An instant messaging system for automatically generating group chats based on the organizational structure hierarchy

Abstract

The present invention relates to the technical field of instant messaging systems, and particularly relates to an instant messaging system for automatically generating group chats based on the organizational structure hierarchy, including an external docking module for obtaining organizational structure data from an external system; a parsing module for parsing the organizational structure data obtained by the external docking module to generate an address book framework and generating a personnel data packet for each person within the organizational structure; a user management module for establishing an address book with group management and generating a to-be-joined member within the corresponding group of the address book according to the personnel data packet; a registration module for user registration and registration review, and during the registration review stage, matching the registration information with the personnel data packet; a group creation module for automatically generating a department communication group for internal use by departments, a management communication group for management use, and a project communication group generated according to project documents based on the groups of the address book. It realizes the automated and intelligent management of group chats.

Description

Technical Field

[0001] The present invention relates to the technical field of instant messaging systems, and particularly to an instant messaging system for automatically generating group chats based on an organizational structure hierarchy. Background Art

[0002] With the expansion of enterprise scale and the complexity of organizational structure, the efficiency and effectiveness of internal communication become particularly important. In large enterprises or complex organizations, there are numerous employees and a complex hierarchical structure. Traditional instant messaging tools often require manual creation and management of a large number of group chats to meet the communication needs between different departments, teams, and levels. This method is not only time-consuming and laborious but also prone to information omission or mistransmission due to poor management. Therefore, developing a system that can automatically generate group chats based on the organizational structure is of great significance for improving the efficiency of internal communication within the organization. Summary of the Invention

[0003] The purpose of the present invention is to provide an instant messaging system for automatically generating group chats based on an organizational structure hierarchy, which can automatically generate group chats according to the organizational structure hierarchy and realize the automated and intelligent management of group chats.

[0004] To solve the above technical problems, the present invention adopts the following technical solutions:

[0005] An instant messaging system for automatically generating group chats based on an organizational structure hierarchy includes an external docking module for obtaining organizational structure data from an external system; a parsing module for parsing the organizational structure data obtained by the external docking module to generate an address book framework and identifying each person within the organizational structure to generate a personnel data packet for each person; a user management module for establishing an address book with group management based on the address book framework and generating a to-be-added member within the corresponding group of the address book according to the personnel data packet; a registration module for user registration and registration review. During the registration review stage, the registration information is matched with the personnel data packet. If the match is successful, the registration is automatically successful. If the match fails, manual review is required. After successful registration, the to-be-added member within the corresponding group is automatically replaced; a group creation module for automatically generating an internal department communication group for department use, a management communication group for management use, and a project communication group generated according to project documents based on the groups of the address book.

[0006] A further technical solution is that the user management module includes a user database and an address book unit. When importing a personnel data packet, the user database generates a personal information table according to the personnel data in the packet. When importing a personnel data packet, the address book unit generates a to-be-added member according to the personnel data in the packet. The to-be-added member is bound to the personal information table. After the user registers and successfully matches with the to-be-added member, the user account replaces the to-be-added member and is bound to the corresponding personal information table. The address book unit is used to manage the address book and perform operations on the address book.

[0007] A further technical solution is that the personal information table includes user information, skill information, project experience value, and current load factor.

[0008] A further technical solution is that the specific steps for the group creation module to generate a project communication group according to a project file include: Step S1, import the project file into the group creation module, and the group creation module extracts and identifies the fields of the file; Step S2, extract the personnel configuration requirements and project start and end times from the project file; Step S3, screen the personal information tables from the user database and draw up a personnel list; Step S4, the group creation module sends the drawn-up personnel list to the project leader; Step S5, after the project leader confirms the list, automatically create a project communication group according to the list; Step S6, after the project ends, the group creation module exports all the chat records in the group to form a communication record document, and saves the communication record document and all the files in the project communication group in the same folder.

[0009] A further technical solution is that the project experience value is dynamically adjusted according to the projects participated in, according to the formula , is the total experience value of small projects, is the total experience value of medium projects, is the total experience value of large projects, through the formula , where , , are the participation times, , , are the position coefficients in the project, takes 0.1, takes 0.2, takes 0.3. If is greater than 2, then takes 2; through the formula , where , , is the number of participations, , , are the position coefficients in the project takes 0.2, takes 0.4, takes 0.6, if is greater than 4, then takes 4; Through the formula , where , , are the number of participations, , , are the position coefficients in the project, takes 0.3, takes 0.6, takes 0.9, if is greater than 10, then takes 10.

[0010] A further technical solution is that the current load factor is dynamically adjusted according to the formula , where is the number of overlapping days between the currently participated Project 1 and the planned participated project, is the difficulty coefficient of Project 1, is the number of overlapping days between the currently participated Project 2 and the planned participated project, is the difficulty coefficient of Project 2, is the number of overlapping days between the currently participated Project n and the planned participated project, is the difficulty coefficient of Project n, is the total number of days of the planned participated project, is the difficulty coefficient of the planned participated project; , , , The values of all are between 0.1 - 0.9. If ≥0.95, it represents full load. If 0.7 < <0.95, it represents normal load. If 0.1 < ≤0.7, it represents light load.

[0011] A further technical solution is that the project document stipulates the value range of the project experience value of different members, as well as the skill requirements. When drawing up the personnel list, a main list and an alternative list are drawn up. When drawing up the main list, first match those who meet the skill requirements, project experience value and load rate Those with a value less than 0.95, and then according to the empirical value Screen from largest to smallest to determine the main list; when formulating the alternative list, first match those who meet the skill requirements and project experience value and load rate Those with a value less than 0.95, and then according to the load rate Screen from smallest to largest to determine the alternative list.

[0012] A further technical solution is that the personnel data packet includes name, gender, department, and position. When the user registers through the registration module, they need to fill in their name, gender, department, and position.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: This system directly extracts the organizational structure data from other systems, analyzes the personnel in the organizational structure, and generates a personnel data packet for each person in the organizational structure to manage the personnel. When the user registers an account in this system, it will automatically match with the personnel data packet, and after successful matching, it will be automatically assigned to the corresponding group in the address book, as well as the corresponding department communication group, management communication group, project communication group, etc. This avoids the situation of missing adding group members when adding manually, which may lead to untimely message transmission and communication, and is of great significance for improving the internal communication efficiency of the organization. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the framework of Embodiment 1 of the present invention.

[0015] Figure 2 It is the specific steps for generating a project communication group according to the project file in Embodiment 1 of the present invention.

[0016] Figure 3 It is a flowchart of Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0018] Embodiment 1:

[0019] Such as Figure 1 and Figure 2As shown in the figure, an instant messaging system for automatically generating group chats based on the organizational structure hierarchy includes an external docking module for obtaining organizational structure data from an external system; a parsing module for parsing the organizational structure data obtained by the external docking module to generate an address book framework, and identifying each person within the organizational structure to generate a personnel data packet for each person; a user management module for establishing an address book with group management according to the address book framework, and generating a member-to-be-added within the corresponding group of the address book based on the personnel data packet; a registration module for user registration and registration review. During the registration review stage, the registration information is matched with the personnel data packet. If the match is successful, the registration is automatically successful. If the match fails, manual review is required. After successful registration, the member-to-be-added within the corresponding group is automatically replaced; a group creation module for automatically generating a department communication group for internal use within the department, a management communication group for management use, and a project communication group generated according to project documents based on the groups of the address book. This system directly extracts organizational structure data from other systems, parses the personnel in the organizational structure, and generates a personnel data packet for each person in the organizational structure to manage personnel. When a user registers an account in this system, it will automatically match with the personnel data packet. After a successful match, it will be automatically assigned to the corresponding group in the address book, as well as the corresponding department communication group, management communication group, project communication group, etc. This avoids the situation of missing adding group members due to manual addition, resulting in untimely message transmission and communication, and is of great significance for improving the internal communication efficiency of the organization. The external docking module can use an API interface to dock with other systems to obtain the organizational structure data of other systems, and analyze the graphics and text through the parsing module to obtain the personnel data within the organizational structure data.

[0020] The user management module includes a user database and an address book unit. When the user database imports the personnel data packet, it generates a personal information table according to the personnel data within the personnel data packet. When the address book unit imports the personnel data packet, it generates a member-to-be-added according to the personnel data within the personnel data packet. The member-to-be-added is bound to the personal information table. When the user registration is successful and the match with the member-to-be-added is successful, the user account replaces the member-to-be-added and is bound to the corresponding personal information table; the address book unit is used to manage the address book and operate on the address book. Specifically, when the address book unit creates the address book, since no user has registered, the members within the address book at this time are all personnel generated according to the organizational structure data, that is, the members-to-be-added. The members-to-be-added are displayed in light gray or other light colors within the address book. After the user registers an account and completes the match with the member-to-be-added, the corresponding member-to-be-added will be replaced.

[0021] The personal information table includes user information, skill information, project experience value, and current load factor. Based on this information, each user's personal information table can be retrieved when the project communication group is established to determine which projects are suitable for participation and screen these people.

[0022] The specific steps of the group creation module generating a project communication group based on the project file include: step S1, importing the project file into the group creation module, and the group creation module extracting and identifying fields from the file; step S2, extracting the personnel configuration requirements and project start and end time in the project file, which is helpful for later judging the load rate of each employee and thus judging whether they are suitable for participating in the project; step S3, screening the personal information table from the user database and drawing up a personnel list; step S4, the group creation module sends the drawn-up personnel list to the project leader. When each project file is released, the project leader will be determined in advance in the project file, and the entire project will be managed by the project leader; step S5, after the project leader confirms the list, the project communication group is automatically created according to the list, which can avoid the trouble of manually pulling the group and avoid missing someone and causing the employee to miss some information; step S6, after the project is completed, the group creation module exports all chat records in the group to form a communication record document, and saves the communication record document and all files in the project communication group in the same folder. After the project is completed, in order to avoid group files and group chat content being left unmanaged for a long time, which may easily lead to information leakage or leaks, all group chat records should be exported after the project is completed. The chat records can be saved in a format that can be read by text or specific software, which can meet the needs of later review and protection of group information.

[0023] The project experience value Dynamically adjust according to the projects you have participated in, the project experience value Dynamic adjustment based on the projects participated in, according to the formula , is the total experience score of small projects, is the total experience score of the medium-sized project, is the total experience score of large projects, By formula ,in 、 、 is the number of participations, 、 、 is the position coefficient in the project, Take 0.1, Take 0.2, Take 0.3, if Greater than 2 Take 2; Through the formula , where , , is the number of participation times, , , is the position coefficient in the project takes 0.2, takes 0.4, takes 0.6. If is greater than 4, then takes 4; Through the formula , where , , is the number of participation times, , , is the position coefficient in the project, takes 0.3, takes 0.6, takes 0.9. If is greater than 10, then takes 10. By setting upper limits for the scores of small projects, medium-sized projects, and large projects, a project threshold can be formed through the total score to avoid unlimited increase in scores. For example, if the total score is not higher than two points, it means that the medium-sized project or large project has not been participated in. If the total score is not higher than six points, it means that the large project has not been participated in.

[0024] The current load factor is dynamically adjusted according to the formula , where is the overlapping days between the currently participated Project 1 and the planned project, is the difficulty coefficient of Project 1, is the overlapping days between the currently participated Project 2 and the planned project, is the difficulty coefficient of Project 2, is the overlapping days between the currently participated Project n and the planned project, is the difficulty coefficient of Project n, is the total days of the planned project. When calculating the days, each day is calculated based on an eight-hour working duration, is the difficulty coefficient of the planned project; , , , The values of all take values between 0.1 - 0.9. The difficulty coefficient is judged according to the experience of the project document writer in the project document to set the corresponding difficulty coefficient. If ≥0.95 represents full load, and exceeding 1 means overtime is required to complete. Project work is only arranged under full load in special cases. If 0.7 < < 0.95, it represents normal load. If 0.1 < ≤0.7, it represents light load.

[0025] The project documents stipulate the value ranges of the project experience values of different members and skill requirements. When drawing up the personnel list, a main list and an alternative list are drawn up. When drawing up the main list, first match the personnel who meet the skill requirements, project experience values and load rates less than 0.95, and then screen them from large to small according to the experience values to determine the main list. The priority for determining the main list is to select experienced employees to participate in the project among the personnel who meet the options, so as to better complete the project. This is generally suitable for projects with a relatively large difficulty coefficient and requires members to have rich experience; when drawing up the alternative list, first match the personnel who meet the skill requirements, project experience values and load rates less than 0.95, and then screen them from small to large according to the load rates to determine the alternative list. The priority for determining the alternative list is to select employees with a small load rate among the personnel who meet the options. Generally, employees with less experience values are selected for such projects. Generally, the project difficulty coefficient is small, which is conducive to training employees with less experience and accumulating project experience.

[0026] The personnel data packet includes name, gender, department, and position. When users register through the registration module, they need to fill in their name, gender, department, and position.

[0027] Embodiment 2:

[0028] As Figure 3 shown, a specific instant messaging method for automatically generating group chats based on the organizational structure hierarchy in this embodiment may specifically include:

[0029] S101. Obtain the organizational structure hierarchy data and employee role attributes through the enterprise database interface, parse the department, position, and project participation degree, generate a structured data set including hierarchical identifiers and function labels, and obtain a real-time architecture snapshot.

[0030] The database interface is used to retrieve organizational hierarchical data and employee role information. Department, position, and project participation are parsed to generate a structured dataset containing hierarchical identifiers and functional labels, yielding a real-time architectural snapshot. If the hierarchical data returned by the database interface contains department and position information, a JSON parser is used to extract the hierarchical identifiers and employee role attributes from the organizational structure, yielding an initial structured dataset. Based on the department information and position responsibilities in the initial structured dataset, employee roles are associated with functional labels using pre-set mapping rules, yielding a functionally labeled dataset. The project participation field in the functionally labeled dataset is used to obtain the employee participation percentage for each project. A weighted average algorithm is used to calculate the overall project participation for each department, yielding a departmental participation index. If the departmental participation index falls below a pre-set threshold, the database interface is used to retrieve the department's real-time hierarchical data, and the hierarchical identifiers in the structured dataset are updated to yield an updated dataset. Based on the hierarchical identifiers and functional labels in the updated dataset, a K-means clustering algorithm is used to group employee roles, generating an architectural snapshot containing grouping information, yielding a classified architectural dataset. By classifying the hierarchical identifiers of each group in the architecture dataset, the real-time hierarchical relationship of the organizational structure is parsed, and a structured snapshot containing hierarchical identifiers, functional labels, and project participation is generated to obtain the final real-time architecture snapshot.

[0031] When retrieving organizational structure data through a database interface, imagine a medium-sized tech company whose database stores information such as departments, positions, employee roles, and project participation. The data returned by the interface is presented in JSON format and includes department names such as "R&D Department," hierarchical identifiers such as "First-Level Department," and employee positions such as "Front-End Engineer." A JSON parser extracts these fields to generate an initial structured dataset.

[0032] For example, the R&D department's hierarchy is labeled L1, employee Zhang's role is "Front-End Engineer," and his project participation is 60% for Project A and 40% for Project B. This initial dataset clearly presents the organizational hierarchy and employee role attributes, laying the foundation for subsequent analysis.

[0033] In one possible implementation, based on the department and position information in the initial dataset, preset mapping rules are used to associate employee roles with functional labels. For example, the rules define "Front-End Engineer" as corresponding to the "Technical Development" label, and "Product Manager" as corresponding to the "Product Planning" label. Through this mapping, Mr. Zhang is labeled "Technical Development," and his functional label is associated with his departmental responsibilities, generating a dataset labeled with functional labels. This mapping ensures consistency between roles and functions, improving the accuracy of data analysis.

[0034] Specifically, project participation analysis is based on the participation ratio field in the labeled dataset.

[0035] For example, Zhang's participation rates in Projects A and B are 60% and 40% respectively, while Li's participation rates in Projects A and B are 80% and 20% respectively. Through the weighted average algorithm, the overall project participation rate of the R & D department is calculated. Assuming that there are 10 employees in the R & D department, the average participation rate is 55% for Project A and 45% for Project B. If the preset threshold is 60%, the participation rate of the R & D department in Project A is lower than the threshold, triggering the re - acquisition of the real - time data of this department. This mechanism ensures that the data reflects the latest status and avoids analysis deviation.

[0036] If an update is triggered, the database interface returns the latest hierarchical data of the R & D department. For example, a new "Front - End Group" is added as the L2 level. After the update, the data set reflects the new hierarchical identifier, maintaining the real - time nature of the architecture snapshot. Based on the updated data set, the K - means clustering algorithm is used to group employees by role.

[0037] For example, according to the function labels and participation rates, the algorithm divides employees into a "Technical Development Group" and a "Product Support Group". Zhang is assigned to the Technical Development Group, and the grouping information is incorporated into the architecture snapshot to generate a classified architecture data set. This clustering helps to identify role similarities and optimize resource allocation.

[0038] In one embodiment, the hierarchical identifiers of the classified architecture data set are parsed to generate the final real - time architecture snapshot.

[0039] For example, the architecture snapshot shows that the R & D department has a Front - End Group under it. Zhang's function label is "Technical Development", and his participation rate in Project A is 60%. The snapshot presents the hierarchical relationship, function labels, and participation rates in a structured form, facilitating managers to quickly understand the organizational status. This snapshot supports dynamic adjustment of team configurations and improves project management efficiency.

[0040] It can be understood that each step of the above - mentioned method is carried out around the goals of real - time nature and structurization. JSON parsing ensures efficient data extraction, mapping rules enhance function associations, participation rate analysis reveals departmental activity levels, K - means clustering optimizes role management, and the final snapshot provides a comprehensive view. These technical effects jointly support the dynamic monitoring and optimization of the organizational structure, assisting enterprise decision - making.

[0041] S102. According to the real - time architecture snapshot, use the hierarchical recursive algorithm to traverse the organizational architecture tree, generate a unique group chat identifier for each hierarchical node, and associate department and project attributes to obtain a dynamic group chat framework.

[0042] The hierarchical recursive algorithm is used to traverse the organizational structure tree, obtain the node hierarchy information from the real-time snapshot, and generate a set of hierarchical nodes. Through the set of hierarchical nodes, a unique group chat identifier is generated for each node to obtain an identifier set. According to the identifier set, department attributes and project attributes are obtained, and an attribute mapping table is generated. If there is an association between the department attribute and the project attribute in the attribute mapping table, the corresponding attributes are bound through the identifier set to obtain a set of group chat attributes. The dynamic generation rules are extracted from the set of group chat attributes, and a preset framework template is used to generate an initial group chat framework. For the initial group chat framework, the update information in the real-time snapshot is obtained. If the update information includes changes in the node hierarchy, the set of group chat attributes is adjusted to obtain a dynamic group chat framework.

[0043] When traversing the organizational structure tree using the hierarchical recursive algorithm, the core is to access the nodes of the organizational structure layer by layer in a recursive manner to obtain the hierarchy information of each node.

[0044] For example, in the organizational structure of an enterprise, assuming that the structure tree includes three levels: headquarters, department, and team, the recursive algorithm starts from the headquarters node, traverses layer by layer to the team node, and records the hierarchy depth of each node. For example, the headquarters is at level 1, the department is at level 2, and the team is at level 3.

[0045] Exemplarily, a certain department node may contain multiple teams. The algorithm will recursively access each team and generate a set containing the node name and hierarchy depth, such as {"Finance Department": 2, "Budget Team": 3}. This method ensures the integrity of the hierarchy information and is applicable to dynamically changing architectures.

[0046] In a possible implementation, a unique group chat identifier is generated for each node through the set of hierarchical nodes. Assuming that an enterprise needs to create internal communication group chats for each department and team, the unique identifier can be generated based on the combination of the node name and hierarchy depth.

[0047] Specifically, the identifier of the Finance Department may be "FIN_L2_001", where "FIN" represents the Finance Department, "L2" represents level 2, and "001" is the serial number. The identifier of the Budget Team may be "FIN_BUD_L3_001". These identifier sets provide a basis for subsequent attribute binding to ensure a one-to-one correspondence between the group chat and the node.

[0048] It should be noted that obtaining department attributes and project attributes and generating an attribute mapping table involve extracting node-related information from the database.

[0049] For example, the department attributes of the Finance Department may include "Function: Financial Management, Staff Size: 50", and the project attributes may be "Participated Projects: Annual Budget Preparation, Participation Rate: 80%". The attribute mapping table associates this information in the form of {"FIN_L2_001": {"Function": "Financial Management", "Project": "Annual Budget Preparation"}}. This mapping clearly shows the multi-dimensional information of the nodes.

[0050] In one embodiment, if the attribute mapping table shows the association between the department and the project, a group chat attribute set is generated by binding attributes through an identification set.

[0051] The group chat attribute set of the Finance Department may include "Group Chat Topic: Financial Management, Associated Project: Budget Preparation". This binding ensures that the group chat function aligns with the actual business needs, such as facilitating project communication.

[0052] It can be understood that the dynamic generation rule extracts information from the group chat attribute set, such as setting the notification frequency of the group chat based on the project participation rate to generate an initial group chat framework.

[0053] For example, if the participation rate of the budget preparation project is high, the group chat framework can be set to a high-frequency notification mode.

[0054] Specifically, the initial group chat framework is generated based on a preset template, which may include the group chat name, member list, notification rules, etc. Assuming the template stipulates that the group chat name is "Department_Project", the group chat name of the Finance Department is "Finance Department_Budget Preparation". If the real-time snapshot shows a change in the node hierarchy, such as the budget team being promoted to an independent department, the group chat attribute set is dynamically adjusted, updated with the identifier "BUD_L2_001", and a new group chat framework is generated. This dynamic adjustment ensures that the group chat is synchronized with the architecture and adapts to the changes in the enterprise.

[0055] S103. If the employee role attribute or the project participation rate changes, then by comparing the real-time architecture snapshot with the historical snapshot, identify the newly added, removed, or adjusted employee nodes to generate a change event set.

[0056] By periodically obtaining real-time architecture snapshots and historical architecture snapshots, using a data consistency verification algorithm to detect differences between snapshots, and obtaining a set of changes in employee node status. If a change in node status is detected, according to the node status detection rules, newly added employee nodes, removed employee nodes, and adjusted employee nodes are classified to generate a preliminary set of change events. Using a preset event generation rule, attribute mapping is performed on the preliminary set of change events to obtain a structured set of change events containing employee role attributes and project participation. Through snapshot comparison and analysis, the matching degree between the set of change events and the historical snapshot is calculated to determine the integrity of the set of change events. If the matching degree is lower than the preset threshold, the real-time architecture snapshot and the historical snapshot are iteratively compared to supplement the missing changes in employee node status, obtaining an updated set of change events. According to the updated set of change events, a data consistency verification algorithm is used to detect the consistency between the set of change events and the real-time architecture snapshot, obtaining a final set of change events. By storing the final set of change events in a preset database and using index optimization technology, a queryable change event log is generated.

[0057] Exemplarily, when there is a change in employee role attributes or project participation, the system will capture a snapshot of the current organizational architecture in real time. For example, the current project team consists of 5 members (Employee A, B, C, D, E), where Employee A's role is a development engineer with a participation rate of 80%, and Employee B is a test engineer with a participation rate of 50%. The system compares the current snapshot with the historical snapshot (such as data 24 hours ago). The historical snapshot shows that the project team had 4 members (Employee A, B, C, D), and Employee A's role was a junior development engineer with a participation rate of 60%. Through a difference analysis algorithm (such as a node matching algorithm based on the Levenshtein distance), it is identified that Employee E is a newly added node, and Employee A's role has changed from a junior development engineer to a development engineer, and the participation rate has increased from 60% to 80%. The system encapsulates these changes into a set of change events, for example, generating an event record in JSON format: {"Newly Added Employees": [{"id": "E", "Role": "UI Designer", "Participation Rate": 30%}], "Role Adjustment": [{"id ": "A", "Old Role": "Junior Development Engineer", "New Role": "Development Engineer"}], "Participation Rate Change": [{"id": "A", "Old Value": 60%, "New Value": 80%}]}. At the same time, the system will trigger an associated business rule engine. For example, when the participation rate increases by more than 20%, the project manager will be automatically notified, and the organizational relationship graph will be updated in real time through a graph database (such as Neo4j) to ensure the final consistency of the architecture data.

[0058] S104. For the change event set, obtain the employee function tags and project participation weights, and use the allocation algorithm based on weighted matching to calculate the matching degree between the employee and the group chat identifier, so as to obtain the personnel allocation plan.

[0059] Obtain the employee identifier, employee function tags and project identifier, extract the corresponding project participation data from the preset database, calculate the participation weight, and obtain the employee participation weight set. Through the group chat identifier, obtain the group chat attributes from the preset database, and combine the employee function tags. Using the weighted matching algorithm, calculate the matching degree score between the employee and the group chat identifier to obtain the matching degree score set. If the matching degree score is higher than the preset threshold, then generate a preliminary personnel allocation plan according to the matching degree score and the employee identifier to obtain the preliminary allocation plan set. According to the preliminary allocation plan set, combine the project identifier and the group chat attributes to verify the relevance between the allocation plan and the project participation degree, and obtain the verified allocation plan set. Through the verified allocation plan set, use the data consistency check algorithm to detect the consistency between the allocation plan and the employee function tags to obtain the final allocation plan set. Obtain the final allocation plan set, combine the plan record attributes, generate a structured plan record, and store it in the preset database to obtain a queryable allocation plan log. For the final allocation plan set, extract the employee identifier and the group chat identifier from the preset database, generate an association mapping table, and store it in the preset database to obtain a traceable allocation relationship set.

[0060] Exemplarily, when extracting the employee identifier, function tags and project identifier from the preset database, assume that the database stores the information of employees A, B, and C, where the function tag of A is front-end development, B is back-end development, C is a product manager, and the project identifier is P1. After extracting the participation data, the participation degree of A in P1 is 70%, B is 60%, and C is 40%. When calculating the participation weight, the weight coefficient can be set based on the function importance. For example, the weight of front-end development is 0.4, the back-end is 0.3, and the product manager is 0.2. Combining the participation degree to generate the weight set: the weight of A is 70%×0.4 = 0.28, B is 0.18, and C is 0.08. This method ensures the comprehensive consideration of functions and participation degrees, and the generated weight set reflects the actual contribution degree of employees to the project.

[0061] In a possible implementation manner, extract the group chat attributes through the group chat identifier. Assume that the attribute of group chat G1 is technical discussion, and front-end and back-end developers are required to participate. Combine the employee function tags and use the weighted matching algorithm to calculate the matching degree score.

[0062] For example, the matching degree of A (front-end development) with G1 is 0.9, B (back-end development) is 0.85, and C (product manager) is 0.3. Setting the matching degree threshold at 0.7, the scores of A and B are higher than the threshold, generating a preliminary allocation plan: A and B are allocated to G1. This matching method ensures a high degree of relevance between group chat requirements and employee functions.

[0063] Specifically, when verifying the relevance between the preliminary allocation plan and project participation, check whether the participation of A and B in P1 (70% and 60% respectively) meets the technical discussion requirements of G1. If G1 requires the total participation to exceed 100%, then the 130% of A and B meets the condition, and the plan passes the verification. Otherwise, the allocation needs to be adjusted. This verification method ensures the coordination between the allocation plan and project requirements.

[0064] Preferably, a data consistency verification algorithm is used to detect the consistency between the final allocation plan and function labels.

[0065] For example, if the plan mistakenly allocates C (product manager) to the technical discussion group chat G1, the verification algorithm will identify that the function of C does not match the requirements of G1 and eliminate this allocation, ensuring that the final plan set only contains the allocations of A and B. This verification method improves the accuracy and reliability of the allocation.

[0066] For example, when generating a structured plan record, store the final allocation plan (A and B are allocated to G1) in JSON format, including attributes such as employee identification, group chat identification, and allocation time, and store it in the database to form a queryable log. This recording method facilitates subsequent traceability and auditing, ensuring the transparency of the allocation process.

[0067] In one embodiment, when generating an association mapping table, extract the corresponding relationships between A, B and G1 from the final plan and store them as a mapping table: A - G1, B - G1. This mapping table facilitates quick query of the association between employees and group chats, supports dynamic adjustment of allocation relationships, and improves management efficiency.

[0068] It can be understood that the above method forms a complete chain from data extraction to plan generation through weight calculation, matching degree evaluation, relevance verification, and consistency verification. Each link supports each other to ensure that the allocation plan not only meets project requirements but also matches employee functions, and at the same time realizes traceability and efficient management through structured records and mapping tables. This method has high practical value in the dynamic adjustment of the organizational structure.

[0069] S105. Through the message queue mechanism, synchronize the personnel allocation plan to the instant messaging system, update the group chat member list, generate a group chat attribution log containing timestamps, and obtain the updated group chat status.

[0070] Obtain the personnel allocation plan from the message queue, parse the plan content, determine the personnel identifiers and group chat identifiers in the allocation plan, and obtain the group chat member data to be synchronized. Through the instant messaging system interface, perform a member list update operation on the group chat member data to be synchronized. Determine that if the personnel identifier already exists in the group chat member list, retain the original member status; otherwise, add a new member to obtain the updated group chat member list. Adopt a timestamp generation mechanism to generate a group chat attribution log containing timestamps for the updated group chat member list, record member change events, and obtain the group chat attribution log data. Obtain the updated group chat status from the instant messaging system, parse the group chat status data, determine the number of group chat members and the active status, and obtain the current group chat status information. Through the log storage mechanism, write the group chat attribution log data into a preset log database. Determine that if the log writing fails, trigger a retry mechanism until the writing is successful to obtain the log storage confirmation. For the current group chat status information, adopt a status synchronization algorithm to synchronize the group chat status to the message queue. Determine that if the synchronization fails, record the synchronization failure event to obtain the status synchronization result. Obtain the status synchronization result, combine it with the group chat attribution log data, generate a group chat status update record, and store it in a preset group chat status database to obtain the final group chat status update confirmation.

[0071] Exemplarily, when obtaining the personnel allocation plan from the message queue, a message queue service such as Kafka can be used to subscribe to a specific topic such as "allocation plan topic", and extract the JSON format plan data containing personnel identifiers and group chat identifiers from it.

[0072] For example, the plan data may contain the employee ID "E001" and the group chat ID "G101". After parsing, it is determined that the group chat member data to be synchronized is "E001 needs to join G101". This method ensures a clear data structure and facilitates subsequent processing.

[0073] In a possible implementation, when updating the group chat member list through the instant messaging system interface, the enterprise WeChat API can be called and the group chat ID and the list of personnel IDs can be passed in. Assume that the group chat G101 already has a member E002, and the interface checks whether E001 is in the list. If it does not exist, add E001 as a new member; if it already exists, keep it unchanged. After the update, the member list of G101 is E001 and E002. This method ensures the accuracy of the member status and avoids duplicate addition.

[0074] Specifically, when generating the group chat attribution log, a timestamp generation mechanism can be adopted, and based on the current system time such as "2025-04-24 10:00:00", a unique log record is generated for each member change.

[0075] For example, the log record is "E001 joined G101 at 10:00:00 on April 24, 2025". This log facilitates tracing the member change history and improves management transparency.

[0076] Preferably, when obtaining the group chat status, the number of members and the active status of G101 can be queried through the instant messaging system API.

[0077] For example, the API returns that G101 has 2 members and there has been message interaction within the last 7 days, which is determined to be the active status. This information is used to monitor the operation of the group chat and provide a basis for subsequent optimization.

[0078] In one embodiment, when storing the log to the database, MySQL can be used as the log database, and the table structure includes fields such as log ID, group chat ID, change time, etc. If the write fails, such as due to network problems, a retry mechanism can be triggered to retry 3 times every 5 seconds until successful. This method ensures the integrity of the log and avoids data loss.

[0079] For example, when synchronizing the group chat status to the message queue, a status synchronization algorithm can be adopted to push the status data of G101 such as "number of members: 2, active" to the "group chat status topic" of the message queue. If the synchronization fails, such as queue service timeout, the failure event is recorded in the local log file, including the failure time and the group chat ID. This mechanism facilitates problem troubleshooting.

[0080] It can be understood that when generating the group chat status update record, the log data and the status synchronization result can be combined to generate a structured record such as "G101 was updated at 10:00:00 on April 24, 2025, number of members: 2, active". After storing it in the group chat status database, it is convenient to query the historical status changes. This method improves the traceability of status management.

[0081] It should be noted that the above embodiments progress step by step from the core solution to the extended solution.

[0082] For example, the retry mechanism for log storage and the failure record of status synchronization are both optional solutions, which enrich the implementation diversity. Each link supports each other to ensure the efficiency and reliability of the entire process from personnel allocation to group chat status update.

[0083] S106. If the group chat ownership log shows frequent member changes, the sliding window algorithm is used to analyze the change frequency, determine whether it exceeds the preset threshold, and generate an optimization trigger signal.

[0084] Obtain member change records from the group chat membership log, parse the time series data, and obtain the change event count. Process the time series data using a sliding window algorithm, calculate the change frequency within the window, and determine the frequency calculation result. If the frequency calculation result exceeds the preset threshold, generate an optimization trigger signal through the threshold comparison logic. According to the optimization trigger signal, analyze the change event count, extract the high-frequency change patterns, and obtain the pattern analysis result. Cluster the time series data based on the pattern analysis result using the K-means algorithm to determine the abnormal change cluster. If the abnormal change cluster contains high-frequency change patterns, adjust the window size parameter, recalculate the change frequency, and obtain the updated frequency result. Generate an optimization trigger signal based on the updated frequency result, store the signal generation mechanism data, and complete the optimization process.

[0085] Exemplarily, obtaining member change records from the group chat membership log is the basis for analyzing group chat dynamics. For example, in an enterprise instant messaging system, the group chat membership log records all member join and leave events, and each record contains a personnel identifier, a group chat identifier, and a timestamp. When parsing the time series data, the change events within a specified time period can be extracted by sorting the timestamps to obtain the change event count.

[0086] Exemplarily, assume that a certain enterprise group chat records 100 member changes in a week, including 50 joins and 50 exits. This count provides a data basis for subsequent analysis. When processing the time series data using a sliding window algorithm, the window size determines the analysis granularity.

[0087] Specifically, set the window to 24 hours and the step size to 1 hour, and calculate the number of changes within each window.

[0088] In a possible implementation, a certain window records 20 changes, and the frequency is 20 times per day. If the preset threshold is 15 times per day, then this frequency exceeds the threshold and triggers an optimization signal.

[0089] It should be noted that the sliding window algorithm can effectively identify the change trend by smoothing the data fluctuations, providing a basis for dynamically adjusting the group chat management strategy. When generating an optimization trigger signal through the threshold comparison logic, it can be judged whether intervention is needed based on the frequency result.

[0090] Preferably, if the change frequency of a certain group chat is continuously higher than the threshold, it may indicate problems in group chat management, such as frequent personnel adjustments.

[0091] In an embodiment, the system detects that a certain department group chat changes 30 times a day. After the trigger signal is sent, the administrator receives a notification to check whether it is an abnormal operation. This mechanism helps to timely detect potential management chaos. When analyzing the change event count to extract high-frequency change patterns, the repeated change behaviors can be identified.

[0092] For example, a large number of members leave and join a certain group chat every day at a fixed time period, which may be related to job handover.

[0093] In one embodiment, the system discovers that there are 10 exits and joins in a certain group chat at 9 o'clock every day. The pattern analysis result shows that this is a routine personnel adjustment. This pattern recognition provides data support for optimizing group chat management. When clustering time series data, the K-means algorithm is used to discover abnormal change clusters.

[0094] It can be understood that K-means identifies abnormal high-frequency changes by grouping change events according to time and frequency.

[0095] For example, a certain group chat has 50 changes on a certain day, which is much higher than the average level and is classified as an abnormal cluster. This clustering analysis helps to locate abnormal events and facilitates targeted handling by the administrator. If the abnormal change cluster contains a high-frequency change pattern, the window size is adjusted and the frequency is recalculated.

[0096] Specifically, the initial window is 24 hours. After an abnormality is discovered, it can be reduced to 12 hours, and the frequency is recalculated to improve the analysis accuracy.

[0097] In one embodiment, after adjustment, it is found that a certain group chat has 25 changes within 12 hours, and it is confirmed that the abnormality stems from a specific event, such as a project team reorganization. This dynamic adjustment improves the flexibility of the analysis. When generating and storing an optimization trigger signal according to the updated frequency result, the signal data records the frequency, time, and trigger reason.

[0098] For example, the optimization signal of a certain group chat indicates 20 changes within 12 hours, and after storage, it can be used for subsequent auditing. This storage mechanism ensures the traceability of change data and provides a reliable basis for optimizing group chat management.

[0099] S107. According to the optimization trigger signal, adjust the weight parameters of the allocation algorithm, balance the rule flexibility and system performance, and generate a new set of allocation rules.

[0100] Obtain trigger signal data, extract feature values from the original signal through preprocessing to obtain a signal optimization data set. Use a classification algorithm to analyze the signal optimization data set, judge the signal type and intensity, and determine the weight adjustment direction. If the weight adjustment direction is positive, increase the weights related to rule flexibility to generate a first set of weight parameters; if it is negative, increase the weights related to system performance to generate a second set of weight parameters. By fusing the first set of weight parameters and the second set of weight parameters, adjust the weight allocation of the allocation algorithm to obtain an updated allocation algorithm model. According to the updated allocation algorithm model, combined with performance balance constraints, generate a preliminary allocation rule set. For the preliminary allocation rule set, perform a simulation test to obtain the rule execution efficiency and system performance indicators, and judge whether the rule set meets the performance balance requirements. If the rule set meets the performance balance requirements, output the final allocation rule set; if not, return to the weight adjustment step, iteratively optimize the weight parameters to obtain the final allocation rule set.

[0101] Exemplarily, obtain trigger signal data from the group chat attribution log, which involves parsing the timestamps and event types in the log. Assume that the group chat log records events such as member joining and leaving, and each record contains the time and event description. During preprocessing, feature values can be extracted, such as the number of events per hour or the event interval time.

[0102] For example, if a certain group chat has 10 member changes within 1 hour, the feature value can be defined as "hourly change frequency = 10". These feature values form a signal optimization data set for subsequent analysis. The key to preprocessing is to clean invalid data, such as duplicate records, to ensure the accuracy of the data set.

[0103] Specifically, use a classification algorithm to analyze the signal optimization data set and judge the signal type and intensity. The classification algorithm can be a decision tree, which classifies the signal into types such as "high-frequency change" and "low-frequency change" based on the feature values, and evaluates the intensity. For example, "high intensity" means that the change frequency exceeds the threshold.

[0104] For example, a certain data set shows that the frequency in a certain period is 12 times per hour, exceeding the threshold of 8 times per hour, and is classified as "high-frequency high-intensity". This classification result guides the weight adjustment direction and avoids blind adjustment.

[0105] In a possible implementation, if the weight adjustment direction is positive, increase the rule flexibility weight.

[0106] For example, for high-frequency change signals, increase the weight of "dynamically adjusting the group chat member limit" to generate a first set of weight parameters, and the parameter value can be set to 0.7 to emphasize flexibility. If it is negative, increase the system performance weight, such as "limiting the processing priority of frequent changes", to generate a second set of weight parameters, and the parameter value is set to 0.6. This weight setting is based on the signal type to ensure the stability of the system under high load.

[0107] Preferably, the first and second weight parameter sets are fused to adjust the weights of the allocation algorithm.

[0108] For example, through weighted average fusion, a comprehensive weight value of 0.65 is obtained to update the allocation algorithm model. The new model allocates resources according to the weights, such as preferentially processing group chats with high-frequency changes. The fusion process needs to consider the balance between the weights to avoid a single weight dominating.

[0109] For example, combined with performance balance constraints, a preliminary set of allocation rules is generated. The rule set may include "allocate computing resources preferentially to group chats with high-frequency changes" and "delay the processing of low-frequency group chats". During the simulation test, the execution efficiency of the rules is tested, such as the resource allocation taking 0.2 seconds, and system performance indicators such as the CPU occupancy rate being lower than 70%. If the requirements are met, the final rule set is output; if the CPU occupancy rate exceeds the standard, the weights need to be iteratively optimized.

[0110] It should be noted that if the rule set does not meet the performance balance requirements, the weight adjustment step is returned. During iterative optimization, the flexibility weight can be adjusted to 0.75, and the rule set is regenerated until the performance requirements are met.

[0111] For example, after optimizing a certain group chat, the change processing delay is reduced from 1 second to 0.5 seconds, and the system stability is improved. This iterative method ensures the efficient applicability of the rule set.

[0112] It can be understood that for the output of the final allocation rule set, the rule parameters and execution effect data need to be stored.

[0113] For example, record that "Rule Set A reduces the system load by 30%", which is convenient for subsequent analysis. This storage mechanism supports the continuous optimization of the system and adapts to the dynamic changes of group chats.

[0114] S108: Through the real-time monitoring module, obtain the incremental update log of the organizational structure database, parse the events of new employees joining or role adjustments, and generate a supplementary architecture snapshot.

[0115] Establish a connection with the organizational structure database through the real-time monitoring module, use the database connection interface to obtain the real-time data stream, and obtain the incremental update log. If the incremental update log contains new data, extract the events of new employees joining or role adjustments through data parsing and processing, and determine the event type. According to the event triggering mechanism, for the event of a new employee joining, obtain the employee information and generate employee node data. If the event is a role adjustment, extract the role information before and after the adjustment from the log recording system to obtain the role change data. Through the architecture snapshot generation module, combine the employee node data and the role change data to update the organizational structure snapshot and obtain a supplementary architecture snapshot. Use the log recording system to store the supplementary architecture snapshot and related event data, and generate a snapshot version record. Verify the consistency between the snapshot version record and the database through the real-time monitoring module to determine that the snapshot update is completed.

[0116] Exemplarily, establishing a connection between the real-time monitoring module and the organizational structure database is the basis for realizing dynamic data updates.

[0117] Exemplarily, the real-time monitoring module can establish a connection with the database through the JDBC interface to ensure low-latency transmission of data streams. Suppose an enterprise database stores information such as employee IDs, names, and departments. The connection interface polls the database every second to obtain incremental update logs. This approach can capture data changes in a timely manner and ensure the efficiency of subsequent processing.

[0118] In a possible implementation, obtaining incremental update logs depends on the trigger mechanism of the database.

[0119] Specifically, when an insert or update operation occurs in the employee table in the database, the trigger automatically generates a log record containing the operation time, change type, etc.

[0120] For example, when a new employee joins, the log record includes detailed information such as employee ID 1001, name Zhang San, and department Marketing. A role adjustment event records the change of employee ID 1001 from Marketing Specialist to Marketing Manager. This structured storage of logs facilitates subsequent parsing and improves data processing efficiency.

[0121] It should be noted that data parsing and processing need to customize rules according to the event type. For the event of a new employee joining, the parsing module extracts employee information such as age 28 and education background undergraduate, and generates employee node data containing node IDs and attributes.

[0122] Preferably, the node data is stored in JSON format to facilitate rapid updates of the architecture snapshot. For the role adjustment event, the parsing module compares the role information before and after the adjustment, such as from Specialist to Manager, and generates role change data, recording the change time 2025-04-24. This refined parsing ensures the accuracy of event processing.

[0123] In one embodiment, the architecture snapshot generation module generates supplementary architecture snapshots through an incremental update method.

[0124] For example, based on the employee node data, the module adds the new employee Zhang San to the Marketing department node; combining the role change data, updates the role attribute of employee 1001 to Manager. The snapshot is stored in a tree structure to reflect the latest state of the organizational structure. This approach reduces the computational overhead of full updates and improves the snapshot generation speed.

[0125] It can be understood that when the log record system stores supplementary architecture snapshots and event data, a version control mechanism is adopted.

[0126] For example, the snapshot version number is V20250424.01, which records the generation time and the associated event ID. This versioned storage facilitates data traceability and error recovery, enhancing the system's robustness.

[0127] For example, when the real-time monitoring module verifies the consistency between the snapshot version record and the database, it compares the role attributes of employee 1001 in the snapshot with the latest record in the database. If they are consistent, it confirms that the update is complete; if not, it triggers an alarm to notify the administrator for inspection. This verification mechanism ensures the reliability of data synchronization.

[0128] Specifically, the implementation of the above method improves the efficiency of organizational structure management through real-time performance and accuracy.

[0129] For example, after a new employee joins, the architecture snapshot is updated immediately, and the HR system can quickly synchronize the permission configuration; after a role is adjusted, the permission management system synchronizes the changes to avoid permission conflicts. These beneficial effects jointly support the business requirements of dynamic management of the organizational structure.

[0130] S109. For the supplementary architecture snapshot, repeat the hierarchical recursive algorithm and the weighted matching algorithm to update the dynamic group chat framework and the personnel allocation plan, and generate the final group chat state.

[0131] By parsing the architecture snapshot, obtain the initial data of the dynamic group chat to get the group chat members and interaction relationships. Use the hierarchical recursive algorithm to perform recursive processing on the initial data to determine the hierarchical structure of the group chat. According to the hierarchical structure, apply the weighted matching algorithm to calculate the matching weights between members to obtain the personnel allocation plan. Through the personnel allocation plan, update the dynamic group chat framework to generate the adjusted framework configuration. If the adjusted framework configuration meets the preset stability threshold, generate the final group chat state; if not, return to the recursive processing step to recalculate. Extract the member allocation and interaction data from the final group chat state to generate a status description file. For the status description file, perform data verification to confirm the integrity of the group chat state to obtain the final output result.

[0132] Exemplarily, parsing the architecture snapshot to obtain the initial data of the dynamic group chat is an important link in organizational structure management, which involves extracting the group chat members and interaction relationships from the snapshot.

[0133] For example, the architecture snapshot may include the departments, positions, and historical communication records of employees.

[0134] Specifically, the parsing module will scan the node data in the snapshot, extract the member list and their interaction frequencies, such as the average weekly communication times between employee A and employee B is 5 times, and generate an initial data table.

[0135] It should be noted that the initial data needs to ensure coverage of all active members to avoid missing key interaction relationships. The hierarchical recursive algorithm is used to process the initial data to determine the group chat hierarchical structure, and the core lies in the recursive decomposition of member relationships.

[0136] Preferably, the algorithm starts from the department head and traverses the subordinate relationships layer by layer to form a tree structure.

[0137] For example, the department manager node is connected to 3 team leaders, and each leader is further connected to several employees. The recursion is processed until there are no child nodes, generating a group chat structure diagram with clear levels. This method can clearly reflect the superior-subordinate relationships within the organization and facilitate subsequent analysis. Applying the weighted matching algorithm to calculate the matching weights between members is a key step in optimizing group chat allocation.

[0138] It can be understood that the weights are based on the interaction frequency and role correlation.

[0139] For example, employee A communicates frequently with B and belongs to the same project team, with a matching weight of 0.8; the communication with C is less frequent, with a weight of 0.3. The algorithm sorts according to the weights and preferentially assigns members with high weights to the same group chat to ensure collaboration efficiency.

[0140] In one embodiment, the weight threshold is set to 0.5, and members with values lower than this are assigned to different group chats. Update the dynamic group chat framework through the personnel allocation plan to generate the adjusted framework configuration, which needs to be adjusted in combination with the actual scenario.

[0141] Exemplarily, a certain department has 10 employees. The allocation plan forms a core group chat with 6 members with high weights, and the remaining 4 are assigned to the auxiliary group chat. The framework configuration includes the group chat name, member list, and interaction permissions. After adjustment, it is necessary to verify whether the configuration meets the stability threshold. For example, the average interaction frequency of group chat members needs to be higher than 3 times / week. If not, return to the recursive process for reallocation. Extract member allocation and interaction data from the final group chat state to generate a status description file, and ensure data integrity.

[0142] Specifically, the file contains the group chat ID, member list, and interaction records, such as "Group chat G1, members A, B, C, average interaction 4 times / week".

[0143] In one possible implementation, the file is in JSON format for easy system parsing. When performing data verification, check whether the file is missing key fields or has duplicate records.

[0144] For example, if it is found during verification that a certain group chat lacks member data, it is marked as incomplete and needs to be regenerated. Through the above method, the construction and optimization of dynamic group chats can closely meet the requirements of the organizational structure. The implementation methods of each technical topic are closely linked, forming a complete process from data extraction to final verification.

[0145] For example, the clear structure of the hierarchical recursive algorithm provides reliable input for weighted matching, while the setting of the stability threshold ensures the practicality of group chats. This logically rigorous implementation can effectively support collaborative management within an organization.

[0146] Although the present invention has been described herein with reference to various illustrative embodiments of the invention, it should be understood that those skilled in the art can devise many other modifications and implementations that will fall within the scope and spirit of the principles disclosed in this application. More specifically, within the scope of the present application's disclosure, the drawings, and the claims, various variations and improvements can be made to the components and / or layout of the subject combination layout. In addition to the variations and improvements made to the components and / or layout, other uses will also be apparent to those skilled in the art.

Claims

1. An instant messaging system for automatically generating group chats based on the organizational structure hierarchy, characterized in that, It includes an external docking module for obtaining organizational structure data from an external system; a parsing module for parsing the organizational structure data obtained by the external docking module to generate an address book framework, and for identifying each person within the organizational structure and generating a personnel data packet for each person. A user management module that creates an address book with group management based on the address book framework, and generates a to-be-added member within the corresponding group of the address book according to the personnel data packet; a registration module for user registration and registration review. During the registration review stage, it matches the registration information with the personnel data packet. If the match is successful, the registration is automatically successful. If the match fails, manual review is required. After successful registration, the to-be-added member within the corresponding group is automatically replaced; a group creation module for automatically generating an internal department communication group for department use, a management communication group for management use, and a project communication group generated according to project documents. The user management module includes a user database and an address book unit. When the user database imports the personnel data packet, it generates a personal information table according to the personnel data within the personnel data packet. The specific steps for the group creation module to generate a project communication group according to project documents include: Step S1, importing the project document into the group creation module, and the group creation module extracts and identifies the fields of the document. Step S2, extracting the personnel configuration requirements and project start and end times from the project document; Step S3, screening the personal information table from the user database to draw up a list of personnel; Step S4, the group creation module sends the drawn-up list of personnel to the project leader; Step S5, after the project leader confirms the list, automatically creates a project communication group according to the list; Step S6, after the project ends, the group creation module exports all the chat records in the group to form a communication record document, and saves the communication record document and all the files in the project communication group in the same folder.

2. The instant messaging system for automatically generating group chats based on the organizational structure hierarchy according to claim 1, characterized in that: When the address book unit imports the personnel data packet, it generates a to-be-added member according to the personnel data within the personnel data packet. The to-be-added member is bound to the personal information table. When the user registration is successful and matches the to-be-added member, the user account replaces the to-be-added member and is bound to the corresponding personal information table. The address book unit is used to manage the address book and operate on the address book.

3. The instant messaging system for automatically generating group chats based on the organizational structure hierarchy according to claim 2, wherein: The personal information table includes user information, skill information, project experience value, and current load factor.

4. An instant messaging system for automatically generating group chats based on the organizational structure hierarchy according to claim 3, characterized in that: The project experience value Is dynamically adjusted according to the participated projects, according to the formula , Is the total score of the small project experience value, Is the total score of the medium project experience value, Is the total score of the large project experience value, Through the formula , where , , Are the participation times, , , Are the position coefficients in the project, Takes 0.1, Takes 0.2, Takes 0.

3. If Is greater than 2, then Takes 2; Through the formula , where , , Are the participation times, , , Are the position coefficients in the project Takes 0.2, Takes 0.4, Takes 0.

6. If Is greater than 4, then Takes 4; Through the formula , where, , , Are the participation times, , , Are the position coefficients in the project, Takes 0.3, Takes 0.6, Takes 0.

9. If Is greater than 10, then Takes 10.

5. An instant messaging system for automatically generating group chats based on the organizational structure hierarchy according to claim 4, characterized in that: The current load factor is dynamically adjusted according to the formula , where is the number of overlapping days between the projects already participated in and the projects planned to participate in for Project 1, is the difficulty coefficient of Project 1, is the number of overlapping days between the projects already participated in and the projects planned to participate in for Project 2, is the difficulty coefficient of Project 2, is the number of overlapping days between the projects already participated in and the projects planned to participate in for Project n, is the difficulty coefficient of Project n, is the total number of days of the projects planned to participate in, is the difficulty coefficient of the projects planned to participate in; , , , all take values between 0.1 and 0.

9. If ≥0.95, it represents full load. If 0.7 < <0.95, it represents normal load. If 0.1 < ≤0.7, it represents light load.

6. The instant messaging system for automatically generating group chats based on the organizational structure hierarchy according to claim 5, wherein: The project document specifies the project experience values of different members The value range and skill requirements of the personnel list. When preparing the personnel list, a main list and a backup list are prepared. When preparing the main list, first match the personnel list with the skill requirements and project experience values. and load factor For those with a score less than 0.95, then according to the empirical value Screen from largest to smallest to determine the main list; when drafting the candidate list, first match those who meet the skill requirements and project experience and load factor Less than 0.95 personnel, then according to the load rate Screen from small to large and determine the shortlist.

7. An instant messaging system for automatically generating group chats based on the organizational structure hierarchy according to claim 1, characterized in that: The personnel data packet includes name, gender, department, and position. When the user registers through the registration module, the user needs to fill in the name, gender, department, and position.

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