A calculation model and method for supervisory expert performance evaluation scores

Abstract

The present invention relates to the technical field of computing models, and in particular to a computing model and method for computing scores of supervisory expert performance evaluation. The computing model comprises: an expert work behavior collection unit: used to collect different tasks received by different experts, the scoring levels of tasks, the time consumed to complete tasks, the degree of completion of tasks, and task consumption information; a scoring data storage unit: internally recording the ability levels of different experts in the team, the scoring data corresponding to tasks of different levels, the task completion scoring data, and the task violation scoring data; a scoring calculation unit: the scoring calculation unit has a built-in scoring algorithm, retrieves the corresponding integral data of the scoring data storage unit, and performs corresponding calculation and scoring according to the tasks received by the experts and the task completion status collected by the expert work behavior collection unit; this model can realize the corresponding recording of expert scores according to the expert execution task data.

Description

Technical Field

[0001] The present invention relates to the technical field of computing models, and in particular to a computing model and method for computing scores for supervisory expert performance evaluation. Background Art

[0002] ‌Expert performance evaluation‌ refers to the evaluation and judgment of the performance of experts in the process of performing their duties and completing tasks. This evaluation is usually based on the expert's job responsibilities within the organization, task completion, work efficiency, and work attitude. The core of performance evaluation lies in the evaluation of the performance of experts' duties, aiming to evaluate whether individuals or teams have achieved the expected work goals and standards. ‌For example, an employee may need to complete certain specific tasks in his position, such as project management, customer service, or technical support. These tasks are set based on their responsibilities and roles to ensure the smooth operation of the entire organization or enterprise. The importance of performance cannot be ignored. However, there is no corresponding supervision and scoring system in the existing technology to monitor and score the work behavior of experts, and it is impossible to effectively monitor the performance of experts and the performance of their duties. We cannot quickly judge whether the members of the team have fulfilled their duties as required, and give corresponding rewards to the expert members who have completed various tasks perfectly; the inability to effectively monitor the work of team members will affect the enthusiasm of the experts in the team.

[0003] Based on the above problems, there is an urgent need for a supervisory expert performance evaluation score calculation model that can monitor the performance of team members and ensure the enthusiasm of team experts. Summary of the invention

[0004] In view of the fact that there is currently no corresponding expert performance calculation model to monitor the workload and work behavior of experts, this application provides a supervision expert performance evaluation score calculation model and calculation method to solve the above problem.

[0005] To achieve the above object, the present invention is implemented through the following technical solutions: The present application embodiment discloses a supervisory expert performance evaluation score calculation model, including: Expert work behavior collection unit: used to collect different tasks received by different experts, task rating levels, task completion time, task completion degree and task consumption information; Scoring data storage unit: internally records the ability levels of different experts in the team, scoring data corresponding to tasks of different levels, task completion scoring data, and task violation scoring data; Scoring calculation unit: The scoring calculation unit has a built-in scoring algorithm, retrieves the corresponding score data from the scoring data storage unit, and calculates the corresponding scores based on the tasks received by the experts and the task completion status collected by the expert work behavior collection unit.

[0006] Adopting the above-mentioned technical scheme: the above-mentioned scheme is an automatic scoring system, which can collect various data on the expert's work behavior, and calculate and score the expert's work behavior according to the score data, task completion score data and task violation score data corresponding to different tasks stored in the scoring data storage unit, which can effectively supervise the expert's work situation and ensure the enthusiasm of the expert work within the team.

[0007] Preferably, the expert work behavior collection unit includes: Task record module: records the tasks received by each expert in a month, a quarter, and a year; Task time recording module: records the deadline for experts to receive and complete tasks, and calculates the time it takes for experts to complete tasks; Task rating level recording module: used to record the rating levels of tasks received by experts; Task completion degree recording module: After the experts complete the received tasks, the required workload and the actual workload are compared to obtain the final task completion degree; Task consumption information recording module: used to record the consumption of funds, manpower and equipment caused by experts completing tasks, and to record violations of experts in the course of performing their duties.

[0008] Adopting the above-mentioned technical solution: the expert work behavior collection unit provided by the above-mentioned solution can record the tasks performed by the experts, the time consumed by the tasks, the task scoring level, the degree of task completion, and the task consumption information, so that the scoring calculation unit can calculate the corresponding data and obtain the final expert performance score.

[0009] Preferably, the rating data storage unit comprises: Team expert competence level storage module: used to record the competence levels of different experts in the team and update the levels in a timely manner according to the experts' years of work and work status; Level task score storage module: stores the scoring data corresponding to tasks of different levels; Task completion score storage module: stores the scoring data corresponding to different task completion levels; Violation score evaluation storage module: stores the scoring data corresponding to different violation situations.

[0010] Adopting the above technical solution: the scoring data storage unit provided by the above solution is divided into various modules, and each module records scoring data for different expert capabilities, level tasks, task completion and violation scores.

[0011] Preferably, the score calculation unit includes: Receiving module: used to receive various data information of the expert's work collected by the expert work behavior collection unit and retrieve various calculated scores recorded in the score data storage unit; The first processing module is used to calculate the positive score according to the expert's ability level, task level, task completion, task consumption and time consumption; The second processing module is used to record the expert's negative violation scores according to the expert's violation behaviors at work, and receive the positive scores calculated by the first processing module to calculate the expert's total score; Output module: used to receive the total expert score calculated by the second processing module and output the corresponding expert score.

[0012] Adopt the above technical solution: the above solution provides a scoring calculation unit, which can receive various work data from experts, calculate positive scores and negative scores based on the various work data of experts, and output the corresponding expert scores.

[0013] Preferably, the built-in algorithm of the scoring calculation unit is: , in, Score points for expert tasks, is the number of tasks, is the task level coefficient, is the expert ability level coefficient, is the task completion coefficient, To complete the task, is the first scoring coefficient, The time allotted for completing the task, The actual time taken to complete the task. is the second scoring coefficient, For the The violation level coefficient of each behavior, For the The violation points coefficient for each behavior.

[0014] Adopt the above technical solution: The above solution has a built-in algorithm in the scoring calculation unit. Through the algorithm model, the expert's various data are calculated and transformed accordingly, and finally the expert's final score is calculated based on the team expert's actual level, task level, completion time and other data.

[0015] Preferably, the task level coefficient is greater than the expert ability level coefficient. The algorithm uses the difference between the task level coefficient and the expert ability level coefficient. The greater the difference between the expert ability level coefficient and the task level coefficient, the higher the calculated task score.

[0016] Adopt the above technical solution: The algorithm calculation method provided in the above solution can effectively avoid it, because the higher the expert's ability level is, the easier it is to complete higher-level tasks, and the higher the expert's final score will be. This design can comprehensively consider the expert's actual ability and the level of the task received.

[0017] A calculation method, applied to a supervisory expert performance evaluation score calculation model as described in any one of the above, comprising: S1: The expert work behavior collection unit collects different tasks received by different experts, the scoring levels of the tasks, the time consumed to complete the tasks, the degree of task completion, and the task consumption information; S2: The scoring calculation unit receives the required calculation data for the expert to complete the task collected by the expert work behavior collection unit and retrieves the corresponding scoring data from the scoring data storage unit; S3: The scoring calculation unit performs scoring calculation based on the data of the expert completing the task collected by the expert work behavior collection unit and the scoring data stored by the scoring data storage unit.

[0018] Adopt the above technical solution: The above technical solution provides a method that can collect various data on experts of different ability levels completing level tasks, and score and calculate the workload of the experts based on the collected data.

[0019] Preferably, the S1 comprises: S11: The task record module records the tasks received by each expert in a month, a quarter, and a year; S12: Record the deadline for the expert to receive the task and complete the task, and calculate the time consumed by the expert to complete the task; S13: Recording the ratings of the tasks received by the experts; S14: After the expert completes the received task, a corresponding comparison is made between the required workload and the actual workload to obtain the final task completion degree; S15: Record the expenditure of funds, manpower and equipment incurred by experts in completing their tasks, and record any violations committed by experts in the course of performing their duties.

[0020] Adopt the above technical solution: the above solution provides a task recording module, which can record the task completion data of each expert.

[0021] Preferably, S3 includes: S31: The receiving module receives the expert's work data information collected by the expert work behavior collection unit and retrieves the various calculated scores recorded in the score data storage unit; S32: The first processing module calculates the positive score according to the expert's ability level, task level, task completion, and task consumption and time consumption. S33: The second processing module records the expert's violation negative score according to the expert's violation behavior at work, receives the positive score calculated by the first processing module, and calculates the expert's total score; S34: Receive the total expert score calculated by the second processing module, and output the corresponding expert score.

[0022] Adopting the above technical solution: the above solution can calculate the scores of experts completing tasks through the algorithm models built into the first processing module and the second processing module. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A connection diagram of a supervisory expert's performance evaluation score calculation model for this application; Figure 2 Flow chart of the calculation method for this application; Figure 3 For this application Figure 2 Detailed flow chart of step S1 in FIG. Figure 4 For this application Figure 2 Detailed flow chart of step S3 in FIG.

[0025] In the figure: 1. Expert work behavior collection unit; 2. Scoring data storage unit; 3. Scoring calculation unit; 4. Task recording module; 5. Task consumption time recording module; 6. Task scoring level recording module; 7. Task completion degree recording module; 8. Task consumption information recording module; 9. Team expert ability level storage module; 10. Level task score storage module; 11. Task completion score storage module; 12. Violation score judgment storage module; 13. Receiving module; 14. First processing module; 15. Second processing module; 16. Output module. DETAILED DESCRIPTION

[0026] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0027] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0028] See also Figure 1-Figure 4 For example, the existing behavior supervision of experts completing tasks does not have a corresponding calculation model, and it is impossible to record the corresponding scores according to the completion of tasks by experts of different ability levels, which makes it difficult to mobilize the enthusiasm of experts to work. Based on the above problems, the embodiment of the present application discloses a scoring calculation model for supervising the performance evaluation of experts, including: Expert work behavior collection unit 1: used to collect different tasks received by different experts, task ratings, task completion time, task completion degree and task consumption information; in the process of experts completing tasks, various task information will be recorded through the corresponding electronic system, and the corresponding supervisors will record the information of the experts completing tasks, such as task completion time, task consumption, including manpower, material consumption, etc.

[0029] Scoring data storage unit 2: internally records the ability levels of different experts in the team, scoring data corresponding to tasks of different levels, task completion scoring data, and task violation scoring data; Scoring calculation unit 3: Scoring calculation unit 3 has a built-in scoring algorithm, retrieves the corresponding score data from the scoring data storage unit 2, and calculates the corresponding scores based on the tasks received by the experts and the task completion status collected by the expert work behavior collection unit 1.

[0030] It is worth mentioning that the above-mentioned scheme is an automatic scoring system, which can collect various data on the expert's work behavior, and calculate and score the expert's work behavior according to the score data, task completion score data and task violation score data corresponding to different tasks stored in the scoring data storage unit 2, which can effectively supervise the expert's work situation and ensure the enthusiasm of the experts in the team.

[0031] The expert work behavior collection unit 1 includes: Task Record Module 4: Record the tasks received by each expert in a month, a quarter, and a year; Task consumption time recording module 5: records the deadline for experts to receive tasks and complete tasks, and calculates the time consumed by experts to complete tasks; Task scoring level recording module 6: used to record the scoring level of the tasks received by the experts; Task completion degree recording module 7: After the expert completes the received task, the required workload and the actual workload are compared to obtain the final task completion degree; Task consumption information recording module 8: used to record the consumption of funds, manpower and equipment caused by experts completing tasks, and record the violations of experts in the process of performing their duties. The expert work behavior collection unit 1 provided by the above scheme can record the tasks performed by experts, task consumption time, task scoring level, task completion degree, and task consumption information, so that the scoring calculation unit 3 can calculate the corresponding data and obtain the final expert performance score.

[0032] The scoring data storage unit 2 includes: Team expert competence level storage module 9: used to record the competence levels of different experts in the team and update the levels in a timely manner according to the experts' years of work and work status; Level task score storage module 10: stores the scoring data corresponding to tasks of different levels; Task completion score storage module 11: stores scoring data corresponding to different task completion degrees; The violation score evaluation storage module 12 stores the scoring data corresponding to different violation situations.

[0033] The scoring data storage unit 2 provided by the above solution is divided into various modules, and each module records scoring data for different expert capabilities, level tasks, task completion and violation scores.

[0034] The score calculation unit 3 includes: Receiving module 13: used to receive various data information of the expert's work collected by the expert work behavior collection unit 1 and retrieve various calculated scores recorded in the score data storage unit 2; The first processing module 14 is used to calculate the positive score according to the expert's ability level, task level, task completion, task consumption and time consumption; The second processing module 15 is used to record the expert's violation negative score according to the expert's violation behavior at work, and receive the positive score calculated by the first processing module 14 to calculate the expert's total score; Output module 16: used to receive the total expert score calculated by the second processing module 15, and output the corresponding expert score. The above scheme provides a scoring calculation unit 3, which can receive various work data from experts, calculate positive scores and negative scores based on the various work data of experts, and output the corresponding expert scores.

[0035] Preferably, the built-in algorithm of the score calculation unit 3 is: , in, Score points for expert tasks, is the number of tasks, is the task level coefficient, is the expert ability level coefficient, is the task completion coefficient, To complete the task, is the first scoring coefficient, The time allotted for completing the task, The actual time spent completing the task, is the second scoring coefficient, For the The violation level coefficient of each behavior, For the The violation points coefficient for each behavior.

[0036] The above scheme provides an algorithm model, which takes the difference between the task level coefficient and the expert ability level as an evaluation data, and multiplies it with the task completion coefficient. The higher the task completion coefficient, the higher the final score, and the consumption of completing the task is used as the denominator. In the process of completing the task, the greater the consumption, the lower the task score will be. The factors considered above are multiplied by the first scoring coefficient to obtain the first score record. The time required to complete the task is subtracted from the time actually consumed to complete the task. When the task is completed ahead of schedule, the final calculated result is a positive value, which is multiplied by the second scoring coefficient to obtain a positive score. The actual time consumed to complete the task is greater than the time required for the task, and the final calculated result is a negative value. It is multiplied by the second scoring coefficient to obtain a negative score. The above score on completion time is recorded as the second score. In the process of experts completing their work, corresponding violations may occur. For violations committed by experts in the process of completing their work, the corresponding violation score can be calculated based on the expert's violation level and the violation score coefficient. The above-mentioned violation deduction is recorded as the third score.

[0037] The task level coefficient is greater than the expert ability level coefficient. The algorithm uses the difference between the task level coefficient and the expert ability level coefficient. The greater the difference between the expert ability level coefficient and the task level coefficient, the higher the calculated task score. The algorithm calculation method provided in the above scheme can effectively avoid this problem, because the higher the expert's ability level, the easier it is to complete higher-level tasks, and the higher the final expert score. This design can comprehensively consider and score based on the actual ability of the expert and the level of the received task.

[0038] A calculation method, applied to a supervisory expert performance evaluation score calculation model as described in any one of the above, comprising: S1: The expert work behavior collection unit 1 collects different tasks received by different experts, the scoring levels of the tasks, the time consumed to complete the tasks, the degree of task completion, and the task consumption information; S2: The scoring calculation unit 3 receives the required calculation data for the expert to complete the task collected by the expert work behavior collection unit 1 and retrieves the corresponding scoring data from the scoring data storage unit 2; S3: The scoring calculation unit 3 performs scoring calculation according to the data of the expert completing the task collected by the expert work behavior collection unit 1 and the scoring data stored in the scoring data storage unit 2.

[0039] The above technical solution provides a method that can collect various data on experts of different ability levels completing level tasks, and score and calculate the workload of the experts based on the collected data.

[0040] The S1 includes: S11: The task recording module 4 records the tasks received by each expert in a month, a quarter, and a year; S12: Record the deadline for the expert to receive the task and complete the task, and calculate the time consumed by the expert to complete the task; S13: Recording the ratings of the tasks received by the experts; S14: After the expert completes the received task, a corresponding comparison is made between the required workload and the actual workload to obtain the final task completion degree; S15: Record the expenditure of funds, manpower and equipment incurred by experts in completing their tasks, and record any violations committed by experts in the course of performing their duties.

[0041] The above solution provides a task recording module 4, which can record the task completion data of each expert.

[0042] The S3 includes: S31: The receiving module 13 receives the expert's work data information collected by the expert work behavior collection unit 1 and retrieves the various calculated scores recorded in the score data storage unit 2; S32: The first processing module 14 calculates the positive score according to the expert's ability level, task level, task completion, and task consumption and time consumption. S33: The second processing module 15 records the expert's violation negative score according to the expert's violation behavior at work, and receives the positive score calculated by the first processing module 14 to calculate the expert's total score; S34: receiving the total expert score calculated by the second processing module 15, and outputting the corresponding expert score.

[0043] The above solution can calculate the scores of experts completing tasks through the algorithm models built into the first processing module 14 and the second processing module 15 .

[0044] Unless otherwise specified, the device components involved in the above embodiments are all conventional device components, and the connection methods and control methods involved are all conventional connection methods and control methods unless otherwise specified.

[0045] The present invention has been described in detail above in conjunction with the embodiments. However, those skilled in the art will appreciate that, without departing from the spirit of the present invention, the specific parameters in the above embodiments may be changed to form a plurality of specific embodiments, which are all within the common variation range of the present invention and will not be described in detail herein.

Claims

1. A scoring calculation model for supervisory expert performance evaluation, characterized in that: include: Expert work behavior collection unit: used to collect different tasks received by different experts, task rating levels, task completion time, task completion degree and task consumption information; Scoring data storage unit: internally records the ability levels of different experts in the team, scoring data corresponding to tasks of different levels, task completion scoring data, and task violation scoring data; Scoring calculation unit: The scoring calculation unit has a built-in scoring algorithm, retrieves the corresponding score data from the scoring data storage unit, and calculates the corresponding scores based on the tasks received by the experts and the task completion status collected by the expert work behavior collection unit.

2. According to the calculation model of the supervisory expert performance evaluation score according to claim 1, it is characterized in that: The expert work behavior collection unit includes: Task record module: records the tasks received by each expert in a month, a quarter, and a year; Task time recording module: records the deadline for experts to receive and complete tasks, and calculates the time it takes for experts to complete tasks; Task rating level recording module: used to record the rating levels of tasks received by experts; Task completion degree recording module: After the experts complete the received tasks, the required workload and the actual workload are compared to obtain the final task completion degree; Task consumption information recording module: used to record the consumption of funds, manpower and equipment caused by experts completing tasks, and to record violations of experts in the course of performing their duties.

3. According to the calculation model of the supervisory expert performance evaluation score according to claim 1, it is characterized in that: The scoring data storage unit comprises: Team expert competence level storage module: used to record the competence levels of different experts in the team and update the levels in a timely manner according to the experts' years of work and work status; Level task score storage module: stores the scoring data corresponding to tasks of different levels; Task completion score storage module: stores the scoring data corresponding to different task completion levels; Violation score evaluation storage module: stores the scoring data corresponding to different violation situations.

4. According to claim 1, a supervisory expert performance evaluation score calculation model is characterized in that: The scoring calculation unit comprises: Receiving module: used to receive various data information of the expert's work collected by the expert work behavior collection unit and retrieve various calculated scores recorded in the score data storage unit; The first processing module is used to calculate the positive score according to the expert's ability level, task level, task completion, task consumption and time consumption; The second processing module is used to record the expert's violation negative score according to the expert's violation behavior at work, and receive the positive score calculated by the first processing module to calculate the expert's total score; Output module: used to receive the total expert score calculated by the second processing module and output the corresponding expert score.

5. According to claim 1, a supervisory expert performance evaluation score calculation model is characterized in that: The built-in algorithm of the scoring calculation unit is: ， in, Score points for expert tasks, is the number of tasks, is the task level coefficient, is the expert ability level coefficient, is the task completion coefficient, To complete the task, is the first scoring coefficient, The time allotted for completing the task, The actual time spent completing the task, is the second scoring coefficient, For the The violation level coefficient of each behavior, For the The violation points coefficient for each behavior.

6. A supervisory expert performance evaluation score calculation model according to claim 5, characterized in that: The task level coefficient is greater than the expert ability level coefficient. The algorithm uses the difference between the task level coefficient and the expert ability level coefficient. The greater the difference between the expert ability level coefficient and the task level coefficient, the higher the calculated task score.

7. A calculation method, applied to a supervisory expert performance evaluation score calculation model as described in any one of claims 1 to 6, characterized in that: include: S1: The expert work behavior collection unit collects different tasks received by different experts, the scoring levels of the tasks, the time consumed to complete the tasks, the degree of task completion, and the task consumption information; S2: The scoring calculation unit receives the required calculation data for the expert to complete the task collected by the expert work behavior collection unit and retrieves the corresponding scoring data from the scoring data storage unit; S3: The scoring calculation unit performs scoring calculation based on the data of the expert completing the task collected by the expert work behavior collection unit and the scoring data stored by the scoring data storage unit.

8. A calculation method according to claim 1, characterized in that: The S1 includes: S11: The task record module records the tasks received by each expert in a month, a quarter, and a year; S12: Record the deadline for the expert to receive the task and complete the task, and calculate the time consumed by the expert to complete the task; S13: Recording the ratings of the tasks received by the experts; S14: After the expert completes the received task, a corresponding comparison is made between the required workload and the actual workload to obtain the final task completion degree; S15: Record the expenditure of funds, manpower and equipment incurred by experts in completing their tasks, and record any violations committed by experts in the course of performing their duties.

9. A calculation method according to claim 1, characterized in that: The S3 includes: S31: The receiving module receives the expert's work data information collected by the expert work behavior collection unit and retrieves the various calculated scores recorded in the score data storage unit; S32: The first processing module calculates a positive score according to the expert's ability level, task level, task completion, and task consumption and time consumption; S33: The second processing module records the expert's violation negative score according to the expert's violation behavior at work, receives the positive score calculated by the first processing module, and calculates the expert's total score; S34: Receive the total expert score calculated by the second processing module, and output the corresponding expert score.

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