the first data set is ['electricity', 'welding', 'work', 'industry', 'safety', 'security', 'safety', 'operation', 'work', 'rules', 'procedure'], and the two characters extracted from the first data set are "electricity" and "safety".

[0036] S300, combining the M extracted characters to obtain a word;

[0037] Exemplarily, during implementation, the two characters "electricity" and "safety" extracted from the first data set are combined to obtain the words "electricity safety" and "safety electricity".

[0038] S400, repeat S200 and S300 until the first data set is traversed, then jump to S500;

[0039] Exemplarily, during the implementation process, steps S200 and S300 are repeated until M = 2 and all the words in the first dataset are traversed. Then, jump to S500.

[0040] S500, set the loop value of the number of words extracted as N; where N = M + 1;

[0041] Exemplarily, during the implementation process, after arbitrarily selecting 2 words from the first dataset for word combination, set the loop value of the number of words extracted as N. Where N = M + 1. That is, N = 3.

[0042] S600, replace the initial value M with the loop value N and jump to S200;

[0043] Exemplarily, during the implementation process, replace M in step S200 with N and execute steps S200, S30, and S400 again. For example, the 3 words extracted from the first dataset are "electricity", "ampere", and "operation". Combine the 3 words "electricity", "ampere", and "operation" extracted from the first dataset to obtain words such as "electricity-ampere-operation", "ampere-electricity-operation", "operation-electricity-ampere", and "operation-ampere-electricity".

[0044] S700, repeat S500 and S600 until the loop value N is greater than the number of words in the first dataset to obtain the second dataset.

[0045] Exemplarily, during implementation, steps S500 and S600 are repeated until N is greater than the number of characters in the first dataset. For example, the first dataset is: ['electricity', 'welding', 'work', 'industry', 'safety', 'safety', 'operation', 'work', 'rules', 'procedure'], meaning the number of characters in the first dataset is 10. In other words, when N > 10, the loop ends. At this point, the words are counted to obtain a second dataset. The second dataset (word set) includes the following: two-character words: electric safety, electric safety, operation rules, operation rules, electric welding, operation, safety, operation, and procedures; three-character words: electric welding operation, welding operation, operation safety, safety operation, operation rules, operation procedures, electric safety operation, electric operation, electric operation, electric safety, and operation safety, etc. Four-character words: welding operation, welding operation safety, operation safety, safe operation, operating procedures, welding operation, safe operation, etc.…Ten-character words: welding operation safety operating procedures, safety operating procedures welding operation, operating procedures welding operation safety operation, etc. On the one hand, not only single characters are extracted, but also continuous word combinations are extracted, covering a variety of semantic units from single characters, two-character words, three-character words to long sentences. In the hope of improving the semantic coverage and matching probability between the ledger name and the directory name, and thus improving the accuracy of storing the ledger in the corresponding classification directory. On the other hand, by recombining the extracted characters, it is hoped to reduce the risk of users misoperating when naming the ledger, resulting in the disorder of the order of characters in the ledger name, and thus leading to a higher error rate when classifying the ledger into the classification directory.

[0046] Matching the words in the second data set with the target name of the classification directory to obtain a second matching number;

[0047] Exemplarily, during the implementation process, each word in the second data set is matched with the target name of the classification directory, and the number of successful matches (ie, the second matching number) is counted.

[0048] Calculating the number of words in the second data set and the second matching number to obtain a second relevance;

[0049] Exemplarily, during implementation, the second degree of association is calculated based on the number of words in the second data set and the second number of matches using a specific algorithm (such as a proportional method or other custom algorithms).

[0050] The first weight coefficient, the second weight coefficient, the first correlation degree and the second correlation degree are calculated and processed to obtain the target correlation degree; wherein the first weight coefficient is the correlation between the characters in the first data set and the target name of the classification directory; the second weight coefficient is the correlation between the words in the second data set and the target name of the classification directory.

[0051] For example, during the implementation process, the first weight coefficient and the second weight coefficient are determined based on actual conditions or historical data analysis. The first weight coefficient represents the correlation between the text in the first data set and the target name of the classification catalog. The second weight coefficient represents the correlation between the words in the second data set and the target name of the classification catalog. Using the first weight coefficient, the second weight coefficient, the first correlation and the second correlation, the final target correlation is calculated by weighted average or other appropriate mathematical models. The target correlation = first weight coefficient * first correlation + second weight coefficient * second correlation. On the one hand, the matching of both text and words is taken into account, making the classification results more accurate, especially for those ledgers that rely on specific terms or phrases to distinguish different categories. On the other hand, it can not only handle simple keyword matching problems, but also understand more complex language structures and adapt to more diverse ledger naming habits. On the other hand, the weight coefficient can be adjusted according to the performance in actual applications to optimize system performance and better meet the needs of specific fields.

[0052] In this embodiment, in order to reduce the amount of data in the calculation process of the first correlation and the second correlation, before constructing the first data set, the method further includes: cleaning the target characters in the ledger name.

[0053] Exemplarily, during the implementation process, before constructing the first data set, the target characters in the ledger name are cleaned. For example, the ledger name is "2024 - Safety operating procedures for electric welding operations", and the target characters such as numbers, punctuation marks, and stop words in the ledger name are deleted. That is, the numbers "2024", punctuation marks "-", stop words "of", etc. that are not related to the classification are deleted from the ledger name. After the ledger name "2024 - Safety operating procedures for electric welding operations" is cleaned, the ledger name is obtained as "Safety operating procedures for electric welding operations". On the one hand, by cleaning out characters that are not related to the classification, it is expected to reduce the amount of data that needs to be processed, thereby reducing the computational complexity and increasing the computational speed. On the other hand, cleaning out characters that are not related to the classification can reduce the risk of a large classification error rate due to a large number of irrelevant characters in the name.

[0054] Example 3: Based on the above example 1, Figure 1 As shown, in this embodiment, the classification information includes the department to which the ledger belongs; the team ledger management system also includes a first judgment module, which is used to judge whether the user information belongs to the department to which it belongs. If so, the user is allowed to view the ledger under the classification directory and store the user information in the classification directory; if not, the user is not allowed to view the ledger under the classification directory.

[0055] Exemplarily, during the implementation process, the above-mentioned classification information includes the department to which the ledger belongs. The team ledger management system also includes a first judgment module, which is used to judge whether the user belongs to the department of the ledger classification information based on the user's user information, so as to decide whether to allow the user to access the ledger under the corresponding classification directory. When a user attempts to access a ledger under a certain classification directory, the second information entry module first obtains the user's user information (such as name, position, department, etc.). At this time, the first judgment module checks the "department" in the user information to determine whether the user belongs to the department in the classification information of the ledger under the classification directory being attempted to be accessed. If the user does belong to the department in the classification information of the ledger, the user is allowed to view the corresponding ledger, and the user information and the viewing information of the user are stored in the classification directory. If the user does not belong to the department in the classification information of the ledger, the user's access request is rejected, and the user is not allowed to view the relevant ledger.

[0056] For example, consider a ledger titled "Welding Safety Operating Procedures," categorized under the "Safety and Environmental Protection" category and marked as belonging to the "Mechanical Shift 2" department. When User A (from Mechanical Shift 2) logs in and attempts to access the "Welding Safety Operating Procedures," the system verifies through the first judgment module that User A belongs to "Mechanical Shift 2," permits access, and records the access. When User B (from Mechanical Shift 1) attempts to access the "Welding Safety Operating Procedures," the system verifies through the first judgment module that User B does not belong to "Mechanical Shift 2," and denies the access request. On the one hand, restricting unauthorized users from accessing specific departmental ledgers aims to reduce the risk of sensitive information leakage and thereby improve the security of internal knowledge resources within the enterprise. This is particularly important when dealing with commercial secrets or important training materials. On the other hand, allocating learning resources based on departmental responsibilities ensures that every employee acquires the necessary knowledge relevant to their job, thereby reducing the excessive use of system resources by unauthorized personnel and improving overall efficiency. On the other hand, legitimate users can more easily find the information they need without being distracted by a large amount of irrelevant content, thereby reducing the possibility of misoperation and the risk of data confusion caused by erroneous access.

[0057] Example 4: Based on the above Example 3, Figure 1As shown, in this embodiment, the review information includes the review start time and the review end time; the team ledger management system also includes a calculation module and a second judgment module; the calculation module is used to calculate the user's review time based on the review start time and the review end time; the second judgment module is used to judge whether the review time is greater than or equal to the time threshold. If so, the user information and the review end time are stored in the classification directory and assigned a first review identifier; if not, the user information and the review time are stored in the classification directory and assigned a second review identifier.

[0058] Exemplarily, during implementation, the aforementioned access information includes the access start time and the access end time. The team record management system also includes a calculation module and a second judgment module. When a user attempts to access a record under a certain category, the calculation module calculates the user's access duration based on the access start time and the access end time. Where access duration = access end time - access start time.

[0059] A time threshold is set based on actual conditions or historical data analysis. The second judgment module compares the browsing time with the preset time threshold. If the browsing time is greater than or equal to the time threshold, it is considered that the user has fully studied the ledger, and a first browsing flag (such as "learned", "learning completed", etc.) is assigned, and the user information and the end time of the browsing are stored in the classification directory. If the browsing time is less than the time threshold, it is considered that the user has only conducted a preliminary browsing, and a second browsing flag (such as "quick browsing, learning incomplete, etc.) is assigned, and the user information and browsing time are stored in the classification directory.

[0060] For example, suppose a ledger named "Safety Operating Procedures for Welding Operations" is categorized under the "Safety and Environmental Protection" category and is marked as belonging to the "Mechanical Shift 2" department. User A (from Mechanical Shift 2) logs in and attempts to access the "Safety Operating Procedures for Welding Operations" document. The query starts at 3:00 PM on July 25, 2025, and ends at 3:30 PM on July 25, 2025. The calculation module calculates the query duration to be 30 minutes, assuming the threshold is 20 minutes. The second judgment module then determines that the query duration exceeds the threshold and assigns the first query flag "Learned" to the user. User A's user information and the query end time are stored in the category. User B (from Mechanical Shift 2) logs in and attempts to access the "Safety Operating Procedures for Welding Operations" document. The query starts at 4:00 PM on July 25, 2025, and ends at 4:10 PM on July 25, 2025. The calculation module calculates the query duration to be 10 minutes. At this point, the second judgment module confirms that the browsing time has not reached the time threshold, so it assigns the second browsing identifier "Quick Browse" and stores the user information and browsing time in the classification directory. On the one hand, it is expected to achieve a more accurate understanding of the employees' learning depth of different ledgers and provide data support for subsequent behavioral analysis. On the other hand, by assigning different browsing identifiers, the user's learning pattern and preferences can be better understood, so as to facilitate the optimization of resource allocation and personalized recommendations. On the other hand, the evaluation indicators based on the browsing time: help managers evaluate the effectiveness of training materials and employees' learning status, so as to ensure that every employee can accumulate enough knowledge.

[0061] Example 5: Based on the above example 4, Figure 1 As shown, in this embodiment, the classification information also includes the learning type of the ledger; the team ledger management system also includes a third judgment module, and the third judgment module is used to judge whether the learning type of the ledger is a compulsory course. If so, a prompt message is generated to prompt the user corresponding to the second reference identifier; if not, no prompt message is generated.

[0062] Exemplarily, during implementation, the aforementioned classification information also includes the learning type of the ledger. The team ledger management system also includes a third judgment module, which is used to determine whether the learning type of the ledger is "compulsory" and, based on the judgment result, determines whether to generate a reminder message. Learning types include compulsory, elective, and unspecified. When a user is marked with a second reference identifier, the third judgment module determines whether the learning type of the ledger is "compulsory." If the ledger is "compulsory," a reminder message is generated to remind the user identified by the second identifier that further study of the ledger is required. If the ledger is not "compulsory," no reminder message is generated, and the user identified by the second reference identifier is not reminded. For ledgers marked as "compulsory," the system pays special attention to user review, ensuring that employees fully study the relevant content, thereby reducing the risk of missing important knowledge due to neglect. Furthermore, by generating reminder messages, users who have only briefly browsed the content are urged to re-read the relevant content, thereby improving the overall effectiveness and quality of training.

[0063] Although the present invention has been described herein with reference to a number of illustrative embodiments thereof, it will be understood that numerous other modifications and implementations may be devised by those skilled in the art that fall within the scope and spirit of the principles disclosed herein. More specifically, within the scope of the present disclosure, the drawings, and the claims, numerous variations and modifications may be made to the components and / or layout of the subject combination arrangement. In addition to variations and modifications to the components and / or layout, other uses will also be apparent to those skilled in the art.

Claims

1. A team record management system, characterized in that: include: A storage module, wherein a database is constructed in the storage module, and a plurality of classification directories are arranged in the database; a first information entry module, the first information entry module being used to obtain ledger information of a ledger to be stored, and assign corresponding classification information to the ledger to be stored based on the ledger information, and the first information entry module being further used to store the ledger to be stored with the classification information into a corresponding classification directory based on the ledger information; The second information entry module is used to obtain the user information of the user, and store the user information and the reference information in the classification directory according to the reference information of the ledger under the classification directory consulted by the user.

2. The team record management system according to claim 1, characterized in that: The ledger information includes the ledger name; The first information input module is used to obtain the target correlation between the ledger name and the directory name of the classification directory, and store the ledger to be stored in the classification directory with the largest target correlation.

3. The team record management system according to claim 2, characterized in that: The method for obtaining the target correlation between the ledger name and the directory name of the classification directory includes: Constructing a first data set; the first data set includes the characters in the ledger name; Matching the characters in the first data set with the target name of the classification directory to obtain a first matching number; Calculating the number of characters in the first data set and the first matching number to obtain a first relevance; Constructing a second data set; the second data set includes words composed of the characters in the first data set; Matching the words in the second data set with the target name of the classification directory to obtain a second matching number; Calculating the number of words in the second data set and the second matching number to obtain a second relevance; Calculating the first weight coefficient, the second weight coefficient, the first correlation degree, and the second correlation degree to obtain a target correlation degree; The first weight coefficient is the correlation between the characters in the first data set and the target name of the classification directory; the second weight coefficient is the correlation between the words in the second data set and the target name of the classification directory.

4. The team record management system according to claim 3, characterized in that: The process of constructing the second data set includes: S100, presetting the initial value of the number of characters to be extracted to M; wherein M=2; S200, extracting M characters from the first data set; S300, combining the M extracted characters to obtain a word; S400, repeat S200 and S300 until the first data set is traversed, then jump to S500; S500, setting the loop value of the number of characters to be extracted to N; wherein N=M+1; S600, replace the initial value M with the loop value N, and jump to S200; S700, repeat S500 and S600 until the loop value N is greater than the number of characters in the first data set, to obtain the second data set.

5. The team record management system according to claim 3, characterized in that: Before constructing the first data set, the method further includes: Clean the target characters in the ledger name.

6. The team record management system according to claim 1, characterized in that: The classification information includes the department to which the ledger belongs; The team ledger management system also includes a first judgment module, which is used to determine whether the user information belongs to the department to which it belongs. If so, the user is allowed to view the ledger under the classification directory and store the user information in the classification directory; if not, the user is not allowed to view the ledger under the classification directory.

7. The team record management system according to claim 6, characterized in that: The query information includes the query start time and the query end time; The team record management system also includes a calculation module and a second judgment module; The calculation module is used to calculate the user's browsing time according to the browsing start time and the browsing end time; The second judgment module is used to determine whether the browsing time is greater than or equal to the time threshold. If so, the user information and the browsing end time are stored in the classification directory and assigned a first browsing identifier; if not, the user information and the browsing time are stored in the classification directory and assigned a second browsing identifier.

8. The team record management system according to claim 7, characterized in that: The classification information also includes the learning type of the ledger; The team record management system also includes a third judgment module, which is used to judge whether the learning type of the record is a compulsory course. If so, a prompt message is generated to prompt the user corresponding to the second reference identifier; if not, no prompt message is generated.

Images