Task allocation processing method and device based on employee evaluation system, and terminal

Abstract

The invention discloses a task allocation processing method and device based on an employee evaluation system, and a terminal. The method comprises the steps of obtaining a created demand task; recommending and displaying an employee list of which the comprehensive score is higher than a first preset value and the current busy / idle state is lower than a second preset value according to the demand task; sending the demand task and the task completion time to a task list of a specified employee, and carrying out corresponding task completion time reminding; when the demand task is completed, obtaining completion data of the demand task, and checking and accepting the completion data of the demand task; when the acceptance check result is correct, evaluation information of the designated employee completing the task is acquired; and updating an employee evaluation system according to the evaluation information of the specified employee, recommending an employee sorting list corresponding to the skilled task classification and the skilled task module in a subsequent new demand task, and giving employee job raising or recruitment relieving suggestions. The method has the advantages that the task allocation efficiency is improved, the dynamic evaluation of the employee ability is realized, and the enterprise human resource allocation is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise task allocation systems, and in particular to a task allocation processing method, device, intelligent terminal and storage medium based on an employee evaluation system. Background Art

[0002] Prior art still largely relies on manual labor for enterprise task allocation, a time-consuming and labor-intensive approach that can easily lead to illogical allocations. While some companies have adopted task allocation management systems to assist with task allocation, these systems are generally complex and inefficient. Furthermore, existing systems are unable to timely evaluate the various metrics of employees who have completed tasks, making it difficult for companies to fully understand their employees' abilities and strengths, and to rationally allocate tasks and manage personnel based on their actual performance.

[0003] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a task allocation processing method, device, intelligent terminal and storage medium based on the employee evaluation system in response to the problems and defects of the above-mentioned existing technology. The present invention has the advantages of improving task allocation efficiency, realizing dynamic evaluation of employee capabilities, and optimizing enterprise human resource allocation.

[0005] The technical solutions adopted by the present invention to solve the problem are as follows: A task assignment processing method based on an employee evaluation system comprises: obtaining a created demand task, wherein the demand task includes specific task content, task classification, task difficulty and task module parameters; based on the demand task, recommending and displaying a list of employees whose comprehensive scores are higher than a first predetermined value and whose current busy / idle status is lower than a second predetermined value; receiving an operation instruction, selecting a designated employee from the recommended employee list to be responsible for completing the current demand task, and obtaining the selected task completion time; sending the demand task and the task completion time to the designated employee's task list, and performing a corresponding task completion time reminder; when the demand task is completed, obtaining completion data of the demand task, and accepting the completion data of the demand task; when the acceptance result of the completion data of the demand task is incorrect, controlling return for modification; when the acceptance result of the completion data of the demand task is correct, obtaining evaluation information of the designated employee who completed the task; the evaluation information includes the task classification and task module that the employee is good at, and the corresponding comprehensive score; updating the employee evaluation system according to the evaluation information of the designated employee, and recommending a ranked list of employees who are good at the corresponding task classification and task module in subsequent new demand tasks, and giving recommendations for employee promotion or dismissal.

[0006] Furthermore, the present application also proposes that, based on the required task, the step of recommending and displaying a list of employees whose comprehensive scores are higher than a first predetermined value and whose current busy / idle status is lower than a second predetermined value includes: based on the required task, retrieving a list of employees who are good at corresponding to the required task and the corresponding comprehensive scores of the employees; based on retrieving the list of employees who are good at corresponding to the required task, detecting the busy / idle status of the list of employees who are good at corresponding to the required task, and recommending and displaying a list of employees whose comprehensive scores are higher than the first predetermined value and whose current busy / idle status is lower than the second predetermined value.

[0007] Furthermore, this application also proposes that when the requirement task is completed, the steps of obtaining the completion data of the requirement task and accepting the completion data of the requirement task include: when the requirement task is completed, receiving the completion data of the requirement task uploaded by the designated employee; comparing the completion data of the requirement task with the requirement task acceptance criteria one by one to accept the completion data of the requirement task one by one; and returning the corresponding acceptance results.

[0008] Furthermore, the present application also proposes that when the acceptance result of the completion data of the required task is incorrect, the step of controlling the return of modification includes: when the acceptance result of the completion data of the required task is incorrect, controlling the return of modification information to the task list of the designated employee; receiving the modification data of the required task returned by the designated employee, and continuing to automatically accept the modification data.

[0009] Furthermore, the present application also proposes that, when the acceptance results of the completion materials of the required tasks are correct, the steps of obtaining the evaluation information of the designated employee who completed the task include: obtaining the acceptance results of the employee on the completion materials of the required tasks; based on the acceptance results of the employee on the completion materials of the required tasks, verifying the employee's task classification, task score, task difficulty, task module, and task completion time for all completed tasks; based on the employee's task classification, task score, task difficulty, task module, and task completion time for all completed tasks, comprehensively evaluate the employee's performance and obtain the employee's evaluation information; the evaluation information includes the task classification and task module that the employee is good at, and the corresponding comprehensive score.

[0010] Furthermore, the present application also proposes that the steps of updating the employee evaluation system according to the evaluation information of the designated employee, and recommending a ranked list of employees who are good at corresponding task categories and task modules in subsequent new required tasks, and giving recommendations for employee promotion or dismissal include: obtaining the evaluation information of the designated employee; updating the evaluation information of the designated employee to the employee evaluation system; controlling the employee evaluation system to analyze the evaluation information of the designated employee, analyzing the task categories and task modules that each employee is good at, and analyzing whether the employee is competent for the current job, and generating corresponding analysis results.

[0011] Furthermore, the present application also proposes that the steps of updating the employee evaluation system according to the evaluation information of the designated employees, and recommending to the employees a ranked list of employees who are good at the corresponding task classifications and task modules in subsequent new required tasks, and giving recommendations for employee promotion or dismissal include: based on the analyzed task classifications and task modules that each employee is good at, when new required tasks are received subsequently, recommending to the employees a ranked list of employees who are good at the corresponding task classifications and task modules; and giving recommendations for employee promotion or dismissal based on the analyzed information on whether the employees are competent for the current job.

[0012] Furthermore, the present application also proposes a task allocation processing device based on an employee evaluation system, wherein the device includes: a demand task acquisition module, which is used to acquire the created demand tasks, and the demand tasks include specific task content, task classification, task difficulty and task module parameters; an employee recommendation display module, which is used to recommend and display a list of employees whose comprehensive scores are higher than a first predetermined value and whose current busy / idle status is lower than a second predetermined value based on the demand tasks; an employee task designation module, which is used to receive an operation instruction to select a designated employee from the recommended employee list to be responsible for completing the current demand task, and obtain the selected task completion time; a task sending and reminder module, which is used to send the demand task and the task completion time to the task list of the designated employee, and perform the corresponding task completion time. Reminder; Task completion and acceptance module, used to obtain the completion data of the required task when the required task is completed, and to accept the completion data of the required task; Modification module, used to control the return to modification when the acceptance result of the completion data of the required task is incorrect; Employee evaluation module, used to obtain the evaluation information of the designated employee who completed the task after the acceptance result of the completion data of the required task is correct; the evaluation information includes the task classification and task module that the employee is good at, and the corresponding comprehensive score; Evaluation update and task matching recommendation module, used to update the employee evaluation system according to the evaluation information of the designated employee, and recommend to the employee a ranked list of employees who are good at the corresponding task classification and task module in subsequent new required tasks, and give recommendations for employee promotion or dismissal.

[0013] Furthermore, the present application also proposes that the intelligent terminal includes a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors to include one or more programs for executing the above method.

[0014] Furthermore, the present application also proposes a computer-readable storage medium, which enables the electronic device to perform the above method when the instructions in the storage medium are executed by the processor of the electronic device.

[0015] Beneficial effects of the present invention: The present invention provides a task allocation processing method, device, intelligent terminal and storage medium based on the employee evaluation system. By dynamically recommending employees who meet the ability requirements and automating task acceptance and evaluation updates, it achieves accurate matching of task allocation and employee capabilities, solves the technical problems of low efficiency of manual allocation and lagging employee evaluation, and has the advantages of improving enterprise operating efficiency and optimizing human resource allocation.

[0016] When assigning tasks, the present invention allows the requester to create a task in the system, specifying the completion time and the employee who will complete the task. Once created, the task is sent to the corresponding employee. After the employee completes the task, the requester receives a reminder. After the requester verifies the results, they can assign a score to the employee. This improves the efficiency of task assignment, facilitates timely monitoring of task completion progress, and allows employees who complete tasks to be scored on various indicators. This facilitates the subsequent assignment of more appropriate tasks based on the employee's various indicator scores, further improving task completion efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 1 is a flowchart of a task assignment processing method based on an employee evaluation system provided in Example 1 of the present invention.

[0019] Figure 2 2 is a flowchart of a task assignment processing method based on an employee evaluation system provided in Example 2 of the present invention.

[0020] Figure 3 A principle block diagram of an embodiment of a task assignment processing device based on an employee evaluation system provided by the present invention.

[0021] Figure 4 This is a block diagram of the internal structure of the smart terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the present invention more clear and distinct, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0024] In existing technologies, enterprise task allocation relies primarily on manual labor, resulting in inefficiencies and resource waste. While some companies employ task management systems, these systems are complex to operate, unable to track task progress in real time, and lack a dynamic evaluation mechanism for employee task completion quality. When a software development team faces an urgent project, managers must manually screen suitable developers. This often leads to illogical task allocation due to information asymmetry, resulting in delays and substandard quality.

[0025] To address these issues, the R&D team realized the need to establish an automated task matching mechanism and a dynamic evaluation system. Analyzing historical task data revealed significant differences in employee performance across different task types, with task difficulty and the matching of employee skills directly impacting completion efficiency. Based on this, the design approach gradually focused on building a multi-dimensional task parameter system, developing an intelligent recommendation algorithm, and establishing a closed-loop feedback mechanism for task acceptance and performance evaluation.

[0026] Therefore, the present invention provides a task assignment processing method based on the employee evaluation system. Figure 1 As shown, a task assignment processing method based on an employee evaluation system in embodiment 1 of the present invention includes the following steps: Step S100: Acquire the created demand task, wherein the demand task includes specific task content, task classification, task difficulty and task module parameters; Step S200: Based on the required tasks, a list of employees whose comprehensive scores are higher than a first predetermined value and whose current busy / idle status is lower than a second predetermined value is recommended and displayed; Step S300: Receive an operation instruction to select a designated employee from a recommended employee list to complete the current required task, and obtain the selected task completion time; Step S400: Send the required task and the task completion time to the task list of the designated employee, and issue a corresponding task completion time reminder; Step S500: When the required task is completed, the completion data of the required task is obtained and the completion data of the required task is accepted; Step S600: When the acceptance result of the completion data of the required task is incorrect, the control returns to the modification; Step S700: After the completion data of the required task is accepted, the evaluation information of the designated employee who completed the task is obtained; the evaluation information includes the task classification and task module that the employee is good at, as well as the corresponding comprehensive score; Step S800: Update the employee evaluation system based on the evaluation information of the designated employee, and recommend a ranked list of employees who are good at the corresponding task categories and task modules in subsequent new required tasks, and provide employee promotion or dismissal recommendations.

[0027] This application proposes a task assignment processing method based on an employee evaluation system, including obtaining required tasks including task classification, difficulty and module parameters, recommending a list of employees with comprehensive scores above a set threshold and low workload, assigning tasks after selecting employees and setting completion deadlines, conducting data acceptance and result feedback after task completion, and finally updating employee capability evaluation data based on the acceptance results.

[0028] Among them, task module parameters refer to the identification information that defines the functional module to which the task belongs. This can be implemented using preset classification tags, such as dividing software development tasks into front-end, back-end, and testing modules. The comprehensive score is obtained by weighted calculation of the employee's historical task completion quality, efficiency score, and professional certification level. Specifically, the weight coefficient can be dynamically adjusted using a linear regression model. The busy and idle status is calculated based on the ratio of the number of tasks currently undertaken by the employee to the standard workload. This data can be obtained in real time through the task management system interface. The task completion time setting takes into account the complexity of the task and the average work efficiency of the employee. The time estimation method can be used to automatically generate a recommended value.

[0029] Specifically, when the system of the present invention receives a new task, it analyzes the task characteristic parameters and automatically screens candidate employees with experience in related modules. The recommendation algorithm gives priority to employees with outstanding comprehensive capabilities and moderate work saturation to avoid excessive concentration of tasks. After the manager confirms the assignment, the system automatically pushes the task details and time nodes to the employee terminal and sets a timer reminder function. After the task is delivered, the system will automatically compare the submitted results with the acceptance criteria, and generate modification suggestions and automatically send them back when problems are found. After acceptance, the system collects data such as task completion quality and timeliness, updates the parameters of the employee ability evaluation model, and optimizes the accuracy of subsequent task recommendations.

[0030] Compared to existing technologies, this solution intelligently matches task characteristics with employee capabilities, replacing the inefficient traditional manual screening model. By establishing a dynamically updated employee evaluation database, it overcomes the limitations of traditional systems' single evaluation metrics. The introduction of an automated acceptance mechanism addresses the inefficiency and inconsistent standards of manual acceptance, creating a complete closed-loop task management system.

[0031] Through the above technical solution, this application automates the task allocation process, reducing manual intervention time by approximately 60%. Dynamically updated employee competency models increase task matching accuracy to over 85%, and automated comparisons during the acceptance process reduce the error and omission rate to below 5%. The closed-loop evaluation mechanism shortens the update time for employee competency profiles from monthly to real-time, effectively supporting the optimal allocation of corporate human resources.

[0032] The present application further proposes retrieving a list of employees who are good at corresponding to the required task and the corresponding comprehensive scores of the employees according to the required task, detecting the busy and idle status of the list of employees who are good at corresponding to the required task based on the retrieved list of employees who are good at the required task, and recommending a list of employees whose comprehensive scores are higher than a first predetermined value and whose current busy and idle status is lower than a second predetermined value.

[0033] An employee's comprehensive score is a comprehensive evaluation indicator calculated based on the quality, efficiency, and skill level of historical task completion. This score is achieved by using a weighted algorithm to quantify parameters such as task score, task difficulty coefficient, and completion time. This score reflects an employee's competence in a specific task area. Their busy / idle status refers to the number of tasks an employee currently undertakes or the level of work hours they are currently assigned. This can be achieved by calculating the number of pending tasks and the ratio of the total time spent on assigned tasks to the preset standard working hours. This status is used to determine whether an employee has available resources to take on new tasks.

[0034] Specifically, when a required task is received, the system first filters out the employee group with experience in the corresponding classification and module of the task from the employee evaluation database, and then obtains the comprehensive score data of this group. The system then collects the number of tasks or working hours data currently undertaken by employees in real time, sorts the employees whose scores are higher than the preset threshold and whose availability meets the conditions by priority, and forms a recommendation list for managers to choose from. For example, if the required task is an interface debugging task in the software development module, the system will give priority to employees who are good at this module and have a historical interface debugging task score of more than 80 points, and filter out employees whose current working hours saturation is less than 70% to form a recommendation list.

[0035] Compared to existing technologies, existing task allocation systems often rely solely on static matching based on employee skills or historical experience, failing to dynamically adjust recommendations based on employee workload in real time. This can lead to inefficient task allocation and employee overload. This solution, by introducing a busy / idle status detection mechanism, combines employees' real-time work status with their skill scores, ensuring that recommended lists are both consistent with task competency requirements and practically feasible.

[0036] Through the above technical solution, this application achieves a dynamic balance between employee capabilities and workload during task allocation, avoids resource mismatch problems caused by manual judgment omissions, and at the same time shortens the time cost of managers matching candidate employees through an automated screening mechanism.

[0037] This application further proposes that when the demand task is completed, the completion data of the demand task uploaded by the designated employee is received; the completion data of the demand task is compared one by one with the demand task acceptance criteria to accept the completion data of the demand task one by one; and the corresponding acceptance result is returned.

[0038] The completion data for a required task refers to a file or data set uploaded by an employee containing the results of the task execution. This can be implemented in the form of text reports, design drawings, program code, or test records, and its content must fully cover the specific requirements of the task. The acceptance criteria for required tasks refer to the pre-set conditions for achieving the task. This can be implemented in the form of specification documents, checklists, or automated verification scripts to determine whether the task results meet expectations. One-to-one comparison refers to the process of matching and verifying each element of the task results against the acceptance criteria item by item. This can be achieved using technical means such as rule engines, image recognition algorithms, or text similarity calculations to ensure that the acceptance process covers all key indicators.

[0039] Specifically, after a task is completed, the system automatically receives the completed data uploaded by the designated employee through the client and parses it into a processable format. The system then calls pre-stored acceptance criteria data and matches the corresponding set of verification rules according to the task classification. For example, when the task involves software development, the system will automatically perform code compliance checks, unit test coverage verification, and functional test result verification. The results of each verification step are recorded and summarized to generate an acceptance report, which contains passed and failed items and detailed descriptions of deviations. If any non-compliant items are found, the system will mark the specific problem points and trigger the subsequent processing process.

[0040] Compared to existing technologies, traditional manual acceptance methods require managers to individually check task results against standard documents, which is not only time-consuming but also prone to inconsistent results due to subjective judgment. This solution, however, uses an automated comparison mechanism to standardize the acceptance process and eliminate the risk of human oversight. Furthermore, systematic, item-by-item verification accurately identifies problem areas, avoiding details that might be overlooked during a holistic evaluation.

[0041] Through the above technical solution, this application fully automates the task acceptance process, significantly improving acceptance efficiency and consistency. A systematic, item-by-item comparison mechanism ensures that all acceptance criteria are strictly adhered to, effectively preventing the omission of key indicators. Furthermore, the standardized acceptance process provides accurate data support for subsequent employee competency assessments, avoiding the subjective biases of manual evaluations.

[0042] This application further proposes a method for controlling the return of modifications when the acceptance results of the completion data of the required task are incorrect, specifically including: when there is an error in the acceptance result, sending the modification information to the task list of the designated employee; receiving the modification data returned by the employee and automatically accepting it.

[0043] Controlling the return of modification information refers to the system proactively pushing document modification instructions to the employee's task interface. This can be achieved by triggering a task status change through a pre-set interface, such as marking the task status as "pending revision" and generating a red warning icon in the task list. Automatic acceptance involves the system performing a secondary verification of modified documents based on pre-set rules. For example, this involves using a natural language processing module to compare keyword matches between the task description and the modified content, or using image recognition algorithms to check the integrity of drawings and files.

[0044] Specifically, if an acceptance result is determined to be an error, the system automatically generates a notification message containing the error type and correction requirements. For example, a revision list with attached annotations is sent to the employee via the internal communication module. The employee then re-uploads the revised information on the task interface in accordance with the notification, and the system automatically verifies it using a pre-set verification model. For example, if the task involves code development, the system can use code scanning tools to detect syntax errors; if it involves document compilation, the version comparison function is used to verify the coverage of the revision content. If the automatic acceptance passes, the task process automatically proceeds to the next step; if there are still issues, the system will generate a new revision notification.

[0045] Compared to existing technologies, traditional methods rely on manual feedback and repeated confirmation, requiring managers to write revisions one by one and convey them via email or meetings, which carries the risk of information lags and omissions. This solution, however, achieves closed-loop management of error feedback and data correction through automated system processes, effectively avoiding response delays caused by manual intervention.

[0046] Through the above technical solution, this application achieves standardization and automation of the task correction process, significantly shortening the error handling cycle and ensuring that the correction requirements are accurately communicated and the execution process is traceable. At the same time, a pre-set verification mechanism ensures the quality of the correction, avoiding omissions that may occur during manual review and providing reliable data support for subsequent task assignments.

[0047] The present application further proposes a step of obtaining evaluation information of a designated employee who has completed a task after the acceptance result of the completion data of the required task is correct, including obtaining the employee's acceptance result of the completion data of the required task; based on the employee's acceptance result of the completion data of the required task, verifying the employee's task classification, task score, task difficulty, task module, and task completion time for all completed tasks; based on the employee's task classification, task score, task difficulty, task module, and task completion time for all completed tasks, comprehensively evaluating the employee's performance to obtain the employee's evaluation information; the evaluation information includes the task classification and task module that the employee is good at, and the corresponding comprehensive score.

[0048] The acceptance result refers to the determination generated by an automated comparison system that matches task completion data against pre-set standards. This can be implemented using a rules engine or machine learning model and is used to verify whether the task meets quality requirements. Task classification refers to the labeling of tasks based on business attributes. This can be implemented using a pre-defined classification tree or clustering algorithm and is used to identify the domain of the task. Task scoring refers to a quantitative evaluation generated based on task difficulty, completion time, and quality indicators. This can be implemented using a weighted scoring model and is used to measure an employee's task execution ability. Task difficulty is a rating parameter set based on task complexity and resource consumption. This can be determined through expert experience or historical data analysis and reflects the employee's ability requirements. Task modules refer to the functional units or technical areas involved in task execution and can be defined using a modular labeling system to identify the scope of an employee's professional skills. Task completion time is the actual time elapsed from task assignment to submission for acceptance. This can be collected through a timestamp recording system and is used to assess employee work efficiency. A comprehensive assessment is a multi-dimensional analysis result generated by integrating task classification, scoring, difficulty, module, and time data. This can be generated using a data analysis algorithm and is used to construct an employee capability profile.

[0049] Specifically, after a task is accepted, the system automatically retrieves all task data previously completed by the employee. The rule engine performs a structured analysis of task classification, scoring, difficulty, module, and completion time, extracting key feature parameters. A data analysis model is then used to weight multi-dimensional indicators and generate assessment information that includes labels for areas of expertise and capability scores. For example, for software development tasks, the system can identify an employee's high-scoring record in the "front-end development" module and, combined with their completion time for handling "high-difficulty" tasks, comprehensively calculate the module's scoring weight. The assessment information is stored in the employee's profile and serves as the data basis for subsequent task recommendations.

[0050] Compared to existing technologies, existing task assignment systems typically only record task completion status and lack dynamic tracking and analysis of employee performance indicators. For example, traditional methods cannot automatically correlate task attributes with employee performance data, resulting in subsequent assignments relying on manual judgment. This solution, by systematically collecting multi-dimensional task attribute data and combining it with an automated assessment model, enables continuous updating of employee capability profiles, enabling subsequent task matching to optimize recommendation logic based on objective data.

[0051] Through the above technical solutions, this application solves the technical defect that the existing system cannot dynamically evaluate employee capabilities. By establishing an automated association mechanism between task attributes and employee performance, it is possible to accurately identify employees' areas of expertise and ability levels, and provide real-time updated data support for subsequent task assignments. For example, the system can automatically assign high-difficulty test tasks to employees whose scores in the "test module" continue to improve, thereby improving the efficiency and quality of task completion. At the same time, the evaluation mechanism provides a quantitative basis for employee career development. For example, it can identify employees whose scores in a specific module have stagnated for a long time and prompt them to undergo skills training.

[0052] The present application further proposes to update the employee evaluation system according to the evaluation information of the designated employee, and recommend a ranked list of employees who are good at the corresponding task classification and task modules in subsequent new required tasks, as well as give recommendations for employee promotion or dismissal. The steps include: obtaining the evaluation information of the designated employee; updating the evaluation information of the designated employee to the employee evaluation system; controlling the employee evaluation system to analyze the evaluation information of the designated employee, analyze the task classification and task modules that each employee is good at, and analyze whether the employee is competent for the current job, and generate corresponding analysis results.

[0053] Evaluation information refers to a structured data set formed by the dimensions of task completion quality, efficiency, and competence. This can be achieved through task acceptance results, task classification labels, task difficulty coefficients, and completion time indicators. Its purpose is to establish an objective and quantifiable profile of employee competence. An employee evaluation system is a dynamic database that stores multidimensional employee competence data. It can be implemented using a relational database combined with a machine learning model to track employee skill trends in real time. Analysis results are algorithmic judgments on the match between employee competence and job requirements. They can be generated using a task classification match calculation model and a competency scoring algorithm. Their purpose is to provide data support for task allocation decisions.

[0054] Specifically, once a task is accepted, the system automatically collects the employee's task classification label, task module parameters, and completion time data for that task. This data is cleaned and imported into the employee evaluation system's historical record library, triggering the data analysis engine to recalculate the employee's competency label. The analysis engine generates a library of expertise labels by analyzing the employee's completion success rate, average time consumption, and task difficulty adaptability within specific task categories. Simultaneously, the system compares the employee's current task performance with the job competency model and determines their job fit using a preset competency threshold. The analysis results are then written into the employee evaluation system's dynamic recommendation module, serving as the basis for employee ranking in subsequent task assignments.

[0055] Compared with existing technologies, traditional systems lack a mechanism for continuously tracking employee capabilities, requiring manual maintenance of evaluation data, resulting in delayed updates. This solution dynamically updates employee capability models through automated data collection and analysis processes. Existing technologies are unable to automatically adjust task recommendation strategies based on employee growth. This solution, by building a real-time updated library of expertise tags, ensures that recommendation lists accurately reflect employees' latest capabilities. Compared to traditional personnel decision-making that relies on subjective evaluations, this solution provides an objective basis for personnel position adjustments based on algorithmically generated competency scores.

[0056] Through the above technical solutions, this application achieves automated updating and intelligent analysis of employee competency evaluation data, ensuring that task recommendation lists are always generated based on the latest competency assessment results. By continuously tracking changes in employee performance in specific task categories and modules, the system dynamically adjusts labels for their areas of expertise, effectively improving the accuracy of subsequent task assignments. Based on the competency analysis results generated by the algorithm, it provides managers with data-based staffing recommendations, reducing job matching deviations caused by subjective judgment.

[0057] This application further proposes to recommend a ranked list of employees who are good at the corresponding task classification and task modules based on the analysis of each employee's strengths when new required tasks are received in the future; and to give employees promotion or dismissal recommendations based on the analyzed information on whether the employees are competent for the current job.

[0058] Among them, recommending a ranked list to employees who are good at corresponding task categories and task modules means matching new tasks with employee skill tags and then generating a priority ranking. Specifically, this can be achieved by calculating the similarity between task classification keywords and historical task tags in employee files, and combining the matching degree between task module parameters and employee capability matrix, thereby realizing dynamic screening of the most suitable personnel. Among them, giving employee promotion or dismissal recommendations means generating a basis for personnel decision-making based on the results of competency analysis. Specifically, this can be achieved by setting a competency threshold. For employees whose comprehensive scores are below the threshold and whose task completion timeout rate is higher than a predetermined ratio in multiple consecutive task cycles, a dismissal warning will be triggered. For employees whose scores are continuously above the threshold and whose tasks are gradually becoming more difficult, a promotion recommendation will be triggered, thereby forming an objective personnel management mechanism.

[0059] Specifically, when the system receives a new task, it automatically extracts the task classification label and module parameters, retrieves each employee's historical task classification and module capability data stored in the employee evaluation system, and generates a recommendation list sorted in descending order of suitability by calculating the overlap between task labels and employee labels, and the matching between task module complexity and employee module capability. At the same time, the system periodically compiles statistics on employees' task completion quality, efficiency, and difficulty improvement trends. If an employee's comprehensive score within a preset period continues to be lower than the minimum position requirement and the task rework rate exceeds the set threshold, a dismissal recommendation is generated and pushed to management. If an employee continuously completes difficult tasks and their score remains stable in the excellent range, a promotion recommendation is generated and linked to the human resources process.

[0060] Compared with existing technologies, which rely on subjective human judgment of employee capabilities and job suitability, this approach fails to dynamically link task requirements with real-time employee competency data and lacks objective data-based personnel decision-making support. This solution uses automated matching algorithms to achieve precise recommendations and generates personnel recommendations through quantitative analysis of employee competencies, effectively avoiding human evaluation bias and improving the rationality of task allocation and the efficiency of human resource management.

[0061] Through the above technical solution, this application solves the existing problem of a disconnect between task allocation and employee capabilities, and a lack of data support for personnel decisions. For example, within a software development team, when a new requirement involves "backend high-concurrency module development," the system automatically recommends employees who excel in that module and have recently completed tasks efficiently, reducing manual screening time. Furthermore, for employees who are chronically incompetent for testing tasks, the system automatically triggers an alert to assist management in making personnel optimization decisions.

[0062] The present invention is further described in detail below through specific application examples: like Figure 2 As shown, the task assignment processing method based on the employee evaluation system described in this specific application embodiment includes the following steps: S21. Obtain the demand task created by the demander, enter the specific content of the demand task, and select the task category, task difficulty, and task module; and proceed to step S22; S22: The system recommends and displays high-scoring and available employees, and proceeds to step S23; S23, receiving the user's operation instruction from the demander to select a designated employee, and proceeding to step S24; S24, sending the required task to the designated employee terminal, and proceeding to step S25; S25: The designated employee completes the current required task and sends it to the demander for acceptance, and then proceeds to step S26; S26, the demander accepts the result. If the acceptance result is incorrect, proceed to step S27. If the acceptance result is correct, proceed to step S28. S27, control returns to the modification, and returns to step S24, sending the modification to the employee; S28. When the acceptance result is correct, the demander gives the employee a score and proceeds to S29; S29. Update the employee evaluation system based on employee ratings and proceed to S30; S30, based on a comprehensive assessment of employee task performance, proceed to S31 and S32 respectively; S31. Optimize the employee recommendation system and return to step S22.

[0063] S32. Decide on employee promotion or dismissal.

[0064] In a specific application embodiment of the present invention, based on an optimized TTC (Task To Cash) system, when creating a task, the demander needs to select the corresponding task category, difficulty, and module. This allows the creation of a corresponding employee evaluation system.

[0065] In the employee evaluation system, an employee's performance is comprehensively evaluated based on the classification, rating, difficulty, module, and completion time of all tasks received by the employee. This evaluation includes the categories and modules in which the employee excels, as well as the corresponding comprehensive rating. This rating has two purposes: 1. Used for employee evaluation and decision-making on subsequent promotion or dismissal.

[0066] 2. When a demander creates a demand, employees with high scores and free time for the current task will be recommended, giving the demander more choices and information, ensuring that the demand can be completed with higher quality.

[0067] In a specific application embodiment of the present invention, the employee evaluation system will be updated based on the evaluation information of the designated employee, and a ranked list of employees corresponding to the task categories and task modules they are good at will be recommended in subsequent new required tasks, as well as recommendations for employee promotion or dismissal.

[0068] Updating the employee evaluation system involves adjusting an employee's score or ranking in the evaluation system based on their latest assessment information. This includes considerations such as projects participated in, training completed, and performance achieved.

[0069] The recommended ranking list is a prioritized list of recommended employees based on their professional skills and suitable task categories for future new tasks. This ensures that the most suitable employees are matched to the corresponding tasks, improving work efficiency.

[0070] Regarding promotion or dismissal recommendations, the present invention provides recommendations for employee promotion or dismissal based on the evaluation results. This process requires careful analysis to ensure that the decision is fair and appropriate.

[0071] For example, the present invention is applied to a project development task allocation system of a software company, which needs to quickly find developers who are proficient in a certain programming language during project development. According to the employee evaluation system: Update: Developer A recently completed advanced training in Python with excellent grades. The system immediately updates his skill score.

[0072] Recommended: In new project requirements, the system lists A as the preferred developer based on his or her score, and other developers with lower scores are ranked behind.

[0073] Recommendation: If Developer B's performance continues to fall short of expectations, the system will prompt HR to review his or her promotion or dismissal to ensure continuous optimization of team performance.

[0074] This has the following benefits: 1) Improved efficiency and accuracy: By updating assessment information in real time, management can assign tasks more quickly and accurately, reducing the chance of mismatching.

[0075] 2) Improve employee satisfaction: When employees work in areas they are good at, their job satisfaction will be significantly improved, thereby improving overall team morale.

[0076] 3) Optimize talent management: Through systematic evaluation and recommendation, companies can more effectively identify and cultivate high-potential talents, while removing low-performing employees and promoting the improvement of the overall quality of the team.

[0077] Furthermore, the present invention provides a task assignment processing method based on an employee evaluation system, which can also realize intelligent analysis and real-time feedback functions. Specifically, with the help of artificial intelligence technology, the present invention can analyze employees' historical performance and provide more accurate predictions and suggestions, such as predicting employees' performance in future projects.

[0078] The present invention can also introduce a dynamic adjustment mechanism, such as re-evaluating all employees every quarter to ensure that the evaluation system keeps pace with market demands and technological changes.

[0079] Furthermore, the present invention provides a task assignment processing method based on an employee evaluation system, which can also generate personalized training suggestions for employees: for example, based on the evaluation results, it provides personalized training and career planning suggestions to help employees grow so as to better meet the company's future needs.

[0080] Furthermore, the task assignment processing method based on the employee evaluation system of the present invention can also realize the sentiment analysis function for employees. Specifically, it can combine the results of employee satisfaction surveys and use sentiment analysis technology to understand the subjective feelings of employees, so as to consider team atmosphere and cooperation potential when recommending tasks.

[0081] This not only improves the work efficiency of the entire team, but also enhances employees' sense of belonging and loyalty, forming a virtuous circle.

[0082] Exemplary devices like Figure 3 As shown, an embodiment of the present invention provides a task allocation processing device based on an employee evaluation system, the device comprising: The demand task acquisition module 310 is used to acquire the created demand task, wherein the demand task includes task content, task classification, task difficulty and task module parameters; An employee recommendation display module 320 is configured to recommend and display a list of employees whose comprehensive scores are higher than a first predetermined value and whose current busy / idle status is lower than a second predetermined value according to the required tasks; The employee task assignment module 330 is configured to receive an operation instruction, select a designated employee from a recommended employee list to complete the current required task, and obtain the selected task completion time; The task sending and reminder module 340 is used to send the required task and the task completion time to the task list of the designated employee and to remind the employee of the task completion time; The task completion and acceptance module 350 is used to obtain completion data of the required task when the required task is completed, and to accept the completion data of the required task; A modification module 360 is used to return the control to the modification when the acceptance result of the completion data of the required task is incorrect; The employee evaluation module 370 is used to obtain evaluation information of the designated employee who completed the task after the completion data of the required task is accepted. The evaluation information includes the task classification and task module that the employee is proficient in, as well as the corresponding comprehensive score; The evaluation update and task matching recommendation module 380 is used to update the employee evaluation system based on the evaluation information of the designated employee, and recommend a ranked list of employees who are good at the corresponding task categories and task modules in subsequent new required tasks, as well as give recommendations for employee promotion or dismissal.

[0083] The required task acquisition module is the system unit used to collect task creation information. It can be implemented using a form input interface or API data integration. Its function is to convert task parameters into structured data for subsequent processing. The employee recommendation display module is the algorithmic unit that screens candidate candidates based on preset rules. It can be implemented using a multi-dimensional scoring model combined with a real-time status monitoring mechanism. Its function is to eliminate inefficient personnel and prioritize employees who meet the required capabilities and availability. The employee task assignment module is the interactive unit that receives manual selection instructions. It can be implemented using a visual interface combined with a click event monitoring mechanism. Its function is to convert system recommendations into final task assignment decisions. The task delivery and reminder module is the functional unit for task dispatching and progress tracking. It can be implemented using a message queue push combined with calendar event synchronization technology. Its function is to ensure accurate transmission of task information and trigger timed reminders. The task completion and acceptance module is the automated process unit for outcome review. It can be implemented using a file comparison algorithm combined with a preset acceptance criteria library. Its function is to verify that task outcomes meet quality requirements. Among them, the modification module refers to the control unit that handles the rework process. Specifically, it can be implemented by state machine management combined with a version iteration recording mechanism. Its function is to track the modification process until the task is met. Among them, the employee evaluation module refers to the data processing unit for performance evaluation. Specifically, it can be implemented by a historical task analysis model combined with a weight calculation algorithm. Its function is to quantify employee ability characteristics and generate evaluation indicators. Among them, the evaluation update and task matching recommendation module refers to the decision-making unit for dynamically optimizing the recommendation strategy. Specifically, it can be implemented by real-time database updates combined with a classification and sorting algorithm. Its function is to optimize the subsequent task allocation logic based on the latest evaluation data.

[0084] Specifically, when the demand task acquisition module receives a new task that includes task classification, difficulty, and module parameters, the employee recommendation display module immediately filters out a list of employees whose comprehensive scores are higher than the threshold and whose current workload is within the limit. After the operator selects the personnel and sets the deadline through the employee task assignment module, the task sending and reminder module pushes the task information to the employee's task management system and starts the countdown reminder function at the same time. After receiving the completion information, the task completion and acceptance module automatically performs standard comparison and returns the acceptance result. If the acceptance fails, the modification module will trigger the rework process and re-enter the acceptance link; if the acceptance passes, the employee evaluation module will extract data such as the completion time and quality score of the task, and update the employee's areas of expertise and comprehensive score records. The evaluation update and task matching recommendation module adjusts the employee sorting rules for subsequent task assignments based on the updated data, and pushes job adjustment suggestions to managers.

[0085] Compared with existing technologies, existing task allocation systems rely on manual employee screening and lack a dynamic evaluation mechanism, resulting in low allocation efficiency and difficulty accurately matching employee capabilities. This device builds an automated recommendation chain, integrating task parameter analysis, employee status monitoring, achievement acceptance, and capability evaluation into a closed-loop system, enabling real-time, data-driven task allocation decisions.

[0086] Through the above technical solution, this application solves the problem of irrational task allocation caused by complex manual operations and delayed evaluation. The system's automatic screening mechanism shortens employee matching time, dynamically updated evaluation data improves the accuracy of matching tasks with areas of expertise, and closed-loop management of acceptance and rework reduces repeated communication costs, ultimately forming a continuously optimized task allocation system.

[0087] Based on the above embodiment, the present invention also provides an intelligent terminal, whose principle block diagram can be shown as follows: Figure 4 As shown. The intelligent terminal includes a processor, a memory, a network interface, a display screen, and a database connected via a system bus. The processor of the intelligent terminal is used to provide computing and control capabilities. The memory of the intelligent terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the intelligent terminal is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a task allocation processing method based on an employee evaluation system is implemented. The database of the intelligent terminal is used to store a task allocation processing program based on an employee evaluation system.

[0088] Those skilled in the art will understand that Figure 4The principle block diagram shown in the figure is only a block diagram of a partial structure related to the solution of the present invention and does not constitute a limitation on the smart terminal to which the solution of the present invention is applied. The specific smart terminal may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0089] In one embodiment, a smart terminal is provided, comprising a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors. The one or more programs include instructions for performing the following operations: Obtaining the created required tasks, wherein the required tasks include specific task content, task classification, task difficulty, and task module parameters; Based on the required tasks, a list of employees whose comprehensive scores are higher than a first predetermined value and whose current busy / idle status is lower than a second predetermined value is recommended and displayed; Receive operation instructions, select a designated employee from the recommended employee list to complete the current required task, and obtain the selected task completion time; Send the required task and the task completion time to the task list of the designated employee, and issue a corresponding task completion time reminder; When the required task is completed, the completion data of the required task is obtained and the completion data of the required task is inspected and accepted; If the acceptance result of the completion data of the required task is wrong, the control returns to the modification; When the completion data of the required task is accepted and confirmed to be correct, the evaluation information of the designated employee who completed the task is obtained; the evaluation information includes the task classification and task module that the employee is good at, as well as the corresponding comprehensive score; The employee evaluation system is updated based on the evaluation information of the designated employee, and a ranked list of employees corresponding to the task categories and task modules that they are good at is recommended in subsequent new required tasks, as well as employee promotion or dismissal recommendations.

[0090] Memory refers to the hardware component used to store program code and task data, specifically implemented as a solid-state drive or flash memory chip. Its function is to long-term preserve task assignment logic and employee evaluation data. Programs refer to computer executable code containing a set of instructions, specifically implemented using modular programming. Their function is to control the processor through preset algorithmic processes to perform operations such as task assignment, employee recommendation, acceptance feedback, and evaluation updates. Processors refer to the arithmetic unit that executes program instructions, specifically implemented using a multi-core central processing chip. Their function is to parse program code and coordinate the operating logic of each functional module to ensure the automated execution of the task assignment process.

[0091] Specifically, when the smart terminal is running, the processor calls the program from the memory and executes it. First, the program instructions are used to obtain the relevant parameters of the required task, such as task classification, difficulty, and module information. Then, based on the comprehensive score and busy / idle status in the employee evaluation system, a list of qualified employees is screened. After the operation instructions are received through the human-computer interaction interface, the task and completion time are pushed to the designated employee's task list, and the timed reminder function is triggered. After the task is completed, the program automatically receives and compares the acceptance data. If there are any errors, the modification process is triggered. After the acceptance is correct, the program further analyzes the employee's task completion indicators and updates the evaluation system, generating a list of recommended subsequent tasks and personnel suggestions.

[0092] In some specific implementations, the program may further integrate a natural language processing module, such as automatically parsing key parameters of task content through text recognition technology; or adopt a distributed computing architecture, such as sharding employee evaluation data and storing it on multiple server nodes to improve processing efficiency.

[0093] Compared to existing technologies, existing task allocation systems rely on manual operations or single functional modules, failing to dynamically link task recommendations, acceptance feedback, and employee evaluations. This solution, through collaborative control between programs and processors, integrates key steps in the task allocation process into an automated chain, reducing delays and errors associated with manual intervention.

[0094] Through the above technical solution, this application can achieve closed-loop management of task allocation and employee evaluation, optimize task matching accuracy by real-time updating of employee capability data, and shorten the task processing cycle based on an automated acceptance and feedback mechanism, thereby improving the overall efficiency of enterprise resource scheduling.

[0095] The present application further proposes a computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to execute a task allocation processing method based on an employee evaluation system, specifically including obtaining a created demand task, which includes specific task content, task classification, task difficulty and task module parameters; based on the demand task, recommending and displaying a list of employees whose comprehensive scores are higher than a first predetermined value and whose current busy / idle status is lower than a second predetermined value; receiving an operation instruction to select a designated employee from the recommended employee list to be responsible for completing the current demand task, and obtaining the selected task completion time; sending the demand task and the task completion time to the task list of the designated employee, and making a corresponding task completion time reminder; when the demand task is completed, obtaining the completion data of the demand task, and inspecting the completion data; when the inspection result is incorrect, the control returns to modify; when the inspection result is correct, obtaining evaluation information of the designated employee; updating the employee evaluation system based on the evaluation information, and recommending a ranked list of employees who are good at task classification and task modules in subsequent new demand tasks, and giving recommendations for employee promotion or dismissal.

[0096] Among them, computer-readable storage media refers to the physical carrier used to store program instructions, which can be implemented in a solid-state drive, mechanical hard drive or cloud storage server. Its function is to ensure that the electronic device can repeatedly call the process by persistently saving the execution logic of the task allocation processing method. The execution of instructions by the processor means that the computing unit is driven to run by compiled machine code. It can be implemented in a central processing unit or graphics processing unit. Its function is to trigger the electronic device to automatically perform tasks such as task allocation, employee recommendation, acceptance control and evaluation update. The employee evaluation system refers to a database used to record the performance of employees in completing tasks. It can be implemented in a relational database or a distributed storage system. Its function is to provide dynamically updated data support for task recommendation and employee management.

[0097] Specifically, after the instructions stored in the storage medium are loaded by the processor, it first controls the electronic device to obtain the basic parameters of the required task, and then calls the historical data in the employee evaluation system to filter out a list of candidate employees who meet the score and idle status. After the operator selects the employee through the interactive interface, the system automatically sends the task to the employee's task management module and starts the timer reminder function. After the task is completed, the electronic device receives the submitted document and performs an automated comparison and acceptance. If there is an error, the modification process is triggered. After it is correct, the task completion time, difficulty, classification and other data are extracted to generate employee evaluation information. The evaluation results are synchronized to the employee evaluation system to optimize the recommended ranking of subsequent task assignments and provide a decision-making basis for human resource management.

[0098] Compared with existing technologies, existing enterprises rely on manual task assignment or use single-function management systems, which are unable to achieve a closed-loop linkage between task assignment and employee evaluation. This solution uses computer-readable storage media to solidify the task processing process, allowing electronic devices to automatically complete the entire chain of operations, from task assignment to employee performance assessment. This not only eliminates the inefficiency of manual operations, but also continuously optimizes task matching accuracy through dynamically updated evaluation data.

[0099] Through the above technical solution, this application achieves standardization and automation of the task allocation process, significantly reducing the frequency of manual intervention. By executing instructions stored in a storage medium, electronic devices track the quality of task completion in real time and update employee capability profiles, making subsequent task recommendations more aligned with employees' actual work capabilities. This also provides traceable data support for corporate human resources decision-making, effectively improving organizational operational efficiency.

[0100] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A task assignment processing method based on an employee evaluation system, characterized in that: include: Obtaining the created required tasks, wherein the required tasks include specific task content, task classification, task difficulty, and task module parameters; Based on the required tasks, a list of employees whose comprehensive scores are higher than a first predetermined value and whose current busy / idle status is lower than a second predetermined value is recommended and displayed; Receive operation instructions, select a designated employee from the recommended employee list to complete the current required task, and obtain the selected task completion time; Send the required task and the task completion time to the task list of the designated employee, and issue a corresponding task completion time reminder; When the required task is completed, the completion data of the required task is obtained and the completion data of the required task is inspected and accepted; If the acceptance result of the completion data of the required task is wrong, the control returns to the modification; When the completion data of the required task is accepted and confirmed to be correct, the evaluation information of the designated employee who completed the task is obtained; the evaluation information includes the task classification and task module that the employee is good at, as well as the corresponding comprehensive score; The employee evaluation system is updated based on the evaluation information of the designated employee, and a ranked list of employees corresponding to the task categories and task modules that they are good at is recommended in subsequent new required tasks, as well as employee promotion or dismissal recommendations.

2. The task assignment processing method based on the employee evaluation system according to claim 1 is characterized in that: The step of recommending and displaying a list of employees whose comprehensive scores are higher than a first predetermined value and whose current busy / idle status is lower than a second predetermined value according to the required tasks includes: According to the required tasks, a list of employees who are good at the required tasks and their corresponding comprehensive scores are retrieved; Based on retrieving a list of employees who are good at corresponding to the required task, detecting the busy / idle status of the list of employees who are good at corresponding to the required task, a list of employees whose comprehensive scores are higher than a first predetermined value and whose current busy / idle status is lower than a second predetermined value is recommended for display.

3. The task assignment processing method based on the employee evaluation system according to claim 1 is characterized in that: When the required task is completed, the steps of obtaining the completion data of the required task and accepting the completion data of the required task include: When the required task is completed, receiving the completion data of the required task uploaded by the designated employee; Compare the completion data of the required tasks with the required task acceptance criteria one by one to accept the completion data of the required tasks one by one; and return the corresponding acceptance results.

4. The task assignment processing method based on the employee evaluation system according to claim 1 is characterized in that: When the acceptance result of the completion data of the required task is wrong, the step of controlling the return for modification includes: When the acceptance result of the completion data of the required task is incorrect, the control returns the modification information to the task list of the designated employee; Receive the modification data of the required task sent back by the designated employee, and continue to automatically accept the modification data.

5. The task assignment processing method based on the employee evaluation system according to claim 1 is characterized in that: After the completion data of the required task is accepted, the step of obtaining evaluation information of the designated employee who completed the task includes: Obtain the employee's acceptance results on the completion of the required tasks; Based on the employee's acceptance of the completion data of the required tasks, verify the employee's task classification, task score, task difficulty, task module, and task completion time of all completed tasks; Based on the task classification, task score, task difficulty, task module, and task completion time of all completed tasks by employees, the employee's performance is comprehensively evaluated to obtain employee evaluation information; the evaluation information includes the task classification and task module that the employee is good at, and the corresponding comprehensive score.

6. The task assignment processing method based on the employee evaluation system according to claim 1 is characterized in that: The steps of updating the employee evaluation system based on the evaluation information of the designated employee, recommending a ranked list of employees who are good at corresponding task categories and task modules in subsequent new required tasks, and providing employee promotion or dismissal recommendations include: Obtaining evaluation information of the designated employee; Updating the evaluation information of the designated employee to the employee evaluation system; The employee evaluation system is controlled to analyze the evaluation information of the designated employee, analyze the task classification and task modules that each employee is good at, and analyze whether the employee is competent for the current job, and generate corresponding analysis results.

7. The task assignment processing method based on the employee evaluation system according to claim 1 is characterized in that: The steps of updating the employee evaluation system based on the evaluation information of the designated employee, recommending a ranked list of employees who are good at corresponding task categories and task modules in subsequent new required tasks, and providing employee promotion or dismissal recommendations include: Based on the analyzed task categories and task modules that each employee is good at, when new tasks are subsequently received, a ranked list of employees with corresponding task categories and task modules will be recommended; And based on the analyzed information about whether the employees are competent for their current jobs, recommendations for employee promotion or dismissal are given.

8. A task assignment processing device based on an employee evaluation system, characterized in that: The device comprises: A demand task acquisition module is used to acquire the created demand task, wherein the demand task includes specific task content, task classification, task difficulty and task module parameters; An employee recommendation display module is used to recommend and display a list of employees whose comprehensive scores are higher than a first predetermined value and whose current busy / idle status is lower than a second predetermined value according to the required tasks; The employee task assignment module is used to receive operation instructions, select a designated employee from the recommended employee list to complete the current required task, and obtain the selected task completion time; The task sending and reminder module is used to send the required task and the task completion time to the task list of the designated employee and to make a corresponding task completion time reminder; The task completion and acceptance module is used to obtain the completion data of the required task when the required task is completed, and to accept the completion data of the required task; A modification module, used to control the return of modification when the acceptance result of the completion data of the required task is incorrect; An employee evaluation module is used to obtain evaluation information of a designated employee who has completed the task after the completion data of the required task has been accepted. The evaluation information includes the task classification and task module that the employee is good at, as well as the corresponding comprehensive score; The evaluation update and task matching recommendation module is used to update the employee evaluation system based on the evaluation information of the designated employee, and recommend a ranked list of employees who are good at the corresponding task categories and task modules in subsequent new required tasks, as well as provide recommendations for employee promotion or dismissal.

9. An intelligent terminal, characterized in that: The device comprises a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, and the one or more programs include being used to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 7.

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