OA intelligent processing system and method for team management application

Abstract

The present invention discloses an OA intelligent processing system and method for team management application, which relates to the technical field of digital management of enterprise teams, including a data acquisition layer, a data storage layer, a data analysis layer, and an application layer. In the present invention, team management optimization is achieved by successively solving the problems of dynamic changes in skills and task adaptation, system adaptability of changes in employee behavior habits, and task allocation coordination based on biological rhythms and group psychology. First, the real-time problem of dynamic changes in skills and task adaptation is solved by using an algorithm based on operation sequence similarity, accurately capturing skill changes and improving matching accuracy. Based on this result, a deep reinforcement learning model is used to solve the system adaptability problem, so that the system can quickly adapt to changes in behavior patterns. Finally, based on the results of the first two problems, task allocation coordination is achieved through a multi-dimensional analysis method, employee potential is brought into play, and deep integration optimization in many aspects is achieved, without the need for additional steps and software and hardware investment throughout the process.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise team digital management, and specifically to an OA intelligent processing system and method for team management application. Background Art

[0002] In the digital management of enterprise teams, there are many problems that need to be solved urgently.

[0003] First, there is a real-time problem with the dynamic changes in team members' skills and task adaptation. In a rapidly developing business environment, members' skills are constantly changing through training and practice. For example, marketers have mastered data analysis skills, but the OA system is based on static skill assessments and preset task templates. It cannot perceive such changes in real time and still assigns tasks according to the old skill labels, resulting in the inability to fully utilize new skills. At the same time, the market environment prompts rapid adjustments in business direction. For example, the shift from offline to online marketing requires new digital marketing skills, but the OA system has difficulty quickly matching new skill requirements with members' existing skills to achieve reasonable task redistribution. Conventional systems lack the ability to perceive skill changes in real time, dynamically analyze business needs, and quickly adjust task assignments. Simply expanding existing technologies cannot solve this problem.

[0004] Secondly, changes in employee behavior habits lead to insufficient system adaptability. As companies introduce new work concepts, tools, or business model shifts, such as the implementation of agile project management, employee work rhythms and task collaboration methods change significantly. However, OA systems are designed based on previous behavioral patterns and business processes. Traditional upgrades are often based on clear changes in business needs or technology updates. They do not adequately consider implicit and dynamically changing factors such as employee behavior habits. They lack intelligent learning and adaptive capabilities, and are unable to automatically identify behavioral pattern changes and dynamically adjust system functions and process settings. As a result, under agile management, modules used for traditional project progress tracking cannot accurately reflect the actual progress of projects, affecting management decision-making and project advancement efficiency.

[0005] Furthermore, the current system completely ignores biorhythms and group psychology when assigning tasks. Everyone has a unique biorhythm, and energy, creativity, and mental agility vary at different times. Teams are subject to group psychology effects, such as how morale and collaborative atmosphere during the critical phase of a project can impact work efficiency. However, existing systems assign tasks based solely on rigid metrics like employee skills and workload, scheduling highly creative tasks during periods of low biorhythms or continuing to assign tasks according to conventional methods even when team morale is low. Furthermore, the biorhythm cycles of different employees vary, making it extremely complex to coordinate individual differences with the demands of the team's tasks and the group's psychological state. Traditional task allocation methods are limited to focusing on the work itself, lack interdisciplinary thinking, and fail to integrate multidisciplinary knowledge to develop new task allocation models and algorithms.

[0006] In summary, it is difficult for existing technologies to effectively solve a series of difficult problems such as the dynamic changes in team members' skills and real-time task adaptation, the system adaptability caused by changes in employee behavior habits, and task allocation and coordination based on biological rhythms and group psychology without adding any steps or any software or hardware. New technical solutions are urgently needed to optimize the team management OA intelligent processing system.

[0007] In view of this, a team management application OA intelligent processing system and method are provided to overcome the above problems. Summary of the Invention

[0008] The purpose of the present invention is to provide an OA intelligent processing system and method for team management applications to solve the problems raised in the above background technology.

[0009] To solve the above technical problems, the present invention provides an OA intelligent processing system for team management applications, comprising:

[0010] Data collection layer: This layer uses an event-driven, native data capture mechanism within the system. Code snippets are embedded at key operational nodes in each business module to capture data. Data mapping tables are used to map and integrate operational data from different business systems in a unified format.

[0011] Data storage layer: We use the MySQL relational database and improve its distributed storage architecture. We use a proprietary distributed storage algorithm to implement sharded storage based on business type, time, and other dimensions. We also dynamically adjust the distribution of storage nodes based on access frequency. We also use a distributed ledger mechanism based on blockchain technology to perform redundant storage and consistency verification for key data.

[0012] Data analysis layer: Build a multimodal data analysis algorithm model that integrates semantic analysis, behavioral pattern recognition, and time series analysis. Adopt a cross-domain knowledge fusion approach to integrate knowledge from psychology, management, and other fields into the algorithm model.

[0013] Application layer: Design an intelligent interactive interface based on user behavior prediction. By analyzing employees' historical operation behaviors and business needs, it predicts the employees' possible next actions and displays relevant functions and data in advance. At the same time, it introduces emotional design concepts and adjusts the interface style and prompt information according to employees' work status and emotional feedback.

[0014] The team management application OA intelligent processing method includes the following steps:

[0015] Step 1: Basic data collection and integration:

[0016] Adopt a recording method based on operation step decomposition to collect employee operation logs, and use data watermark technology to mark the operation log data;

[0017] The feedback information processing system based on natural language understanding and knowledge graph collects employee feedback information and uses automatic classification and priority sorting algorithms to classify and sort the feedback information;

[0018] Use a combination of sentiment analysis and topic modeling, as well as social network dynamic modeling technology to collect project discussion area information;

[0019] Step 2:

[0020] Skill change perception: This system uses a skill assessment algorithm based on the similarity of operation sequences to compare employees' operation sequences at different times and calculate similarities. It also introduces a dynamic weighting assessment mechanism based on operation frequency and time consumption.

[0021] Task adaptation and adjustment: Using a deep task requirement decomposition algorithm based on semantic networks and business rules, we transform task requirements into semantic networks, refine each element, assign weights, and build a two-way matching model between employee skills and task requirements.

[0022] Step 3:

[0023] Identification of behavioral pattern changes: Build a dynamic behavioral pattern recognition model based on deep reinforcement learning, and adopt a multi-dimensional behavioral data fusion analysis method to integrate employees' operational behavior data, communication behavior data, task collaboration data, etc. for analysis;

[0024] System adaptive adjustment: Dynamically reconfigure relevant modules based on the dynamic module reconstruction method of the microservice architecture. Utilize the user portrait-based interface adaptive adjustment algorithm to generate personalized user portraits based on employee behavior patterns and work needs, and adjust the interface layout and function display.

[0025] Step 4:

[0026] Biorhythm analysis: A biorhythm analysis algorithm based on time series clustering and cycle prediction is proposed. This algorithm performs cluster analysis and cycle prediction on employees' long-term operation time series data, and introduces correlation analysis between environmental factors and biorhythms.

[0027] Group psychology assessment: Build a group psychology assessment model based on emotional communication and social network structure, adopt a multi-source data fusion group psychology analysis method, and integrate employee behavior data, communication data, performance data, etc. with project discussion area information for analysis;

[0028] Task allocation coordination: formulate task allocation strategies based on task allocation based on biological rhythms and group psychology, and establish a dynamic adjustment mechanism to automatically adjust task allocation according to real-time data changes during task execution.

[0029] Furthermore, the data acquisition layer is based on the native data capture mechanism in the event-driven system, and code snippets are implanted in the key operation nodes of each business module.

[0030] Furthermore, the shard storage formula of the self-developed distributed storage algorithm in the data storage layer is:

[0031] S i =Hash(B i ,T i )modN

[0032] Among them, S i Indicates the target server node number for data storage, B i Represents the business type code, T i is the timestamp of data generation, N is the total number of server nodes;

[0033] The formula for dynamically adjusting storage nodes is:

[0034]

[0035] Among them, P j represents the probability of data being stored in the jth server node, F j is the access frequency of data on the jth server node.

[0036] Furthermore, the importance evaluation formula of the automatic classification and prioritization algorithm in the employee feedback information processing system is:

[0037]

[0038] Where I represents the importance score of the feedback information, M is the number of key factors in the knowledge graph related to the feedback information, and w m is the weight of the mth key factor, K m is to extract the content related to the mth key factor from the feedback information, f m It is a function that quantitatively evaluates the extracted content based on the key factor type;

[0039] The urgency assessment formula is:

[0040]

[0041] Among them, E represents the urgency score of the feedback information, N is the number of judgment factors related to the urgency, and v n is the weight of the nth factor, T n is the time information related to urgency extracted from the feedback information, g n It is a function that quantifies the urgency based on time information;

[0042] The comprehensive priority score formula is:

[0043] P=α×I+β×E

[0044] Wherein, P is the comprehensive priority score of the feedback information, α and β are the weight coefficients of importance and urgency respectively, and α+β=1.

[0045] Furthermore, the operation sequence similarity of the skill evaluation algorithm based on operation sequence similarity in skill change perception is calculated as:

[0046]

[0047] Among them, Sim(S1,S2) represents the similarity of two operation sequences S1 and S2,

[0048] S1=[s 11 ,s 12 ,…,s 1L ],S2=[s 21 ,s 22 ,…,s 2L ], L is the length of the shorter of the two sequences; Match(s 1i ,s 2i ) is a matching function; |S1| and |S2| are the lengths of the operation sequences S1 and S2 respectively;

[0049] Comprehensive skill evaluation formula considering operation frequency and time consumption:

[0050]

[0051] Among them, Skill(t) represents the skill level of the employee at time, S t and S t-Δt are the operation sequences at time t and t-Δt respectively; w1, w2, w3 are weight coefficients, and w1+w2+w3=1, which respectively represent the importance of operation sequence similarity, operation frequency, and operation time in skill evaluation; i Indicates the i-th operation, Weight(o i ) is the operation o i The weight of Freq(o i ) is the operation o i Frequency, Time(o i ) is the operation o i The average time taken.

[0052] Furthermore, in task adaptation and adjustment, the task requirement semantic network of the deep decomposition algorithm based on the semantic network and business rules is constructed as follows:

[0053] G=(V,E)

[0054] Among them, G represents the semantic network of task requirements, V is a set of nodes, and each node v i ∈V represents an element of the task, E is a set of edges, and each edge e ij ∈E represents the element v i and v j the relationship between;

[0055] Factor refinement and weight allocation formula:

[0056]

[0057] Among them, W(v i ) is the node v i The weight of node v is k. i The number of associated nodes, Influence(v j ,v i ) represents node v j For node v i Importance(v j ) is the node v j Importance score.

[0058] Furthermore, the time series clustering formula of the biorhythm analysis algorithm based on time series clustering and period prediction in biorhythm analysis is:

[0059]

[0060] Among them, C is the number of cluster categories, S i is the i-th cluster, x j is the jth data point in the time series, μ i is the center of the i-th cluster;

[0061] Cycle prediction algorithm formula:

[0062]

[0063] Where y(t) is the predicted employee's work efficiency or ability performance at time, N is the number of period components, and A n is the amplitude of the nth periodic component, f n is the frequency of the nth periodic component, is the phase of the nth periodic component, and ∈(t) is the error term.

[0064] Furthermore, the intelligent interactive interface based on user behavior prediction in the application layer analyzes employees' historical operation behavior data and uses machine learning algorithms to build a prediction model to predict the employees' next operation.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] The project focuses on solving three key challenges in team management. By successively addressing the dynamic changes in skills and task adaptation, the system adaptability to changes in employee behavior habits, and the task allocation and coordination based on biorhythms and group psychology, the team management is optimized.

[0067] 1. Solve the problem of dynamic skill changes and real-time task adaptation

[0068] Solution: Skill change perception adopts an algorithm based on operation sequence similarity and a dynamic weight evaluation mechanism for operation frequency and time consumption; task adaptation adjustment uses a deep decomposition algorithm for task requirements based on semantic networks and business rules and a two-way matching model.

[0069] The traditional method of evaluating skill changes is one-sided, and the decomposition and matching of task requirements are rough. The new method of multi-dimensional evaluation and comprehensive matching is more in line with reality.

[0070] Accurately capture skill changes, save costs, improve the accuracy of task and skill matching, provide data and technical support for subsequent problem solving, and optimize resource and task allocation.

[0071] 2. Solve system adaptability problems based on technical problem results

[0072] Solution: Behavioral pattern change identification adopts a method based on deep reinforcement learning model and multi-dimensional behavioral data fusion analysis; the system adaptively adjusts the dynamic module reconstruction based on microservice architecture and the interface adaptive adjustment algorithm based on user portrait.

[0073] Traditional behavioral pattern recognition and system adjustment methods have poor adaptability and flexibility, while the new method is self-learning, multi-dimensional, and highly flexible.

[0074] Accurately identify changes in behavioral patterns, quickly adapt the system to ensure smooth business processes, improve workflow management flexibility and adaptability, and leverage skill problem-solving results without additional investment.

[0075] 3. Realize task allocation collaboration based on the first two problems

[0076] Solution: Biorhythm analysis proposes an algorithm based on time series clustering and cycle prediction and introduces environmental factor correlation analysis; group psychological assessment is constructed based on emotional communication and social network structure models and adopts multi-source data fusion methods; task allocation collaboration is based on biorhythms and group psychology to coordinate tasks and establish a dynamic adjustment mechanism.

[0077] Traditional analysis and evaluation methods are simple and subjective, while new methods conduct multi-dimensional analysis, comprehensively consider individual and team status, and make dynamic adjustments.

[0078] Accurately determine biorhythms and assess group psychology to provide a basis for task allocation, unleash employee potential, improve teamwork and project management efficiency, and achieve deep integration and collaborative optimization in multiple aspects without additional investment.

[0079] Without adding steps or software or hardware, we can solve the three key problems in team management in turn to achieve comprehensive optimization of the team from the individual to the overall level. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 This is a schematic diagram of the OA intelligent processing system and method for team management application of the present invention. DETAILED DESCRIPTION

[0081] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0082] See also Figure 1 , the present invention provides a technical solution:

[0083] See Figure 1 As shown, an embodiment of the OA intelligent processing system and method for team management is as follows:

[0084] 1. System Architecture Construction

[0085] This OA intelligent processing system adopts a unique and improved B / S architecture, consisting of four layers: data collection, storage, analysis, and application. Each layer is both independent and collaborative to achieve efficient system operation.

[0086] (1) Data Collection Layer

[0087] Data collection method

[0088] We abandon traditional data collection methods that rely on external engines and instead adopt an event-driven, native data capture mechanism within the system. Code snippets are embedded at key operational nodes in each business module. When an employee performs an action, such as recording a customer communication or processing an order in the sales module, the system automatically triggers data capture, detailing the time, object, and result of the action.

[0089] Based on the business system's operation log structure, data mapping tables are used to map and integrate operation data from different business systems into a unified format. For example, fields in customer follow-up records in the customer relationship management system are mapped to the data structure preset in the OA system to ensure data consistency and availability.

[0090] Traditional data capture engines are highly versatile but lack specificity, making them unable to precisely meet the real-time data collection needs of enterprises in complex business scenarios. However, by embedding code within key operational nodes within the system, data can be directly acquired from the source, ensuring real-time and complete data. The use of data mapping tables resolves the issue of data format differences across different business systems and provides a unified, standardized data foundation for subsequent data analysis.

[0091] This approach ensures the timeliness and integrity of data, enabling accurate and real-time acquisition of employee behavior information, providing a solid foundation for in-depth analysis of employee skills and behavioral patterns. In terms of resource management, accurate and real-time data collection helps companies rationally allocate human and material resources and avoid waste. In personnel management, it provides sufficient data support for subsequent, accurate assessment of employee skills, enhancing the scientific nature of personnel management.

[0092] (2) Data Storage Layer

[0093] Storage architecture design

[0094] We chose the MySQL relational database and improved its distributed storage architecture. Using a proprietary distributed storage algorithm, we shard data based on business type, time, and other dimensions. For example, employee operation log data for the past year is stored on one set of server nodes, while older data is stored on another set of nodes. The storage node distribution is dynamically adjusted based on data access frequency.

[0095] Distributed storage algorithm formula

[0096] Shard storage formula:

[0097] S i =Hash(B i ,T i )modN

[0098] Among them, S i Indicates the target server node number for data storage, B i Represents the business type code (for example, sales business code is 001, human resources business code is 002, etc.), T i is the timestamp of the data generation, and N is the total number of server nodes. By hashing the business type and timestamp and taking the modulus of the total number of nodes, data can be evenly distributed to different server nodes.

[0099] Dynamically adjust storage node formula:

[0100]

[0101] Among them, P j represents the probability of data being stored in the jth server node, Fj is the access frequency of data on the jth server node. By calculating the ratio of the data access frequency of each node to the total access frequency, the storage probability of data on different nodes is determined, and the storage node distribution is dynamically adjusted according to the access frequency.

[0102] Traditional distributed storage algorithms are mostly based on simple hash modulo or average distribution by data volume, and do not fully consider the impact of business type and time factors on data access patterns. This algorithm combines business type and timestamp to perform data sharding, which can make data storage more consistent with business characteristics and improve the locality of data reading. For example, business data generated recently is often accessed more frequently, and this method can be used to centrally store it in specific nodes to improve access efficiency. The formula that dynamically adjusts the distribution of storage nodes based on access frequency breaks through the limitations of traditional static storage allocation and optimizes the storage layout in real time according to the actual access situation of the data. This is an idea that has not appeared in existing technologies. Existing technologies mostly focus on balanced data storage, while this algorithm focuses on the dynamic characteristics of data access to improve the performance of the overall storage system.

[0103] The distributed ledger mechanism based on blockchain technology is used to redundantly store and verify the consistency of key data. When writing data, the consensus mechanism of multiple nodes is used to ensure the accuracy and integrity of the data and prevent data tampering.

[0104] Enterprise data volumes are growing rapidly, and traditional storage methods are prone to storage bottlenecks and read performance issues when faced with massive amounts of data. Our proprietary distributed storage algorithm, combined with business-specific data sharding, effectively improves storage efficiency and read speeds. The introduction of blockchain technology enhances data security and reliability, which is crucial when storing critical employee information and business data.

[0105] It enables efficient storage and rapid access to massive amounts of data, providing a stable and reliable data source for the data analysis layer. In workflow management, rapid data access can accelerate business processes and improve work efficiency. In project management, reliable data storage ensures the security of project-related data and guarantees the smooth progress of projects.

[0106] (3) Data Analysis Layer

[0107] Multimodal data analysis algorithm model

[0108] Build a multimodal data analysis algorithm model that integrates semantic analysis, behavioral pattern recognition, and time series analysis. For employee operation logs, apply semantic analysis to understand the meaning of the operation content, combine behavioral pattern recognition to uncover the underlying patterns of operation behavior, and utilize time series analysis to identify trends in operation time.

[0109] For example, when analyzing the operational logs of employees writing market research reports, semantic analysis determines the quality and professionalism of the report content, behavioral pattern recognition discovers whether specific materials are frequently referenced during the writing process, and time series analysis determines the changing trends in writing time and efficiency.

[0110] By integrating cross-disciplinary knowledge into the algorithmic model, we can incorporate knowledge from psychology, management, and other fields. For example, based on the theory of learning curves in psychology, when evaluating employee skill development, we can consider the phased nature of skill learning and more accurately determine the degree of skill mastery.

[0111] Traditional analytical methods are limited in scope and fail to comprehensively and deeply analyze multi-dimensional employee data. Integrating multiple analytical techniques can unlock the value of data from different perspectives and more accurately grasp employee skill development and behavioral habits. This cross-disciplinary knowledge integration breaks the limitations of computer science and enables analysis that better reflects the behavioral and psychological characteristics of employees in real-world workplaces.

[0112] Compared to traditional analytical methods, this model offers greater accuracy and adaptability, enabling more precise identification of underlying patterns in employee skill changes and behavioral habits. In personnel management, accurate skills assessments aid companies in talent development and promotion decisions. In task allocation, it provides a scientific basis for rational task allocation and improves its accuracy.

[0113] (4) Application layer

[0114] Interactive interface design

[0115] Design an intelligent interactive interface based on user behavior prediction. By analyzing employees' historical operations and business needs, the system predicts their likely next actions and displays relevant functions and data in advance on the interface. For example, when an employee enters the project management module, the system predicts the project progress data they may view or the project reports they need to submit based on their past operation habits, and displays the relevant function portals and data in a prominent position in advance.

[0116] Introducing emotional design concepts, the interface style and prompts are adjusted based on employees' work status and emotional feedback. For example, if the system detects that an employee is continuously handling complex tasks and their efficiency is declining, the interface automatically switches to a soothing color scheme and provides encouraging prompts to boost employee motivation.

[0117] Traditional interactive interfaces lack intelligent predictions of user behavior, forcing users to spend time searching for functions. Designs based on predictive user behavior can reduce the number of steps required and improve efficiency. Emotional design concepts consider the impact of employee work status on productivity and, through adjustments to interface style and prompts, enhance employee experience and motivation.

[0118] This improves the user experience, ensuring that employees and managers can easily access system functions. In terms of resource management, the efficient interface reduces employee time and resources wasted on tedious operations, improving resource utilization efficiency. In workflow management, convenient operations help accelerate business process execution and improve overall work efficiency.

[0119] 2. Basic Data Collection and Integration

[0120] (1) Collection of employee operation logs

[0121] Operation log recording mode

[0122] The system uses a step-by-step recording method. Each business operation is broken down into multiple atomic steps for recording. For example, in order processing, detailed information about each step, such as order entry, inventory query, and logistics information association, is recorded, including operation time, input data, and output results.

[0123] Use data watermarking technology to mark operation log data to ensure data integrity and traceability. When the data is generated, a watermark containing information such as operation time and employee identification is embedded. During subsequent analysis and use, the watermark can be used to verify the authenticity and source of the data.

[0124] Traditional operation log recording methods are too general and fail to accurately reflect employee operational details and skill application. Step-by-step operation logging provides more detailed operational information, enabling in-depth analysis of employee skills. Data watermarking technology effectively prevents data tampering and ensures data reliability, which is particularly important when data is used as a basis for employee skill assessment and business decision-making.

[0125] It can intuitively reflect employees' work behaviors and skill application, providing rich data support for detecting skill changes and identifying behavioral patterns. In personnel management, detailed operation logs help accurately assess employee work capabilities and contributions. In task allocation, it provides strong data support for determining whether employees are suitable for specific tasks.

[0126] (2) Collection of employee feedback information

[0127] Feedback information processing mechanism

[0128] Develop a feedback processing system based on natural language understanding and knowledge graphs. Employees enter their feedback via free text. The system first uses natural language understanding to extract key information, such as descriptions of skill improvements and key takeaways from their work. This information is then linked to the system's knowledge graph. For example, new skills can be linked to relevant business areas and project types to determine their placement and application scenarios within the enterprise's business system.

[0129] Automatic classification and prioritization algorithms are used to categorize and prioritize feedback based on its importance and urgency. For example, urgent feedback related to business process optimization is marked as high priority, while general suggestions are marked as low priority, allowing the system and managers to prioritize important feedback.

[0130] Feedback information automatic classification and priority sorting algorithm formula

[0131] Importance assessment formula:

[0132]

[0133] Where I represents the importance score of the feedback information, M is the number of key factors in the knowledge graph related to the feedback information, and w m is the weight of the mth key factor (for example, factors related to business process optimization have higher weights than general recommendation factors), K m is to extract the content related to the mth key factor from the feedback information, f m is a function that quantitatively evaluates the extracted content based on the key factor type. For example, for business process optimization feedback, f m This may be quantified based on the number of process steps mentioned in the feedback and the potential scope of impact.

[0134] Urgency assessment formula:

[0135]

[0136] Among them, E represents the urgency score of the feedback information, N is the number of judgment factors related to the urgency, and v n is the weight of the nth factor (e.g., factors involving business interruption risk have high weights), T n is the time information related to the urgency extracted from the feedback information (such as the time point when the business is expected to be affected), g n It is a function that quantifies the degree of urgency based on time information. For example, if the feedback mentions that a certain business will have serious problems within 24 hours, g n This time information is converted into a higher urgency score.

[0137] Comprehensive priority score formula:

[0138] P=α×I+β×E

[0139] Where P is the overall priority score of the feedback information, α and β are weighting coefficients for importance and urgency, respectively, with α + β = 1. The values of α and β can be adjusted based on the management strategy of different companies. For example, companies that prioritize emergency response can appropriately increase the values.

[0140] Traditional feedback information classification and sorting rely heavily on manual experience or simple keyword matching, and are unable to accurately and comprehensively assess the importance and urgency of feedback content. This algorithm quantitatively evaluates feedback by constructing a multi-factor comprehensive evaluation model, combining key factors in the knowledge graph with key information such as time in the feedback information. For example, in the importance assessment, considering the different degrees of impact of different business factors in the knowledge graph on the enterprise, a comprehensive calculation is performed using weights and quantification functions, which is not adopted by traditional methods. In the urgency assessment, time information is quantified and included in the calculation to more accurately reflect the urgent nature of the feedback. This multi-dimensional, multi-factor quantitative evaluation method breaks the limitations of traditional simple classification and sorting, and provides an innovative method for the efficient processing of feedback information.

[0141] Traditional employee feedback processing methods are inefficient, making it difficult to quickly and accurately extract useful information and effectively utilize it. The combination of natural language understanding and knowledge graph technology can better understand employee feedback and integrate it into corporate business systems. Automatic classification and prioritization algorithms can improve feedback processing efficiency and ensure that important feedback is addressed promptly.

[0142] Leveraging employees' subjective information provides a more comprehensive perspective for system decision-making. In personnel management, timely addressing employee feedback helps improve employee satisfaction and loyalty. In workflow management, employee feedback on business processes can help companies optimize processes and improve efficiency.

[0143] (3) Information collection in the project discussion area

[0144] Information mining technology

[0145] We use a combination of sentiment analysis and topic modeling to mine information from project discussion forums. Sentiment analysis determines the emotional leaning of comments, such as positive, negative, or neutral. Topic modeling is used to extract discussion topics, such as discussions on project technical challenges and team collaboration issues.

[0146] Using dynamic social network modeling technology, we construct a real-time social network diagram of team members in the discussion forum. By analyzing interactions between members, likes, and comments, we determine their influence within the team and the paths through which information is disseminated. For example, members who frequently like and reply are considered opinion leaders within the team, and their comments are likely to have a significant impact on team decision-making and team dynamics.

[0147] Traditional analysis of project discussion forums is relatively simplistic and fails to deeply explore the underlying insights into team collaboration and group psychology. Combining sentiment analysis with topic modeling can comprehensively understand discussion forum information from both the content and sentiment dimensions. Social network dynamic modeling technology can intuitively display the interactions among team members, providing a powerful tool for analyzing group psychology.

[0148] This provides a strong basis for task allocation based on group psychology. In project management, understanding team members' collaborative state and group psychology helps managers allocate tasks rationally and improve team collaboration efficiency. In personnel management, identifying potential leaders within a team can provide a reference for team formation and management.

[0149] 3. Solve the real-time problem of dynamic changes in team members' skills and task adaptation

[0150] (1) Perception of skill changes

[0151] Operation log analysis method

[0152] A skill assessment algorithm based on operation sequence similarity is proposed. This algorithm compares employees' operation sequences over time and calculates their similarity. For example, for data analysis skills, the order of steps an employee uses a data analysis tool over different time periods is compared. Increased similarity indicates a more proficient and stable mastery of data analysis skills.

[0153] Skill evaluation algorithm formula based on operation sequence similarity

[0154] Operation sequence similarity calculation:

[0155]

[0156] Among them, Sim(S1,S2) represents the similarity of two operation sequences S1 and S2,

[0157] S1=[s 11 ,s 12 ,…,s 1L ],S2=[s 21 ,s 22 ,…,s 2L ], L is the length of the shorter of the two sequences. Match(s 1i ,s 2i ) is a matching function, if the operation s 1i and s 2i If they are the same or belong to the same semantic category (for example, "open data file" and "import data" in data analysis belong to the same semantic category), then 1 is returned; otherwise, 0 is returned. |S1| and |S2| are the lengths of the operation sequences S1 and S2, respectively.

[0158] Comprehensive skill evaluation formula considering operation frequency and time consumption:

[0159]

[0160] Among them, Skill(t) represents the skill level of the employee at time, S t and S t-Δt are the operation sequences at time t and t-Δt (Δt is the time interval, such as a week or a month). w1, w2, and w3 are weight coefficients, and w1+w2+w3=1, respectively representing the importance of operation sequence similarity, operation frequency, and operation time in skill assessment. i Indicates the i-th operation, Weight(o i ) is the operation o i The weight of the operation (determined by the complexity and importance of the operation, such as complex data mining operations have a high weight and simple data cleaning operations have a low weight), Freq(o i ) is the operation o i Frequency, Time(o i ) is the operation o i The average time taken.

[0161] Traditional skill assessment algorithms often focus only on the results of operations or the number of simple operations, and are unable to assess skill mastery from the perspective of operational consistency and logic. This algorithm's calculation of operation sequence similarity breaks through the limitations of traditional skill assessment based solely on results, and measures skill stability and proficiency from the perspective of the operational process. For example, in data analysis, even if the final analysis results are the same, optimizing the sequence of operational steps may reflect an employee's in-depth understanding of data analysis skills. The formula that comprehensively considers operation frequency and time consumption innovatively incorporates operational characteristics into skill assessment. Traditional methods do not distinguish between the complexity and importance of different operations. This algorithm sets weights to make the assessment more consistent with the actual skill improvement process. Different types of operations contribute differently to skill assessment, and this method can more comprehensively and accurately assess employee skill levels.

[0162] A dynamic weighting mechanism for evaluating operation frequency and duration has been introduced. When evaluating skills, the weighting of operation frequency and duration is dynamically adjusted based on the complexity and importance of the operation. For example, for complex data mining operations, accuracy and duration are weighted higher; for simple data cleaning operations, frequency is weighted higher.

[0163] Traditional analysis methods, which analyze operation logs from a single dimension, struggle to comprehensively and accurately assess employee skill development. The operation sequence similarity algorithm assesses skill mastery from the perspective of operational coherence and logic, more closely reflecting the actual process of skill improvement. The dynamic weighted evaluation mechanism considers the characteristics of different operations, making the evaluation results more scientific and accurate.

[0164] It accurately captures the dynamic changes in employee skills without relying on additional skills testing, saving time and labor costs while ensuring data authenticity and real-time availability. In resource management, accurate skills assessments help companies rationally allocate human resources and assign the right talent to the right positions. In task allocation, it provides an accurate basis for timely adjustments, improving the rationality of task allocation.

[0165] (2) Task adaptation and adjustment

[0166] Task requirement decomposition and matching algorithm

[0167] A deep task requirement decomposition algorithm based on semantic networks and business rules is employed. Task requirements are converted into a semantic network, where nodes represent the various elements of the task, such as data processing and result presentation, and edges represent the relationships between elements. Based on business rules, each element is refined and weighted. For example, for a data analysis task in an online marketing project, the data processing element is broken down into sub-elements such as data cleaning and data mining, and each sub-element is weighted based on the project objectives.

[0168] Algorithm formula for deep decomposition of task requirements based on semantic network and business rules

[0169] Task requirements semantic network construction:

[0170] G=(V,E)

[0171] Among them, G represents the semantic network of task requirements, V is a set of nodes, and each node v i ∈V represents an element of a task (such as data processing, result presentation, etc.), E is a set of edges, and each edge e ij ∈E represents the element v i and v j relationships between data and results (e.g., there may be an “influence” relationship between data processing and result presentation).

[0172] Factor refinement and weight allocation formula:

[0173]

[0174] Among them, W(v i ) is the node v i (i.e., the weight of the task element), k is the weight of the node v iThe number of associated nodes, Influence(v j ,v i ) represents node v j For node v i The degree of influence of data cleaning on data mining can be determined by business rules in data analysis tasks. j ) is the node v j Importance score (determined based on business rules and project goals. For example, for projects aiming at accurate analysis, the importance score of nodes related to data accuracy is high).

[0175] Traditional methods for decomposing task requirements are crude and often rely on manual judgment or simple task classification, which cannot accurately reflect the complex requirements of the task and the relationships between the various elements. This algorithm transforms task requirements into a structured model by constructing a semantic network, clearly displaying the relationships between the various elements. The weight distribution formula comprehensively considers the degree of influence between elements and the importance of the elements themselves, which is not included in traditional methods. For example, in a project, different task elements have different impacts on the achievement of the final goal. This quantitative approach can more scientifically determine the weight of each element. This in-depth decomposition method based on semantic networks and business rules provides a more accurate description of task requirements for the precise matching of subsequent tasks with employee skills, breaking through the limitations of traditional task requirement analysis.

[0176] Build a two-way matching model between employee skills and task requirements. Consider not only the skill requirements of the task but also the adaptability of the employee's skills to the task and the potential for improvement. For example, if an employee has a basic understanding of data analysis but needs to further develop their skills, prioritize assigning them data analysis tasks that are challenging but can help them improve their skills.

[0177] Traditional methods for decomposing and matching task requirements are crude and simplistic, failing to accurately reflect the complex demands of tasks and the skills of employees. A decomposition algorithm that combines semantic networks with business rules enables a more comprehensive and accurate analysis of task requirements. The bidirectional matching model overcomes the limitations of traditional one-way matching by comprehensively considering both the task and the employee, resulting in more scientific and rational task allocation.

[0178] This improves the accuracy of matching tasks with employee skills, achieves scientific and rational task allocation, and fully unleashes employees' work potential. In personnel management, reasonable task allocation contributes to employee career development and skill improvement. In project management, accurate task allocation can improve project execution efficiency and quality.

[0179] 4. Solve the system adaptability problem caused by changes in employee behavior habits

[0180] (1) Identification of changes in behavioral patterns

[0181] Behavioral pattern recognition technology

[0182] A dynamic behavioral pattern recognition model based on deep reinforcement learning. The model continuously interacts with employee behavior data and automatically adjusts model parameters to adapt to changes in behavior patterns. For example, when an enterprise implements an agile project management model, employee task switching and collaboration behaviors change. The model can automatically identify these changes and update its understanding of normal behavior patterns.

[0183] Employ a multi-dimensional behavioral data fusion analysis method. This method integrates and analyzes employee operational behavior data, communication behavior data, and task collaboration data to determine whether behavioral patterns have changed from multiple perspectives. For example, by combining the frequency and content of employee contributions in project discussion forums with their operational behavior in task assignment modules, we can comprehensively determine whether their work behavior patterns meet the requirements of agile project management.

[0184] Traditional behavioral pattern recognition methods lack adaptability and struggle to quickly and accurately identify dynamic changes in employee behavior patterns. Deep reinforcement learning models, with their self-learning and adaptive capabilities, can keep pace with changes in employee behavior patterns. Multi-dimensional behavioral data fusion analysis methods offer comprehensive assessments from multiple perspectives, avoiding the one-sidedness of single-dimensional analysis and improving recognition accuracy.

[0185] It can accurately identify changes in employee behavior patterns, providing precise judgment basis for system adaptive adjustments. In workflow management, timely identification of behavioral pattern changes helps companies adjust business processes and improve work efficiency. In personnel management, understanding changes in employee behavior patterns helps managers better manage and guide employees.

[0186] (2) System Adaptive Adjustment

[0187] System Adjustment Strategy

[0188] A dynamic module reconstruction method based on a microservices architecture. When the system identifies changes in employee behavior patterns, it dynamically reconfigures relevant modules using a microservices architecture. For example, in the progress tracking module, the original linear progress display method is reconstructed into an iterative progress display based on the requirements of agile project management. Using task iteration as the basic unit, each iteration cycle is divided into planning, execution, and review phases, and progress status is updated in real time.

[0189] Utilize an adaptive interface adjustment algorithm based on user profiles. Generate personalized user profiles based on employee behavior patterns and work needs. For example, for employees who frequently perform data analysis, highlight relevant analysis functions and data entry points on the system interface. As employee behavior patterns change, the user profile is automatically updated, and the interface layout and function presentation are adjusted accordingly.

[0190] Traditional system adjustments lack flexibility and are difficult to quickly adapt to changes in employee behavior. The dynamic module reconfiguration approach of the microservices architecture allows for flexible adjustments to specific modules, quickly adapting to changing business needs. The user-profile-based interface adaptive adjustment algorithm addresses individual user needs, improving system usability and adaptability.

[0191] This enables the system to quickly adapt to changes in employee behavior, improving both system adaptability and user experience. In terms of resource management, the system's rapid adaptation reduces resource waste and inefficiencies caused by changes in employee behavior. In workflow management, it ensures smooth business processes and improves work efficiency.

[0192] 5. The challenge of achieving collaborative task allocation based on biological rhythms and group psychology

[0193] (1) Biorhythm analysis

[0194] Biorhythm Analysis Methods

[0195] A biorhythm analysis algorithm based on time series clustering and cycle prediction is proposed. Cluster analysis is performed on long-term employee operation time series data, dividing operating time into different time periods, such as peak and trough periods. A cycle prediction algorithm is then used to predict employee work efficiency and performance during these different time periods. For example, analysis revealed that an employee's work efficiency is highest between 10:00 AM and 12:00 PM and between 3:00 PM and 5:00 PM, and this pattern exhibits a certain cyclical nature.

[0196] Biological rhythm analysis algorithm formula based on time series clustering and cycle prediction

[0197] Time series clustering formula:

[0198]

[0199] Among them, C is the number of clustering categories (for example, dividing working hours into peak hours, trough hours, etc.), S i is the i-th cluster, x j is the jth data point in the time series (i.e., the employee's operation time point), μ iIs the center of the i-th cluster. The operation time series is divided into different time periods by minimizing the sum of the squares of the distances from each data point to the center of the cluster to which it belongs.

[0200] Cycle prediction algorithm formula:

[0201]

[0202] Where y(t) is the predicted employee's work efficiency or ability performance at time, N is the number of period components, and A n is the amplitude of the nth periodic component, f n is the frequency of the nth periodic component, is the phase of the nth periodic component, and ∈(t) is the error term. The cyclical changes in employee work efficiency and performance are predicted by fitting multiple sinusoidal functions.

[0203] Traditional biorhythm analysis methods are simple, often based on experience or simple time statistics, and fail to fully leverage data mining and prediction techniques to uncover patterns in time series data. This algorithm's time series clustering formula draws on clustering algorithms from data mining but innovatively applies it to the analysis of employee operating time, dividing the time series into meaningful time periods, a feature not found in traditional biorhythm analysis. The cycle prediction algorithm, by constructing a sinusoidal function model, accounts for the potential cyclical variations in biorhythms and predicts employee work efficiency and performance. Traditional methods fail to quantitatively predict this cyclical nature. This algorithm, by introducing a mathematical model, more accurately analyzes employee biorhythms, providing a more scientific basis for task allocation. Furthermore, when considering the relationship between environmental factors and biorhythms, this model can be further expanded to incorporate environmental factors as adjustment parameters to better reflect real-world work scenarios.

[0204] Introduce correlation analysis between environmental factors and biorhythms. Consider the impact of factors such as work environment and task type on employees' biorhythms. For example, a noisy work environment may disrupt an employee's biorhythm, reducing work efficiency. By analyzing these correlations, we can more accurately assess the biorhythm status of employees in different environments.

[0205] Traditional biorhythm analysis methods are simple and fail to fully consider employees' actual work situations and environmental factors. Time series clustering and cycle prediction algorithms can mine the patterns of employees' biorhythms from data. Correlation analysis between environmental factors and biorhythms makes the analysis results more relevant to actual work scenarios, improving the accuracy and practicality of biorhythm analysis.

[0206] Accurately identifying the peak and trough periods of an employee's biorhythm provides a scientific basis for task allocation. Assigning tasks based on employee biorhythms can fully leverage their strengths and improve work efficiency and quality. In personnel management, understanding employee biorhythms can help rationally arrange work and rest periods, enhancing employee job satisfaction and physical and mental well-being.

[0207] (2) Group Psychological Assessment

[0208] Group Psychological Assessment Model

[0209] Build a group psychology assessment model based on emotional communication and social network structure. Analyze the path and scope of emotional information in project discussion forums and, combined with the social network structure, determine the group's psychological state. For example, when positive emotions spread rapidly and widely within a social network, team morale is high; conversely, when negative emotions spread, team morale is likely low.

[0210] We use a multi-source data fusion approach to analyze group psychology. We integrate employee behavior, communication, and performance data with project discussion forum information to comprehensively assess group psychology from multiple perspectives. For example, we can assess the overall work atmosphere and psychological state of the team by combining employee task completion and their attitude in discussion forums.

[0211] Traditional group psychology assessments rely primarily on subjective judgment or a single data source, lacking accuracy and comprehensiveness. This assessment model, which combines emotional communication with social network structure, reveals the dynamics of group psychology from the perspective of information dissemination. Multi-source data fusion analysis methods integrate diverse data and comprehensively consider multiple factors, enabling a more precise understanding of group psychology.

[0212] Comprehensively assessing group psychology from multiple dimensions ensures more accurate and reliable results. In team management, accurately understanding group psychology helps managers take timely measures to adjust team atmosphere, enhance team cohesion, and improve collaboration efficiency. During project implementation, a positive group psychology can stimulate team members' enthusiasm and creativity, increasing the probability of project success.

[0213] (3) Task allocation and coordination

[0214] Task allocation strategy

[0215] Task allocation based on biorhythms and group psychology: The system identifies the peak and trough periods of employees' biorhythms and assesses the team's collective psychological state, developing a unique task allocation strategy. For example, when team morale is low, tasks requiring high creativity and initiative can be assigned to influential team members during peak biorhythm periods, allowing their outstanding performance to boost team spirit. Simultaneously, other members can be assigned relatively easy tasks with clear goals and quick results, enhancing their sense of accomplishment and gradually boosting team morale.

[0216] Dynamic Adjustment Mechanism: During task execution, the system continuously monitors employee biorhythm data, task completion progress, and team member interactions. By establishing a dynamic adjustment model, task allocation is automatically adjusted based on real-time data changes. For example, if an employee originally assigned to a creative task experiences a drop in productivity due to biorhythm changes or other reasons, while another employee is experiencing a peak biorhythm and possesses a certain level of interest and ability in the task, the system will automatically transfer some tasks to the latter, ensuring efficient progress.

[0217] Traditional task allocation ignores biorhythms and group psychology, making it difficult to fully tap into employee potential and improve overall team effectiveness. This system's task allocation strategy fully considers individual employee differences and the overall team state. By rationally allocating tasks, it ensures optimal performance and enhances team collaboration. The dynamic adjustment mechanism, based on real-time data, can promptly respond to changes and ensure optimal task allocation at all times, a feat difficult to achieve with traditional task allocation systems.

[0218] The coordinated allocation of tasks based on biorhythms and group psychology fully unleashes employee potential, improving teamwork efficiency and work quality. In terms of resource management, rational task allocation avoids waste of human resources and improves resource utilization efficiency. In project management, it significantly increases project success rates and execution efficiency, creating greater value for the company.

[0219] 6. Problem Solving Order and Correlation Analysis

[0220] Prioritize solving the problems of dynamic skill changes and task adaptation

[0221] Reasoning: Employee skills are the core basis for task allocation. In a rapidly changing business environment, real-time understanding of employee skill dynamics is fundamental to achieving optimal task allocation. By addressing this issue, the system established a comprehensive system for collecting, analyzing, and evaluating employee skill data. For example, the operation log analysis methods and employee feedback correlation mechanisms developed during this process provide critical data support and technical foundation for addressing other issues later. This not only ensures the accuracy of current task allocation but also lays a solid foundation for analyzing the relationship between employee behavioral habits and skill improvement, as well as the impact of biorhythms on skill utilization.

[0222] This has achieved a preliminary and reasonable allocation of tasks, improved the efficiency and quality of employee task execution, and avoided resource waste and work delays caused by a mismatch between skills and tasks. In terms of personnel management, it has promoted the effective use and improvement of employee skills, which is beneficial to employee career development planning.

[0223] Solve the problem of changing behavior habits based on the results of solving skill problems

[0224] Implementation: The operational log analysis methods, data processing techniques, and in-depth understanding of employee behavioral data accumulated while addressing the dynamic evolution of skills and task adaptation can be directly applied to identifying changes in employee behavior habits and adaptively adjusting the system. For example, the monitoring and analysis of operational behavior data is used both to perceive skill changes and to identify changes in behavioral patterns. By deeply mining operational behavior data, effective management of both employee skills and behavioral habits can be achieved simultaneously. This process does not require additional steps or hardware or software investment, but leverages existing data processing processes and technologies, reducing system improvement costs and improving overall operational efficiency.

[0225] This enables the system to quickly adapt to changes in employee behavior, ensuring smooth business processes and avoiding decreased work efficiency and management chaos caused by the system's inability to adapt to changes in employee behavior. In workflow management, it improves the flexibility and adaptability of business processes and enhances the company's ability to respond to internal changes.

[0226] Based on the solution of the first two problems, the synergy between biological rhythm and group psychology is achieved.

[0227] Collaborative Implementation Process: Solving the first two problems provides the system with a rich collection of information on employee operation time, task completion, and team member interactions. This data provides a sufficient source for biorhythm analysis and group psychology assessment. By integrating and analyzing this data across multiple domains, we innovatively achieve collaborative task allocation based on biorhythms and group psychology. For example, we use employee operation time information to determine biorhythms, combine task completion status and team member interaction information to assess group psychology, and then formulate a scientific and rational task allocation strategy. This process, without adding any additional steps or hardware or software investment, further enhances the scientific and rational nature of task allocation, fully unleashes employee potential, and improves team collaboration efficiency and work quality.

[0228] Comprehensive optimization has been achieved from the individual to the team level, significantly improving the company's overall operational efficiency and competitiveness. Deep integration and collaborative optimization have been achieved in multiple aspects, including resources, workflows, personnel, and project management, bringing significant economic and management benefits to the company.

[0229] Summarize:

[0230] First, solve the real-time problem of dynamic changes in team members' skills and task adaptation

[0231] Solution:

[0232] Skill Change Perception: A skill assessment algorithm based on the similarity of operation sequences is used to compare employee operation sequences over time to calculate similarities. For example, this can be used to analyze the sequence of steps an employee takes when using a data analysis tool to determine their mastery of data analysis skills. A dynamic weighting mechanism for operation frequency and duration is also introduced. This weighting is dynamically adjusted based on the complexity and importance of the operation. For example, complex data mining operations are weighted more for accuracy and duration, while simple data cleaning operations are weighted more for frequency.

[0233] Task Adaptation and Adjustment: Using a deep task requirement decomposition algorithm based on semantic networks and business rules, we transform task requirements into semantic networks, break down each element, and assign weights. For example, for online marketing project data analysis tasks, we break down data processing elements and assign weights. We also build a two-way matching model between employee skills and task requirements, taking into account both the skill requirements of the task and the adaptability and potential for improvement of the employee's skills to the task, to assign appropriate tasks to employees.

[0234] Traditional methods analyze operation logs from only a single dimension, making it difficult to comprehensively and accurately assess employee skill changes. Furthermore, their simple and crude task breakdown and matching fail to reflect the complexity of tasks and the specific skills of employees. The new method assesses skills from multiple dimensions, including operational consistency, logic, and operational characteristics. This comprehensive matching approach, from both the task and employee perspectives, better meets actual needs.

[0235] Accurately capture the dynamic changes in employee skills without the need for additional skills testing, saving costs and ensuring accurate, real-time data. This improves the accuracy of matching tasks with employee skills, enabling scientific and rational task allocation, fully stimulating employee work potential, and providing key data support and technical foundation for subsequent problem solving. For example, this system provides operational data for behavioral habit analysis and employee skill and task allocation data for biorhythm and group psychology analysis. This system optimizes resource management, allocating human resources, and improves the rationality of task allocation.

[0236] Based on the results of solving the skill problem, the solution to the system adaptability problem caused by changes in employee behavior habits is as follows:

[0237] Identifying behavioral pattern changes: Developing a dynamic behavioral pattern recognition model based on deep reinforcement learning allows it to continuously interact with employee operational behavior data and automatically adjust parameters to adapt to behavioral pattern changes. For example, after an enterprise implements an agile project management model, the model can identify changes in employee task switching and collaborative behavior. Using a multi-dimensional behavioral data fusion analysis method, integrating operational behavior, communication behavior, and task collaboration data, it comprehensively determines whether behavioral patterns have changed. For example, it can combine comments in project discussion forums and operations in task assignment modules to determine employee work behavior patterns.

[0238] System Adaptive Adjustment: A dynamic module reconfiguration approach based on a microservices architecture dynamically reconfigures relevant modules when changes in employee behavior are identified. For example, the progress tracking module is reconfigured to meet agile project management requirements. A user-profile-based interface adaptive adjustment algorithm generates personalized user profiles based on employee behavior patterns and work requirements. When behavior patterns change, the profiles are automatically updated and the interface layout and functionality are adjusted.

[0239] Traditional behavioral pattern recognition methods lack adaptability, struggle to quickly and accurately identify dynamic changes, and lack flexibility in system adjustments. New methods leverage the self-learning and adaptive capabilities of deep reinforcement learning, integrate multi-dimensional data to avoid bias, and utilize a microservices architecture for flexible adjustments, with user profiling tailored to individual needs.

[0240] Accurately identify changes in employee behavior patterns, providing precise judgment for system adaptive adjustments. The system quickly adapts to changes in employee behavior habits, improving adaptability and user experience, ensuring smooth business processes, and avoiding efficiency decline and management chaos caused by system inadaptability. In workflow management, it improves business process flexibility and adaptability, enhancing the company's ability to respond to internal changes. This process leverages operational log analysis methods, data processing techniques, and understanding of employee behavior data accumulated during skill problem solving, without requiring additional steps or hardware or software investment.

[0241] Based on the solutions to the first two problems, a collaborative solution to task allocation based on biological rhythms and group psychology is achieved:

[0242] Biorhythm Analysis: This algorithm, based on time series clustering and cycle prediction, clusters and analyzes employee time series data over time, dividing them into time periods. Using this cycle prediction algorithm, we predict work efficiency and performance, such as identifying peak work periods. We also incorporate correlation analysis between environmental factors and biorhythms, considering the impact of work environment, task type, and other factors on biorhythms, to more accurately assess employee biorhythm status.

[0243] Group Psychology Assessment: We build a group psychology assessment model based on emotional communication and social network structure. We analyze the path and scope of emotional information dissemination in project discussion forums and use the social network structure to assess group psychology, such as judging team morale by the spread of positive or negative emotions. We use a multi-source data fusion group psychology analysis method to integrate employee behavior, communication, performance, and other data with project discussion forum information to comprehensively assess group psychology.

[0244] Task Allocation Collaboration: Task allocation is based on biorhythms and group psychology. The system determines employee biorhythm periods and the team's collective psychological state to develop a task allocation strategy. For example, when team morale is low, highly creative tasks are assigned to influential members during peak biorhythm periods, while other members are assigned easier tasks to boost morale. A dynamic adjustment mechanism continuously monitors employee biorhythms, task progress, and team interactions during task execution. Through a dynamic adjustment model, task allocation is automatically adjusted based on real-time data changes.

[0245] Traditional biorhythm analysis methods are simple and fail to consider actual work conditions and environmental factors. Group psychology assessments rely on subjective judgment or a single data source, lacking accuracy and comprehensiveness. Traditional task allocation ignores biorhythms and group psychology. The new method analyzes biorhythms and group psychology from multiple dimensions, comprehensively considers individual differences and the overall state of the team when allocating tasks, and dynamically adjusts based on real-time data to achieve collaborative optimization.

[0246] Accurately identify peak and trough periods for employee biorhythms and assess group psychological states, providing a scientific basis for task allocation. Synergizing task allocation based on biorhythms and group psychology fully unlocks employee potential, improving team collaboration efficiency and work quality. In resource management, this approach avoids waste and improves resource utilization efficiency; in project management, it increases project success rates and execution efficiency. Comprehensive optimization is achieved from the individual to the team level, achieving deep integration and collaborative optimization across multiple aspects without adding additional steps or hardware or software investment.

[0247] Through the above method, we first solve the real-time problem of dynamic changes in team members' skills and task adaptation, then solve the system adaptability problem caused by changes in employees' behavioral habits based on this, and finally realize task allocation coordination based on biological rhythms and group psychology, so as to meet the three problems of solving the real-time problem of dynamic changes in team members' skills and task adaptation, the system adaptability problem caused by changes in employees' behavioral habits, and the task allocation coordination problem based on biological rhythms and group psychology without adding any steps or any software or hardware.

Claims

1. The team management application OA intelligent processing method is characterized by: The following steps are involved: Step 1: Basic data collection and integration: Adopt a recording method based on operation step decomposition to collect employee operation logs, and use data watermark technology to mark the operation log data; The feedback information processing system based on natural language understanding and knowledge graph collects employee feedback information and uses automatic classification and priority sorting algorithms to classify and sort the feedback information; Use a combination of sentiment analysis and topic modeling, as well as social network dynamic modeling technology to collect project discussion area information; Step 2: Skill change perception: This system uses a skill assessment algorithm based on the similarity of operation sequences to compare employees' operation sequences at different times and calculate similarities. It also introduces a dynamic weighting assessment mechanism based on operation frequency and time consumption. Task adaptation and adjustment: Using a deep task requirement decomposition algorithm based on semantic networks and business rules, we transform task requirements into semantic networks, refine each element, assign weights, and build a two-way matching model between employee skills and task requirements. Step 3: Identification of behavioral pattern changes: Build a dynamic behavioral pattern recognition model based on deep reinforcement learning, and adopt a multi-dimensional behavioral data fusion analysis method to integrate employees' operational behavior data, communication behavior data, task collaboration data, etc. for analysis; System adaptive adjustment: Dynamically reconfigure relevant modules based on the dynamic module reconstruction method of the microservice architecture. Utilize the user portrait-based interface adaptive adjustment algorithm to generate personalized user portraits based on employee behavior patterns and work needs, and adjust the interface layout and function display. Step 4: Biorhythm analysis: A biorhythm analysis algorithm based on time series clustering and cycle prediction is proposed. This algorithm performs cluster analysis and cycle prediction on employees' long-term operation time series data, and introduces correlation analysis between environmental factors and biorhythms. Group psychology assessment: Build a group psychology assessment model based on emotional communication and social network structure, adopt a multi-source data fusion group psychology analysis method, and integrate employee behavior data, communication data, performance data, etc. with project discussion area information for analysis; Task allocation coordination: Develop task allocation strategies based on biological rhythms and group psychology, and establish a dynamic adjustment mechanism to automatically adjust task allocation according to real-time data changes during task execution; The operation sequence similarity of the skill evaluation algorithm based on operation sequence similarity in skill change perception is calculated as: Among them, Sim(S1, S2) represents the similarity of two operation sequences S1 and S2, S1 = [s 11 ,s 12 ,…,s 1L ],S2=[s 21 ,s 22 ,…,s 2L ], L is the length of the shorter of the two sequences; Match(s 1i ,s 2i ) is a matching function; |S1| and |S2| are the lengths of the operation sequences S1 and S2 respectively; Comprehensive skill evaluation formula considering operation frequency and time consumption: Among them, Skill(t) represents the skill level of the employee at time, S t and S t-Δt are the operation sequences at time t and t-Δt respectively; w1, w2, w3 are weight coefficients, and w1+w2+w3=1, which respectively represent the importance of operation sequence similarity, operation frequency, and operation time in skill evaluation; i Indicates the i-th operation, Weight(o i ) is the operation o i The weight of Freq(o i ) is the operation o i Frequency, Time(o i ) is the operation o i The average time taken.

2. The team management application OA intelligent processing method according to claim 1, characterized in that: The importance evaluation formula for the automatic classification and prioritization algorithm in the employee feedback information processing system is: Where I represents the importance score of the feedback information, M is the number of key factors in the knowledge graph related to the feedback information, and w m is the weight of the mth key factor, K m is to extract the content related to the mth key factor from the feedback information, f m It is a function that quantitatively evaluates the extracted content based on the key factor type; The urgency assessment formula is: Among them, E represents the urgency score of the feedback information, N is the number of judgment factors related to the urgency, and v n is the weight of the nth factor, T n is the time information related to urgency extracted from the feedback information, g n It is a function that quantifies the urgency based on time information; The comprehensive priority score formula is: P=α×I+β×E Wherein, P is the comprehensive priority score of the feedback information, α and β are the weight coefficients of importance and urgency respectively, and α+β=1.

3. The team management application OA intelligent processing method according to claim 1, characterized in that: The task requirement semantic network of the task requirement deep decomposition algorithm based on semantic network and business rules in task adaptation adjustment is constructed as follows: G=(V,E) Among them, G represents the semantic network of task requirements, V is a set of nodes, and each node v i ∈V represents an element of the task, E is a set of edges, and each edge e ij ∈E represents the element v i and v j the relationship between; Factor refinement and weight allocation formula: Among them, W(v i ) is the node v i The weight of node v is k. i The number of associated nodes, Influence(v j ,v i ) represents node v j For node v i The degree of influence, Impor tan ce(v j ) is the node v j Importance score.

4. The team management application OA intelligent processing method according to claim 1, characterized in that: The time series clustering formula of the biorhythm analysis algorithm based on time series clustering and cycle prediction in biorhythm analysis is: Among them, C is the number of cluster categories, S i is the i-th cluster, x j is the jth data point in the time series, μ i is the center of the i-th cluster; Cycle prediction algorithm formula: Where y(t) is the predicted employee's work efficiency or ability performance at time, N is the number of period components, and A n is the amplitude of the nth periodic component, f n is the frequency of the nth periodic component, is the phase of the nth periodic component, and ∈(t) is the error term.

5. The team management application OA intelligent processing system is characterized by: The team management application OA intelligent processing method according to any one of claims 1 to 4 is applied, and the system includes: Data collection layer: This layer uses an event-driven, native data capture mechanism within the system. Code snippets are embedded at key operational nodes in each business module to capture data. Data mapping tables are used to map and integrate operational data from different business systems in a unified format. Data storage layer: We use the MySQL relational database and improve its distributed storage architecture. We use a proprietary distributed storage algorithm to implement sharded storage based on business type, time, and other dimensions. We also dynamically adjust the distribution of storage nodes based on access frequency. We also use a distributed ledger mechanism based on blockchain technology to perform redundant storage and consistency verification for key data. Data analysis layer: Build a multimodal data analysis algorithm model that integrates semantic analysis, behavioral pattern recognition, and time series analysis. Adopt a cross-domain knowledge fusion approach to integrate knowledge from psychology, management, and other fields into the algorithm model. Application layer: Design an intelligent interactive interface based on user behavior prediction. By analyzing employees' historical operation behaviors and business needs, it predicts the employees' next operation and displays relevant functions and data in advance. At the same time, it introduces emotional design concepts and adjusts the interface style and prompt information according to employees' work status and emotional feedback.

6. The team management application OA intelligent processing system according to claim 5, characterized in that: The data acquisition layer is based on the native data capture mechanism in the event-driven system, and code snippets are implanted in the key operation nodes of each business module.

7. The team management application OA intelligent processing system according to claim 5, characterized in that: The shard storage formula of the self-developed distributed storage algorithm in the data storage layer is: S i =Hash(B i ,T i )mod N Among them, S i Indicates the target server node number for data storage, B i Represents the business type code, T i is the timestamp of data generation, N is the total number of server nodes; The formula for dynamically adjusting storage nodes is: Among them, P j represents the probability of data being stored in the jth server node, F j is the access frequency of data on the jth server node.

8. The team management application OA intelligent processing system according to claim 5, characterized in that: The intelligent interactive interface based on user behavior prediction in the application layer analyzes employees' historical operation behavior data and uses machine learning algorithms to build a prediction model to predict the employees' next operation.

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