A team ledger management system

Abstract

The application discloses a kind of team account management system, it is related to account management technical field.The main technical scheme: including storage module, first information input module and second information input module, database is built in storage module, multiple classification directories are arranged in database;The first information input module is used to obtain the account information of the account to be stored, and according to the account information, the corresponding classification information of the account to be stored is given, and the first information input module is also used to store the account to be stored with classification information to the corresponding classification directory according to the account information;The second information input module is used to obtain the user information of user, and according to the information of the account under the classification directory consulted by user, user information and consultation information are stored to classification directory.It is expected to reduce the risk of account information confusion, repeated storage, omission and the like, so as to reduce the error rate when team obtains learning content, to improve the purpose of training effect and management efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of account management, and particularly relates to a team account management system. BACKGROUND

[0002] In traditional team learning and team activities, offline centralized training and book materials are mainly relied on, the learning form is relatively single, the courseware display means is limited, and there are time and space constraints. At the same time, the training content source is relatively fixed, and is mostly general and universal courseware, lacking personalized customized content for different posts and different types of work, resulting in insufficient training pertinence and professionalism, and being difficult to meet the new requirements of modern enterprises on team learning informatization and data management.

[0003] However, the existing team safety knowledge targeted service system is difficult to realize efficient and accurate classification and storage when facing newly entered account information, resulting in chaotic learning resource management, greatly increasing the error rate of the team in obtaining learning content, and reducing the training effect and management efficiency. SUMMARY

[0004] The purpose of the present application is to provide a team account management system, which solves the problem that the existing team safety knowledge targeted service system is difficult to realize efficient and accurate classification and storage when facing newly entered account information, resulting in chaotic learning resource management, greatly increasing the error rate of the team in obtaining learning content, and reducing the training effect and management efficiency.

[0005] To solve the above technical problems, the present application adopts the following technical solutions:

[0006] A team account management system is provided, comprising 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 to obtain account information of a to-be-stored account, and to assign corresponding classification information to the to-be-stored account according to the account information, and the first information input module is also used to store the to-be-stored account with classification information into the corresponding classification directory according to the account information; the second information input module is used to obtain user information of a user, and to store the user information and the search information into the classification directory according to the search information of the user searching the accounts under the classification directory.

[0007] Further, the account information includes an account name; the first information input module is used to obtain a target correlation degree between the account name and the directory name of the classification directory, and to store the to-be-stored account in the classification directory with the largest target correlation degree.

[0008] Further, the method for obtaining the target correlation degree between the account name and the directory name of the classification directory comprises: constructing a first data set; the first data set comprises each character in the account 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 degree; constructing a second data set; the second data set comprises words formed by each character 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 degree; calculating a first weight coefficient, a second weight coefficient, the first correlation degree and the second correlation degree 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; and the second weight coefficient is the correlation between the words in the second data set and the target name of the classification directory.

[0009] Further, the construction process of the second data set comprises: S100, presetting an initial value of the number of extracted characters as M; wherein M=2; S200, extracting M characters from the first data set; S300, combining the extracted M characters to obtain a word; S400, repeating S200 and S300 until the first data set is traversed, and then jumping to S500; S500, setting a loop value of the number of extracted characters as N; wherein N=M+1; S600, replacing the initial value M with the loop value N, and then 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 then obtaining the second data set.

[0010] Further, before constructing the first data set, the method further comprises: performing cleaning processing on the target character in the account name.

[0011] Further, the classification information comprises the department to which the account belongs; and the team account management system further comprises a first judgment module, which is configured to judge whether the user information belongs to the department, and if yes, the user is allowed to check the accounts under the classification directory, and the user information is stored in the classification directory; and if not, the user is not allowed to check the accounts 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] Exemplarily, in the implementation process, a database is built in the storage module, and a classified catalog of safety and environmental protection, management quality, clean and saving, learning and innovation, and humanistic harmony is arranged in the database, for storing corresponding team account books.

[0020] The first information input module is used for obtaining account book information of the to-be-stored account book. The account book information can include account book name, account book content, account book file format, etc. After obtaining the account book information of the account book, the first information input module identifies the content of the account book information, and assigns corresponding classification information to the to-be-stored account book according to the content of the account book information. The classification information includes the department to which the account book belongs and the learning type. The learning type includes types such as no specified learning, compulsory course and elective course. After receiving the classification information of the account book, the first information input module matches the corresponding classified catalog in the database according to the account book information, and stores the account book with the classification information under the corresponding classified catalog in the database.

[0021] The second information input module is used for obtaining user information of a user. The user information includes the name, post, and department to which the user belongs. After obtaining the user information of the user, the second information input module tracks the review information of the user reviewing the account books under the classified catalog. The review information includes review time, review content, and whether to download, etc. After binding the user information and the review information of the user reviewing the account books, the second information input module stores them under the corresponding classified catalog.

[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] Exemplarily, in the implementation process, each single character in the account name is extracted. For example, the account name is "safety operation rules for electric welding work", and the extraction result is "electric, welding, work, safety, operation, rules", and the extraction result is taken as the first data set.

[0027] The characters in the first data set are matched with the target names in the classification catalog to obtain a first matching number.

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

[0029] The number of characters in the first data set and the first matching number are calculated and processed to obtain a first correlation degree.

[0030] Exemplarily, in the implementation process, based on the number of characters in the first data set and the first matching number, the first correlation degree is calculated by a specific algorithm (such as a proportional method or other custom algorithm).

[0031] A second data set is constructed; the second data set includes words composed of characters in the first data set.

[0032] In the present embodiment, the construction process of the second data set includes: S100, presetting the initial value of the number of extracted characters as M; wherein M=2.

[0033] Exemplarily, in the implementation process, the initial value of the number of extracted characters is M, which means that 2 consecutive characters are extracted from the first data set composed of account names each time.

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

[0035] Exemplarily, in the implementation process, M=2 characters are extracted from the first data set. For example, the first data set is: ['electric', 'welding', 'work','safety', 'full', 'operation', 'work', 'rules', 'process'], and the 2 characters extracted from the first data set are "electric" and "safety".

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

[0037] Exemplarily, in the implementation process, the 2 characters "electric" and "safety" extracted from the first data set are combined to obtain the words "electric safety" and "safety electric".

[0038] S400, repeating S200 and S300 until the first data set is traversed, and jumping to S500;

[0039] Exemplarily, in the implementation process, the steps S200 and S300 are repeated until M=2, and the characters in the first data set are traversed. Then, jump to S500.

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

[0041] Exemplarily, in the implementation process, after the word combination of the two characters selected from the first data set is completed, the loop value of the number of characters extracted is set as N. Wherein, 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, in the implementation process, replace M in step S200 with N, and execute steps S200, S30 and S400 again. For example, the three characters extracted from the first data set are "electricity", "safe" and "work". The three characters "electricity", "safe" and "work" extracted from the first data set are combined to obtain words such as "electricity safe work", "safe electricity work", "work electricity safe" and "work safe electricity".

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

[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 the embodiment, the review information includes a review start time and a review end time; the team account management system further includes a calculation module and a second judgment module; the calculation module is configured to calculate the review duration of the user according to the review start time and the review end time; the second judgment module is configured to judge whether the review duration is greater than or equal to a duration threshold, if yes, store the user information and the review end time to the classified catalog and assign a first review identifier; if no, store the user information and the review duration to the classified catalog and assign a second review identifier.

[0058] For example, in the implementation process, the review information includes a review start time and a review end time. The team account management system further includes a calculation module and a second judgment module. When the user attempts to access the account under a certain classified catalog, the calculation module calculates the review duration of the user according to the review start time and the review end time. The review duration = review end time - review start time.

[0059] The duration threshold is set according to the actual situation or historical data analysis. The second judgment module compares the review duration with the preset duration threshold. If the review duration is greater than or equal to the duration threshold, it is considered that the user has fully learned the account, a first review identifier (such as "has learned, learning completed, etc.") is assigned, and the user information and the review end time are stored to the classified catalog. If the review duration is less than the duration threshold, it is considered that the user has only made a preliminary browsing, a second review identifier (such as "quickly browse, learning not completed, etc.") is assigned, and the user information and the review duration are stored to the classified catalog.

[0060] For example, assume that a table account named "Electric Welding Operation Safety Operation Procedures" is classified under the "Safety and Environmental Protection" category and is marked as belonging to the "Mechanical Team 2" department. User A (belonging to the Mechanical Team 2) logs into the system and attempts to access "Electric Welding Operation Safety Operation Procedures" with a start time of 15:00 on July 25, 2025, and an end time of 15:30 on July 25, 2025. The calculation module calculates that the reading duration is 30 minutes, assuming that the duration threshold is 20 minutes. At this time, the second judgment module confirms that the reading duration exceeds the threshold, so the first reading identification is assigned as "has learned", and the user information of user A and the end time of reading are stored in the category. User B (belonging to the Mechanical Team 2) logs into the system and attempts to access "Electric Welding Operation Safety Operation Procedures" with a start time of 16:00 on July 25, 2025, and an end time of 16:10 on July 25, 2025. The calculation module calculates that the reading duration is 10 minutes. At this time, the second judgment module confirms that the reading duration does not reach the duration threshold, so the second reading identification is assigned as "quickly browse", and the user information and reading duration are stored in the category. On the one hand, it is expected to be able to more accurately understand the learning depth of employees on different table accounts, providing data support for subsequent behavior analysis. On the other hand, by assigning different reading identifications, it is possible to better understand the learning patterns and preferences of users, with the expectation of facilitating resource allocation and personalized recommendations. On the other hand, based on the evaluation index of reading duration: help managers evaluate the effectiveness of training materials and the learning situation of employees, with the expectation of ensuring that each employee can accumulate enough knowledge.

[0061] In the above embodiment 4, as shown in the following embodiment 5, the classification information further includes the learning type of the table account; the team table account management system further includes a third judgment module, the third judgment module is used to judge whether the learning type of the table account is compulsory course, if yes, generate prompt information to prompt the user corresponding to the second reading identification; if not, do not generate prompt information. Figure 1

[0062] ​Exemplarily, in the implementation process, the above-mentioned classification information further includes a learning type of the account. The team account management system further includes a third judgment module, which is configured to judge whether the learning type of the account is a compulsory course, and determine whether to generate a prompt information according to the judgment result. The learning type includes a compulsory course, an elective course and no designation. When the user is marked as the second review identifier, the third judgment module judges whether the learning type of the account is a compulsory course. If the account is a compulsory course, a prompt information is generated to remind the user represented by the second identifier to further learn the account. If the account is not a compulsory course, no prompt information is generated, and the user of the second review identifier is not reminded. On the one hand, for the account marked as a compulsory course, the system will pay special attention to the review situation of the user, ensure that the employee fully learns the relevant content, and expect to achieve the purpose of reducing the risk of missing important knowledge due to negligence. On the other hand, by generating the prompt information, the user who only conducts preliminary browsing is urged to learn the relevant content again. It is expected to achieve the purpose of improving the overall training effect and quality.

[0063] While the application has been described with reference to the exemplary embodiments thereof, it is to be understood that the application is not limited to the embodiments disclosed, but is intended to cover numerous other modifications thereof. More generally, many variations and modifications will be apparent to those skilled in the art from the disclosure, the drawings and the claims. Other uses will 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; A second information entry module, the second information entry module is used to obtain user information of the user, and store the user information and the referenced information in the classification directory according to the referenced information of the ledger under the classification directory by the user; 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; 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 correlation degree; 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.

2. The team record management system according to claim 1, 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.

3. The team record management system according to claim 1, characterized in that: Before constructing the first data set, the method further includes: Clean the target characters in the ledger name.

4. 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.

5. The team record management system according to claim 4 is 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.

6. The team record management system according to claim 5, 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