Distributed collaborative office data processing method and system

Abstract

The invention relates to the technical field of enterprise data management, in particular to a distributed collaborative office data processing method and system, and the method comprises the following steps: based on employee task completion records, evaluating various capability processing capabilities of employees through extracting and analyzing the project completion data, feedback frequency and interaction data of the employees, and calculating the task completion data of the employees; the task process of the current project is analyzed, the requirements of the target project for various skills are identified, and a capability and requirement data set is formed; according to the invention, through real-time monitoring and analysis of interaction data and feedback frequency of employees, capability of the employees is accurately assessed and project requirements are matched, reasonable human resource configuration is realized and flexibility of project management is improved, and through dynamic adjustment of data access authority, information security is ensured and a data leakage risk is reduced. Real-time data synchronization and consistency verification guarantee that data is accurate among multiple nodes, the team is helped to identify the reason of low efficiency in combination with analysis of cooperation efficiency, and the cooperation efficiency among team members is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise data management, and in particular to a distributed collaborative office data processing method and system. Background Art

[0002] The field of enterprise data management technology focuses on systematically collecting, storing, managing, protecting and utilizing data within an organization, and ensuring data quality, access control, data integrity and security. By implementing effective data management strategies, it helps enterprises ensure the accuracy and availability of data, support decision-making processes, optimize business processes and improve overall operational efficiency. It combines data lifecycle management, including data creation and acquisition, data storage and maintenance, data archiving and deletion, and protects sensitive information from unauthorized access and leakage by paying attention to and complying with data protection regulations and standards.

[0003] Among them, the collaborative office data processing method aims to support collaborative work within or across organizations and optimize the collaboration efficiency among team members, including real-time data sharing, collaborative document editing, meeting record synchronization, task management and tracking, to achieve increased document processing speed, reduced communication costs, and improved decision-making quality. It also ensures the consistency and accuracy of information through centralized data management, helps enterprises achieve real-time updating and sharing of data, supports remote work, and conducts flexible and efficient project management.

[0004] Traditional collaborative office data processing methods are not flexible enough in supporting dynamic task allocation and real-time data synchronization. They rely on preset data management rules and static permission settings, which limits the organization's response speed when faced with rapidly changing project requirements and employee dynamics. In the absence of real-time data synchronization and dynamic permission management, remote work teams encounter problems such as information lag and outdated data access permissions, leading to delayed project schedules and leakage of key information. There is a lack of in-depth analysis of employee collaborative behavior, and it is impossible to effectively predict potential bottlenecks in team collaboration, and it is impossible to fully utilize data-driven decision support to optimize business processes and improve operational efficiency. Summary of the invention

[0005] In order to solve the technical problems of poor collaboration efficiency and data leakage risk in the prior art, the embodiment of the present invention provides a distributed collaborative office data processing method and system. The technical solution is as follows:

[0006] On the one hand, a distributed collaborative office data processing method is provided, the method comprising:

[0007] S1: Based on the employee's task completion records, by extracting and analyzing the employee's project completion data, feedback frequency, and interaction data, the employee's ability to handle multiple skills is evaluated, and the task flow of the current project is analyzed to identify the target project's needs for multiple skills, forming a capability and demand data set;

[0008] S2: planning and adjusting the task assignment list based on the capabilities and required data sets, and adjusting the data management permissions for employees in multiple task roles based on the data access requirements of the tasks, to form a document permission configuration list;

[0009] S3: Based on the document permission configuration list, detect data updates between multiple nodes, synchronize office data in real time, and verify the consistency of data in multiple nodes to form a real-time data synchronization record;

[0010] S4: Analyze the interaction frequency, meeting activities, and file sharing of team members using the real-time data synchronization record, identify key factors affecting collaboration efficiency, predict the team's collaboration efficiency, and generate collaboration efficiency analysis results;

[0011] S5: Based on the collaboration efficiency analysis results and the project progress, create version snapshots of the documents and archive the files. Based on the sensitivity of the file content, adjust the storage location and security level of the documents and generate office data processing records.

[0012] As a further solution of the present invention, the capability and requirement data set includes the employee's skill score, requirement priority, and task matching degree; the document permission configuration list includes document access rights, editing rights, and file sharing rights of multiple task roles; the real-time data synchronization record includes the timestamp of data transmission between nodes, office data synchronization status information, and consistency detection results; the collaboration efficiency analysis results include team interaction scores, influencing factor analysis results, and collaboration efficiency evaluation information; the office data processing records include document version creation records, file archiving results, and security level adjustment records.

[0013] As a further solution of the present invention, based on the employee task completion record, by extracting and analyzing the employee's project completion data, feedback frequency, and interaction data, the employee's ability to handle multiple capabilities is evaluated, and the task flow of the current project is analyzed to identify the target project's needs for multiple skills. The steps of forming the capability and demand data set are specifically as follows:

[0014] S101: Based on the employee task completion records, collect the employee's project completion data, feedback frequency, and interaction data, count the number of project participations and completion speeds of multiple employees, and generate participation performance statistics;

[0015] S102: Based on the participation performance statistics, analyze the skill levels of multiple employees, including problem-solving ability and teamwork ability, calculate scores of multiple skills for target employees, match skill tags, and generate a skill level classification list;

[0016] S103: Based on the skill level classification list, analyze the task flow of the current project, evaluate the target project's demand intensity for multiple skills, and generate capabilities and demand data sets.

[0017] As a further solution of the present invention, the specific formula for calculating the scores of multiple skills for the target employee is:

[0018]

[0019] Among them, p ij represents the score of the i-th employee in the j-th skill, i represents the employee's serial number, j represents the serial number of the skill, and represents different skill areas, including problem solving, teamwork, ijk represents the score of the i-th employee on the k-th dimension of the j-th skill. k represents the dimension number of the evaluation, indicating the specific evaluation dimension under the target skill, including time management ability and innovative thinking. k is the weight coefficient of the kth dimension, and K represents the total number of dimensions evaluated.

[0020] As a further solution of the present invention, according to the capability and requirement data set, the task allocation list is planned and adjusted, and according to the data access requirements of the task, the data management permissions are adjusted for employees of multiple task roles. The steps of forming the document permission configuration list are specifically as follows:

[0021] S201: using the capability and requirement data set, planning task allocation according to the skill types of multiple employees, and generating a project task allocation result;

[0022] S202: Analyze the data access, editing and sharing permissions required by multiple employees according to the project task allocation result and the task roles of multiple employees in the target project, and generate a role requirement analysis result;

[0023] S203: Based on the role requirement analysis result, access, editing and sharing permissions of multiple employees are configured to generate a document permission configuration list.

[0024] As a further solution of the present invention, based on the document permission configuration list, data updates between multiple nodes are detected, office data is synchronized in real time, and the consistency of data of multiple nodes is verified to form real-time data synchronization records. Specifically, the steps are as follows:

[0025] S301: Based on the document permission configuration list, analyzing data update requests of multiple nodes, recording the sending and receiving time of data packets, comparing timestamp differences, calculating the transmission delay of data packets, and generating data transmission status records;

[0026] S302: Based on the data transmission status record, performing consistency check on the data, including comparing the contents of data packets received by multiple nodes, identifying and recording data packets with inconsistent contents, and generating a data consistency check result;

[0027] S303: Integrate the data consistency check results, adjust network parameters by evaluating the transmission status of the data packet, including the transmission speed and error rate, optimize the data synchronization process, and generate a real-time data synchronization record.

[0028] As a further solution of the present invention, the real-time data synchronization record is used to analyze the interaction frequency, meeting activities, and file sharing of team members, identify key factors affecting collaboration efficiency, predict the collaboration efficiency of the team, and generate the collaboration efficiency analysis results in the following steps:

[0029] S401: extracting the interaction data of team members in the target project based on the real-time data synchronization record, calculating the interaction frequency, analyzing the activity of participating in meetings and the frequency of file sharing, and generating team interaction analysis results;

[0030] S402: Based on the team interaction analysis results, evaluate the impact of various interactive behaviors on the progress and quality of the project, identify key factors affecting collaboration efficiency, and generate behavior impact evaluation results;

[0031] S403: Analyze and predict the team's collaboration efficiency using the behavior impact assessment result, and generate a collaboration efficiency analysis result.

[0032] As a further solution of the present invention, according to the collaborative efficiency analysis results and the project progress, a version snapshot of the document is created and the file is archived, and the storage location and security level of the document are adjusted in combination with the sensitivity of the file content. The steps of generating office data processing records are specifically as follows:

[0033] S501: Analyze the project progress data according to the collaboration efficiency analysis result, create version snapshots for multiple task documents, record the saving time and version numbers of multiple versions, archive the files, and generate a version control log;

[0034] S502: Based on the version control log, analyzing the usage frequency and modification history of multiple documents, evaluating the sensitivity of the file contents, calculating the security levels of multiple files, and generating risk level calculation results;

[0035] S503: Based on the risk level calculation result and according to the security level, the encryption level and access rights of the file are adjusted to optimize the security of the data and generate an office data processing record.

[0036] As a further solution of the present invention, the specific formula for calculating the security levels of multiple files is:

[0037]

[0038] L represents the security level of the file, which is used to quantify the security risks faced by the file. f represents the number of times the document has been accessed within a specific period of time, which reflects the activity of the document. m represents the number of times the document has been modified since its creation. s represents the sensitivity level of the document content, which is a score preset based on the sensitivity of the content. w f Represents the weight value of the access frequency, showing the relative importance of the access frequency in the overall security assessment, w m The weight value representing the number of modifications indicates the impact of the modification frequency on the overall security level. s The weight value represents the sensitivity of the content, reflecting the weight of sensitive content in security risk assessment.

[0039] On the other hand, a distributed collaborative office data processing system is provided, which is applied to a distributed collaborative office data processing method, and the system includes:

[0040] The employee competency analysis module extracts project completion data, feedback records, and interaction logs of multiple employees based on their task completion records, calculates the average time to complete tasks, feedback response speed, and interaction frequency, evaluates the competency level of employees, and analyzes the task flow of the current project, identifies the target project's needs for multiple skills, and generates employee skill evaluation results;

[0041] The task flow analysis module analyzes the employee skill assessment results, analyzes the skills required for multiple tasks in the target project according to the task requirements of the current project, and generates a task allocation list;

[0042] The control authority management module identifies the task roles of multiple employees according to the task assignment list, configures data access permissions for multiple employees, including read, write, and share permissions, and generates a document authority configuration list;

[0043] The data transmission synchronization module collects the timestamp and status information of data transmission between multiple nodes based on the document permission configuration list, verifies the consistency of data of multiple nodes by comparing the differences in timestamps and the integrity of data, and generates real-time data synchronization records;

[0044] The collaborative efficiency evaluation module uses the real-time data synchronization record to analyze the activity patterns of team members in the project, including meeting participation frequency, file sharing activities, response time, analyze key factors affecting collaborative efficiency, evaluate the collaborative efficiency of the team, and generate collaborative efficiency analysis results;

[0045] The data archiving management module creates multiple version snapshots of the document according to the collaboration efficiency analysis results and the real-time progress of the project, archives the files, adjusts the storage location and security settings according to the sensitivity of the file content, and generates office data processing records.

[0046] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0047] By real-time monitoring and analysis of employees' interaction data and feedback frequency, we can accurately assess employees' capabilities and match them with project needs, achieve reasonable human resource allocation and improve the flexibility of project management. By dynamically adjusting data access rights, we can ensure information security and reduce the risk of data leakage. Real-time data synchronization and consistency verification ensure the accuracy of data between multiple nodes. Combined with the analysis of collaborative efficiency, we can help the team identify the causes of inefficiency and enhance the collaborative efficiency among team members. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0058] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0059] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0060] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0061] The embodiment of the present invention provides a distributed collaborative office data processing method, such as Figure 1 The distributed collaborative office data processing method flow chart shown in the figure may include the following steps:

[0062] S1: Based on the employee's task completion records, by extracting and analyzing the employee's project completion data, feedback frequency, and interaction data, the employee's ability to handle multiple skills is evaluated, and the task flow of the current project is analyzed to identify the target project's needs for multiple skills, forming a capability and demand data set;

[0063] S2: planning and adjusting the task assignment list based on the capabilities and required data sets, and adjusting the data management permissions for employees in multiple task roles based on the data access requirements of the tasks, to form a document permission configuration list;

[0064] S3: Based on the document permission configuration list, detect data updates between multiple nodes, synchronize office data in real time, and verify the consistency of data in multiple nodes to form a real-time data synchronization record;

[0065] S4: Analyze the interaction frequency, meeting activities, and file sharing of team members using the real-time data synchronization record, identify key factors affecting collaboration efficiency, predict the team's collaboration efficiency, and generate collaboration efficiency analysis results;

[0066] S5: Based on the collaboration efficiency analysis results and the project progress, create version snapshots of the documents and archive the files. Based on the sensitivity of the file content, adjust the storage location and security level of the documents and generate office data processing records.

[0067] The capability and requirement data set includes the employee's skill score, requirement priority, and task matching degree; the document permission configuration list includes the document access rights, editing rights, and file sharing rights of multiple task roles; the real-time data synchronization record includes the timestamp of data transmission between nodes, office data synchronization status information, and consistency detection results; the collaboration efficiency analysis results include team interaction scores, influencing factor analysis results, and collaboration efficiency evaluation information; the office data processing records include document version creation records, file archiving results, and security level adjustment records.

[0068] See also Figure 2 ,Based on the employee's task completion records, by extracting and analyzing the employee's project completion data,feedback frequency, and interaction data, the employee's ability to handle multiple skills is evaluated, and the task flow of the current project is analyzed to identify the target project's needs for multiple skills. The specific steps for forming the ability and demand data set are as follows:

[0069] S101: Based on the employee task completion records, collect the employee's project completion data, feedback frequency, and interaction data, count the number of project participations and completion speeds of multiple employees, and generate participation performance statistics;

[0070] Through automated software, we track each employee’s participation frequency, interaction, and feedback speed in different projects, extract employees’ task completion records from the company’s internal database, and use the association rule learning algorithm in data mining technology to analyze target data, determine employees’ work habits and efficiency patterns, and use the Apriori algorithm to extract patterns from employees’ interaction data. We identify frequent item sets by calculating support and confidence, and determine common patterns of participation and task completion. Based on the target pattern, we use the weighted average method to calculate each employee’s project completion speed. The weights are automatically adjusted according to the complexity and urgency of the project, and the target calculation results are summarized into a performance statistics table for subsequent performance evaluation and team configuration optimization.

[0071] S102: Based on the participation performance statistics, analyze the skill levels of multiple employees, including problem-solving ability and teamwork ability, calculate scores of multiple skills for target employees, match skill tags, and generate a skill level classification list;

[0072] The specific formula for calculating the scores of multiple skills for the target employee is:

[0073]

[0074] Among them, p ij represents the score of the i-th employee in the j-th skill, i represents the employee's serial number, j represents the serial number of the skill, and represents different skill areas, including problem solving, teamwork, ijk represents the score of the i-th employee on the k-th dimension of the j-th skill. k represents the dimension number of the evaluation, indicating the specific evaluation dimension under the target skill, including time management ability and innovative thinking. k is the weight coefficient of the kth dimension, and K represents the total number of dimensions evaluated.

[0075] formula:

[0076]

[0077] Detailed explanation of the formula and the process of formula calculation and derivation:

[0078] Formulas are used to calculate each employee's score in multiple skill areas, and the results are used to assess the employee's skill level and serve as the basis for human resource decisions.

[0079] Parameter meaning and setting value:

[0080] p ij is the average score of the i-th employee in the j-th skill;

[0081] w k is the weight coefficient of the kth dimension, assuming that problem-solving ability w1 = 0.5, teamwork ability w2 = 0.3, and innovation ability w3 = 0.2;

[0082] r ijk is the score of the i-th employee on the k-th dimension of the j-th skill. Assume that the target employee scores 85 in problem-solving ability, 90 in teamwork ability, and 88 in innovation ability;

[0083] K is the total number of evaluation dimensions, assumed to be 3;

[0084] Substitute the parameters into the formula for calculation:

[0085]

[0086] Resultsij =29.03 indicates that the average score of the target employees in the considered skill area is 29.03. The value reflects the employee's performance in the target skill area. The calculation process is used to help evaluate the employee's skill level and assign skill labels, providing a data basis for task allocation.

[0087] S103: Based on the skill level classification list, analyze the task flow of the current project, evaluate the demand intensity of the target project for multiple skills, and generate capability and demand data sets;

[0088] Export all tasks and descriptions of the current project from project management, including task complexity, estimated completion time, and required skill types. Use decision tree algorithms to analyze target task data and identify the types of tasks with the highest skill requirements. Decompose the decision-making process layer by layer to determine the most important skill influencing factors, prioritize employees with corresponding skill labels, and generate a detailed data set of capabilities and requirements by matching with the skill level classification list. The data set includes the skill requirements for each task, the urgency and priority of the requirements, and dynamically adjusts task assignments based on the intensity of task requirements and the skill level of employees to ensure that each task is undertaken by the most appropriate employee to improve project execution efficiency and quality.

[0089] See also Figure 3 , based on the capabilities and required data sets, plan and adjust the task assignment list, and adjust the data management permissions for employees in multiple task roles based on the data access requirements of the tasks. The specific steps for forming a document permission configuration list are:

[0090] S201: using the capability and requirement data set, planning task allocation according to the skill types of multiple employees, and generating a project task allocation result;

[0091] Using the resource optimization model, the required information is extracted from the capability and demand data set. The data set records the skill level of each employee and the skill requirements of each project task. Through the linear programming model, the task allocation plan is calculated to minimize the overall project completion time while maximizing the utilization of employee skills. In the process, the Pulp library in the Python programming language is used to set various constraints, such as each employee's working time limit, skill matching, and task priority. After running through the solver, a task list for each employee is generated. The list indicates which employees should be responsible for specific tasks and the scheduled start and end time of each task, which improves the efficiency of resource allocation and ensures the fairness and transparency of task allocation.

[0092] S202: Analyze the data access, editing and sharing permissions required by multiple employees according to the project task allocation result and the task roles of multiple employees in the target project, and generate a role requirement analysis result;

[0093] By implementing association analysis, we evaluate the types of data access required by employees when performing their tasks, such as reading, editing, or sharing permissions. We use decision support technology to combine the employee's task role and the project's data security policy to define standard permission settings for each role. Through matching algorithms, such as K-means clustering, we analyze the actual activity data of employees to determine deviations and exceptions in permission requirements. The permission requirement analysis results for each role then generate a detailed report that lists the data access requirements and recommended permission settings for each role, ensuring that each employee obtains the data permissions necessary to complete their tasks while avoiding excessive authorization.

[0094] S203: Based on the role requirement analysis result, configure the access, editing and sharing permissions of multiple employees and generate a document permission configuration list;

[0095] Use a role-based access control model to set specific permissions for each employee to ensure that they can access, edit or share the corresponding project documents. During the configuration process, the administrator sets permissions through enterprise resource planning and dynamically adjusts permissions to meet the needs of project progress and team changes. The permission configuration list is generated by an automated script. The script is written in a Python environment and uses SQL queries and API calls to retrieve employee and task data from the database and apply permission templates. The generated permission configuration list provides detailed permission settings for each document, including which employees can access, edit, and share which documents. The process ensures the security and compliance of project information while improving the efficiency of team collaboration.

[0096] See also Figure 4 Based on the document permission configuration list, the data update between multiple nodes is detected, the office data is synchronized in real time, and the consistency of the data of multiple nodes is verified. The steps of forming the real-time data synchronization record are as follows:

[0097] S301: Based on the document permission configuration list, analyzing data update requests of multiple nodes, recording the sending and receiving time of data packets, comparing timestamp differences, calculating the transmission delay of data packets, and generating data transmission status records;

[0098] The data packet sending and receiving situation of each network node is analyzed through monitoring tools. The process involves extracting the timestamp of each data packet from the network monitoring, including the precise time of sending and receiving. The total delay time of each data packet from sending to receiving is calculated through the timestamp. The Pandas library in the Python programming environment is used to process time series data to calculate the transmission delay of each data packet. The delay time is obtained by subtracting the difference between the sending timestamp and the receiving timestamp, and recorded in the central database to form a detailed data transmission status record. The data table records the transmission delay of each data packet and points out the abnormal situation where the delay exceeds the standard threshold, providing basic data for network performance analysis and subsequent optimization.

[0099] S302: Based on the data transmission status record, performing consistency check on the data, including comparing the contents of data packets received by multiple nodes, identifying and recording data packets with inconsistent contents, and generating a data consistency check result;

[0100] Use a hash algorithm to generate a unique hash value for the content of the data packet received by each node, collect data packets from all nodes, generate a hash value for each data packet, and store the target hash value in a centralized database. Compare the target hash values, identify data packets with inconsistent content on different nodes, and record detailed information on the target data packet, including the data packet ID, related nodes, hash value, and reception time. This is done through automated scripts. The scripts use SQL query language and network protocol analysis tools to capture and analyze network data. The generated data consistency check results provide a clear report that points out inconsistent data packets and their causes, providing network administrators with a basis for repairing network errors.

[0101] S303: Integrate the data consistency check results, adjust network parameters by evaluating the transmission status of the data packet, including the transmission speed and error rate, optimize the data synchronization process, and generate a real-time data synchronization record;

[0102] Optimize data synchronization by dynamically adjusting network parameters, analyze bottlenecks in data transmission based on data transmission status records, such as slow transmission speed and high error rate, use network performance optimization tools to adjust network bandwidth and error correction algorithms, reduce packet loss and transmission delays, and improve network architecture by modifying network routing and reconfiguring switch strategies to ensure that data packets can reach their destination in the shortest possible time. All adjustments and results are recorded in real-time data synchronization records. The target record lists the differences before and after each adjustment, the specific parameters implemented, and the optimized synchronization efficiency. The record provides real-time feedback and long-term improvement data support for network operation and maintenance.

[0103] See also Figure 5, using the real-time data synchronization record, analyzing the interaction frequency, meeting activities, and file sharing of team members, identifying the key factors affecting the collaboration efficiency, predicting the team's collaboration efficiency, and generating the collaboration efficiency analysis results are specifically as follows:

[0104] S401: extracting the interaction data of team members in the target project based on the real-time data synchronization record, calculating the interaction frequency, analyzing the activity of participating in meetings and the frequency of file sharing, and generating team interaction analysis results;

[0105] The number of meetings each member participated in and the frequency of file sharing were counted, and the dplyr package in the data statistics software R language was used to aggregate and analyze the data to generate interaction frequency data for each team member. Through descriptive statistical analysis, the activity of team members in different time periods was calculated, such as weekly meeting participation and monthly file sharing activities. The target data was visualized through scatter plots and bar charts to show the activeness of team member interactions and form the results of team interaction analysis. The results provided quantitative indicators of team collaboration and revealed the interaction patterns of team members in the project, providing data support for further analysis of team dynamics.

[0106] S402: Based on the team interaction analysis results, evaluate the impact of various interactive behaviors on the progress and quality of the project, identify key factors affecting collaboration efficiency, and generate behavior impact evaluation results;

[0107] A causal analysis model is used to evaluate the impact of different interactive behaviors on project progress and quality. The model is implemented through a structural equation model to evaluate the causal relationship between variables and define interactive behavior variables that affect project progress and quality, such as meeting participation, file sharing frequency, and communication efficiency. Amos software is used for model construction and path analysis to evaluate the direct and indirect effects between target variables. Through analysis, the key behavioral factors that have the greatest impact on collaborative efficiency are identified, and behavioral impact assessment results are generated. The results describe which behaviors have a positive impact on project success and which lead to delays or quality declines, providing a basis for project management to optimize team interactions.

[0108] S403: Analyze and predict the team's collaboration efficiency using the behavior impact assessment result, and generate a collaboration efficiency analysis result;

[0109] The team's collaborative efficiency is predicted through a regression analysis model. Multiple linear regression is used to explore the predictive ability of different interactive behaviors on collaborative efficiency. Key behavioral factors are extracted from the behavioral impact assessment results as independent variables, and various measures of collaborative efficiency are used as dependent variables. SPSS software is used for data processing and regression analysis to determine the correlation and regression coefficient between variables. Through target analysis, the collaborative efficiency analysis results are generated. The results show the specific impact of various behavioral factors on collaborative efficiency and provide improvement suggestions, including increasing the frequency of team communication to improve collaborative efficiency. Scientific decision-making support is provided to management to help formulate effective team management strategies.

[0110] See also Figure 6 , based on the results of the collaboration efficiency analysis, according to the project progress, create version snapshots of the document and archive the file, and adjust the storage location and security level of the document in combination with the sensitivity of the file content. The specific steps of generating office data processing records are as follows:

[0111] S501: Analyze the project progress data according to the collaboration efficiency analysis result, create version snapshots for multiple task documents, record the saving time and version numbers of multiple versions, archive the files, and generate a version control log;

[0112] Refine the management of project progress data and use the Git distributed version control tool to automatically create version snapshots for each task document. The process includes recording the save time and version number of each version and storing the target information in the central version library. Through automated scripts, whenever a document is modified, a new version snapshot is generated and the log file is updated. This log records the version history of each file, the creator and modification time of each version, ensuring the historical traceability of project documents. In order to improve access efficiency, files are classified and archived, and storage space is reduced by regularly archiving old versions. The generated version control log provides detailed document version tracking for project management, helping the management team to efficiently trace back and review project history.

[0113] S502: Based on the version control log, analyzing the usage frequency and modification history of multiple documents, evaluating the sensitivity of the file contents, calculating the security levels of multiple files, and generating risk level calculation results;

[0114] The specific formula for calculating the security levels of multiple files is:

[0115]

[0116] L represents the security level of the file, which is used to quantify the security risks faced by the file. f represents the number of times the document has been accessed within a specific period of time, which reflects the activity of the document. m represents the number of times the document has been modified since its creation. s represents the sensitivity level of the document content, which is a score preset based on the sensitivity of the content. w f Represents the weight value of the access frequency, showing the relative importance of the access frequency in the overall security assessment, w m The weight value representing the number of modifications indicates the impact of the modification frequency on the overall security level. s The weight value represents the sensitivity of the content, reflecting the weight of sensitive content in security risk assessment.

[0117] formula:

[0118]

[0119] Detailed explanation of the formula and the process of formula calculation and derivation:

[0120] The formula is used to calculate the security level of the file, and the result is used to assess the security risk of the file in the enterprise data management system;

[0121] Parameter meaning and setting value:

[0122] f represents the number of times the document is accessed in the target period, assuming it is 150 times, reflecting the frequency of document use;

[0123] m represents the number of modifications since the document was created, assuming it is 30 times, indicating the frequency of document changes;

[0124] s represents the sensitivity level of the document content. Assume that it is 5, which means that the document contains highly sensitive information.

[0125] w f 、w m 、w s are the weights of access frequency, modification times and sensitivity respectively. Assume that w f =0.3,

[0126] w m =0.4, w s =0.3;

[0127] Substitute the parameters into the formula for calculation:

[0128]

[0129] L≈7.65;

[0130] The result 7.65 indicates that the file has a high security level, indicating that the document is rated at a high risk level due to high frequency of access, multiple modifications and containing sensitive information. The numerical value is used to help adjust security measures, including access control and encryption protection, to prevent data leakage and unauthorized access.

[0131] S503: Based on the risk level calculation result, according to the security level, the encryption level and access rights of the file are adjusted to optimize the security of the data and generate an office data processing record;

[0132] Use AES encryption technology to dynamically adjust encryption strength according to the security level of the file to protect file contents from unauthorized access and leakage. Adjust file access rights through the role-based access control model to ensure that only authorized users can access specific files. Through automated permission management, configure access rights based on the user's role and the security level of the file. Each permission change will be recorded in the office data processing record. The target record details the execution time, executor and execution results of each security measure, providing a complete audit trail for data security management and ensuring the integrity and confidentiality of information.

[0133] See also Figure 7 , a distributed collaborative office data processing system, the distributed collaborative office data processing system is used to execute the above-mentioned distributed collaborative office data processing method, the system includes:

[0134] The employee competency analysis module extracts project completion data, feedback records, and interaction logs of multiple employees based on their task completion records, calculates the average time to complete tasks, feedback response speed, and interaction frequency, evaluates the competency level of employees, and analyzes the task flow of the current project, identifies the target project's needs for multiple skills, and generates employee skill evaluation results;

[0135] The task flow analysis module analyzes the employee skill assessment results, analyzes the skills required for multiple tasks in the target project according to the task requirements of the current project, and generates a task allocation list;

[0136] The control authority management module identifies the task roles of multiple employees according to the task assignment list, configures data access permissions for multiple employees, including read, write, and share permissions, and generates a document authority configuration list;

[0137] The data transmission synchronization module collects the timestamp and status information of data transmission between multiple nodes based on the document permission configuration list, verifies the consistency of data of multiple nodes by comparing the differences in timestamps and the integrity of data, and generates real-time data synchronization records;

[0138] The collaborative efficiency evaluation module uses the real-time data synchronization record to analyze the activity patterns of team members in the project, including meeting participation frequency, file sharing activities, response time, analyze key factors affecting collaborative efficiency, evaluate the collaborative efficiency of the team, and generate collaborative efficiency analysis results;

[0139] The data archiving management module creates multiple version snapshots of the document according to the collaboration efficiency analysis results and the real-time progress of the project, archives the files, adjusts the storage location and security settings according to the sensitivity of the file content, and generates office data processing records.

[0140] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0141] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0142] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0143] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0144] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0145] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0146] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0147] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0149] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0150] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A distributed collaborative office data processing method, characterized in that: The method comprises: Based on the employee's task completion records, by extracting and analyzing the employee's project completion data, feedback frequency, and interaction data, the employee's ability to handle multiple skills is evaluated, and the task flow of the current project is analyzed to identify the target project's needs for multiple skills, forming a capability and demand data set; Plan and adjust the task assignment list based on the capabilities and required data sets, and adjust data management permissions for employees in multiple task roles based on the data access requirements of the tasks to form a document permission configuration list; Based on the document permission configuration list, detect data updates between multiple nodes, synchronize office data in real time, and verify the consistency of data in multiple nodes to form real-time data synchronization records; Utilizing the real-time data synchronization records, analyzing the interaction frequency, meeting activities, and file sharing of team members, identifying key factors affecting collaboration efficiency, predicting the team's collaboration efficiency, and generating collaboration efficiency analysis results; According to the results of the collaboration efficiency analysis and based on the project progress, create version snapshots of the document and archive the file. In combination with the sensitivity of the file content, adjust the storage location and security level of the document and generate office data processing records.

2. The distributed collaborative office data processing method according to claim 1, characterized in that: The capability and requirement data set includes the employee's skill score, requirement priority, and task matching degree; the document permission configuration list includes the document access rights, editing rights, and file sharing rights of multiple task roles; the real-time data synchronization record includes the timestamp of data transmission between nodes, office data synchronization status information, and consistency detection results; the collaboration efficiency analysis results include team interaction scores, influencing factor analysis results, and collaboration efficiency evaluation information; the office data processing records include document version creation records, file archiving results, and security level adjustment records.

3. The distributed collaborative office data processing method according to claim 1, characterized in that: Based on the employee's task completion records, by extracting and analyzing the employee's project completion data, feedback frequency, and interaction data, the employee's ability to handle multiple skills is evaluated, and the task flow of the current project is analyzed to identify the target project's needs for multiple skills. The specific steps for forming the ability and demand data set are as follows: Based on the employee task completion records, collect the employee's project completion data, feedback frequency, and interaction data, count the number of project participations and completion speeds of multiple employees, and generate participation performance statistics; Based on the participation performance statistics, analyze the skill levels of multiple employees, including problem-solving ability and teamwork ability, calculate scores of multiple skills for target employees, match skill tags, and generate a skill level classification list; Based on the skill level classification list, the task flow of the current project is analyzed, and the target project's demand intensity for multiple skills, generation capabilities and demand data sets are evaluated.

4. The distributed collaborative office data processing method according to claim 3, characterized in that: The specific formula for calculating the scores of multiple skills for the target employee is: Among them, p ij represents the score of the i-th employee in the j-th skill, i represents the employee's serial number, j represents the serial number of the skill, and represents different skill areas, including problem solving, teamwork, ijk represents the score of the i-th employee on the k-th dimension of the j-th skill. k represents the dimension number of the evaluation, indicating the specific evaluation dimension under the target skill, including time management ability and innovative thinking. k is the weight coefficient of the kth dimension, and K represents the total number of dimensions evaluated.

5. The distributed collaborative office data processing method according to claim 1, characterized in that: According to the capabilities and required data sets, plan and adjust the task assignment list, and adjust the data management permissions for employees in multiple task roles according to the data access requirements of the tasks. The specific steps to form a document permission configuration list are as follows: Using the capability and demand data set, planning task allocation according to the skill types of multiple employees, and generating project task allocation results; According to the project task allocation results, and according to the task roles of multiple employees in the target project, the data access, editing and sharing permissions required by multiple employees are analyzed to generate role requirement analysis results; Based on the role requirement analysis results, the access, editing and sharing permissions of multiple employees are configured to generate a document permission configuration list.

6. The distributed collaborative office data processing method according to claim 1, characterized in that: Based on the document permission configuration list, data updates between multiple nodes are detected, office data is synchronized in real time, and consistency of data of multiple nodes is verified to form real-time data synchronization records. Specifically, the steps are as follows: Based on the document permission configuration list, analyzing data update requests of multiple nodes, recording the sending and receiving time of data packets, comparing timestamp differences, calculating the transmission delay of data packets, and generating data transmission status records; Based on the data transmission status record, performing consistency check on the data, including comparing the contents of data packets received by multiple nodes, identifying and recording data packets with inconsistent contents, and generating data consistency check results; The data consistency check results are integrated, and network parameters are adjusted by evaluating the transmission status of the data packets, including transmission speed and error rate, to optimize the data synchronization process and generate real-time data synchronization records.

7. The distributed collaborative office data processing method according to claim 1, characterized in that: The steps of using the real-time data synchronization record to analyze the interaction frequency, meeting activities, and file sharing of team members, identify key factors affecting collaboration efficiency, predict the collaboration efficiency of the team, and generate collaboration efficiency analysis results are as follows: Based on the real-time data synchronization record, the interaction data of the team members in the target project are extracted, the interaction frequency is calculated, the activity of participating in the meeting and the frequency of file sharing are analyzed, and the team interaction analysis results are generated; Based on the team interaction analysis results, evaluate the impact of various interactive behaviors on the progress and quality of the project, identify key factors affecting collaboration efficiency, and generate behavior impact evaluation results; The behavioral impact assessment results are used to analyze and predict the team's collaboration efficiency and generate collaboration efficiency analysis results.

8. The distributed collaborative office data processing method according to claim 1, characterized in that: According to the results of the collaboration efficiency analysis, based on the project progress, create version snapshots of the document and archive the file, and adjust the storage location and security level of the document in combination with the sensitivity of the file content. The specific steps of generating office data processing records are as follows: Analyze the project progress data according to the collaboration efficiency analysis results, create version snapshots for multiple task documents, record the saving time and version numbers of multiple versions, archive the files, and generate version control logs; Based on the version control log, the usage frequency and modification history of multiple documents are analyzed, and the sensitivity of the file contents is evaluated, the security level of multiple files is calculated, and a risk level calculation result is generated; Based on the risk level calculation result, the encryption level and access rights of the file are adjusted according to the security level, the security of the data is optimized, and an office data processing record is generated.

9. The distributed collaborative office data processing method according to claim 8, characterized in that: The specific formula for calculating the security levels of multiple files is: L represents the security level of the file, which is used to quantify the security risks faced by the file. f represents the number of times the document has been accessed within a specific period of time, which reflects the activity of the document. m represents the number of times the document has been modified since its creation. s represents the sensitivity level of the document content, which is a score preset based on the sensitivity of the content. w f Represents the weight value of the access frequency, showing the relative importance of the access frequency in the overall security assessment, w m The weight value representing the number of modifications indicates the impact of the modification frequency on the overall security level. s The weight value represents the sensitivity of the content, reflecting the weight of sensitive content in security risk assessment.

10. A distributed collaborative office data processing system, characterized in that: According to the distributed collaborative office data processing method according to any one of claims 1 to 9, the system comprises: The employee competency analysis module extracts project completion data, feedback records, and interaction logs of multiple employees based on their task completion records, calculates the average time to complete tasks, feedback response speed, and interaction frequency, evaluates the competency level of employees, and analyzes the task flow of the current project, identifies the target project's needs for multiple skills, and generates employee skill evaluation results; The task flow analysis module analyzes the employee skill assessment results, analyzes the skills required for multiple tasks in the target project according to the task requirements of the current project, and generates a task allocation list; The control authority management module identifies the task roles of multiple employees according to the task assignment list, configures data access permissions for multiple employees, including read, write, and share permissions, and generates a document authority configuration list; The data transmission synchronization module collects the timestamp and status information of data transmission between multiple nodes based on the document permission configuration list, verifies the consistency of data of multiple nodes by comparing the differences in timestamps and the integrity of data, and generates real-time data synchronization records; The collaborative efficiency evaluation module uses the real-time data synchronization record to analyze the activity patterns of team members in the project, including meeting participation frequency, file sharing activities, response time, analyze key factors affecting collaborative efficiency, evaluate the collaborative efficiency of the team, and generate collaborative efficiency analysis results; The data archiving management module creates multiple version snapshots of the document according to the collaboration efficiency analysis results and the real-time progress of the project, archives the files, adjusts the storage location and security settings according to the sensitivity of the file content, and generates office data processing records.

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