Performance assessment data management platform

Abstract

The invention relates to the technical field of data management, and discloses a performance assessment data management platform. Comprising the steps of dividing assessment personnel into subsets, screening out assessment data, extracting assessment arrangement parameters, calculating an assessment priority value, calculating performance assessment indexes of the assessment personnel, identifying personnel to be rechecked, collecting second-order parameters of the personnel to be rechecked, calculating second-order rechecking values of the personnel to be rechecked, and checking the performance of the personnel to be rechecked according to the second-order parameters of the personnel to be rechecked. The performance level of the assessment personnel is determined; compared with the prior art, the performance assessment sequence of the assessment personnel can be orderly and accurately limited, interference and influence phenomena possibly occurring during synchronous calculation of multiple pieces of similar assessment data are prevented, two levels of performance assessment calculation processing operation are combined, and the performance assessment accuracy is improved. The problem of assessment data dimension limitation during performance assessment is effectively avoided, the purpose of multi-level accurate calculation of performance assessment is achieved, and the accuracy of a performance assessment result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and more specifically, to a performance appraisal data management platform. Background Art

[0002] Performance appraisal can comprehensively evaluate the daily work status and work ability of staff members, and the final result of performance appraisal is directly related to the efficiency and quality of the staff members' work. Therefore, accurate performance appraisal of staff members is necessary.

[0003] The patent application with reference publication number CN111178853A discloses a performance appraisal system, method, storage medium and electronic terminal for employees, including a storage module for storing basic information of employees and corresponding appraisal templates, the appraisal templates including performance appraisal specifications and performance appraisal standards, the performance appraisal specifications including a number of performance appraisal items, the performance appraisal standards indicating the possible score range of each performance appraisal item and the weight of the performance appraisal item, a performance appraisal item score calculation module for obtaining a performance appraisal item score according to the performance appraisal item and the performance appraisal standard, and a performance calculation module for obtaining a corresponding performance appraisal total score according to the appraisal template and the performance appraisal item score; When conducting performance appraisal, existing appraisers obtain relevant subjective and objective appraisal scores of the appraisers, summarize and calculate the various appraisal scores, and then express the performance appraisal results through the final score. For example, in the above-mentioned patent application, the performance appraisal item scores are obtained according to the performance appraisal items and performance appraisal standards, and the corresponding performance appraisal total scores are obtained according to the appraisal template and the performance appraisal item scores to achieve the purpose of performance appraisal. Although this method can achieve the effect of performance appraisal, it is unable to arrange the large amount of data involved in the appraisal calculation in an orderly manner, and when a single-level appraisal calculation method is used to synchronously calculate multiple similar appraisal data, the probability of calculation errors in the performance appraisal calculation will increase, thereby resulting in low accuracy of the performance appraisal calculation results.

[0004] In view of this, the present invention proposes a performance appraisal data management platform to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a performance appraisal data management platform, applied to an appraisal server, comprising: The data screening module is used to divide the assessors into subsets according to their assessment attributes and screen the assessment data from the database; The assessment ranking module is used to extract assessment ranking parameters from the assessment data. The assessment ranking parameters include effective input time, data completeness rate and effective quantity value, and calculate the assessment priority value of the assessor; The assessment calculation module is used to extract performance parameters from the assessment data, calculate the performance assessment index of the assessee, and identify the assessees to be reviewed from among the assessees; The review calculation module is used to collect the second-order parameters of the personnel to be reviewed during the assessment period. The second-order parameters include the effective adoption rate and the decision execution degree, and calculate the second-order review value of the personnel to be reviewed; The assessment rating module is used to determine the performance level of the assessee and send the performance assessment results to the management platform.

[0006] Further, the subset includes a first subset, a second subset, and a third subset; The division method of the first subset, the second subset and the third subset is: The identity information of each assessor is retrieved one by one through the personnel management system, and the agency level and assessor name of the assessor are marked in the identity information; The level fields in the A levels of the agencies are identified one by one through natural language processing technology, and the character parts in the level fields are recorded as the level status; The assessment attributes corresponding to the level fields with level statuses of "A-0", "A-1" and "A-2" are recorded as basic-level attributes, middle-level attributes and high-level attributes respectively; The assessors whose assessment attributes are grassroots attributes, middle-level attributes and high-level attributes are respectively summarized to generate a first subset, a second subset and a third subset.

[0007] Furthermore, the screening method of assessment data is: The level status of all comprehensive data in the database is queried one by one, and the comprehensive data with the level status of low level, medium level and high level are respectively summarized to generate the first data layer, the second data layer and the third data layer; Mark the names of the personnel in the comprehensive data in the first data layer, the second data layer, and the third data layer one by one, and compare the names of the personnel in the comprehensive data with the assessment names of the assessors; Record the comprehensive data whose names of personnel in the first data layer are consistent with the assessment names of the assessors in the first subset as assessment data; Record the comprehensive data whose personnel names in the second data layer are consistent with the assessment names of the assessors in the second subset as assessment data; The comprehensive data whose personnel names in the third data layer are consistent with the assessment names of the assessors in the third subset is recorded as assessment data.

[0008] Furthermore, the method for extracting the effective input duration, data completeness rate and effective quantity value is as follows: Match A assessors with corresponding assessment data one by one to generate A data packets; The entry time of the first assessment data and the last assessment data in A data packets are queried one by one through the timestamps, and recorded as the entry start time and the entry end time respectively; The time between the recording start time and the recording end time of A data packets is recorded as the effective recording time, and A effective recording time is obtained; Query the filling status of all the assessment data in the A data packets one by one, and record the assessment data with a filling status of complete filling as complete data; The number of complete data and the total number of test data in A data packets are counted respectively, and the number of complete data is compared with the total number of test data to obtain the data completeness rate; The expression of data integrity rate is: ; In the formula, For the The data integrity rate of the data packets, =1,2,...,A, For the The number of complete data in a packet, For the The total number of assessment data in a data package; Perform security checks on all the complete data in the A data packets one by one, and count the number of complete data that pass the security check to obtain A valid number values.

[0009] Furthermore, the calculation method of the assessment priority value is: The effective input duration, data integrity rate and effective quantity of A data packets are assigned corresponding weight coefficients and compared to obtain A assessment priority values; The expression of the assessment priority value is: ; In the formula, For the The assessment priority value of each assessor. No. The effective recording time of a data packet, No. The effective number of packets. , , All of them are weight coefficients greater than 0.

[0010] Furthermore, the performance parameters include work ability score, work attitude score and work progress score; The extraction method of work ability score, work attitude score and work progress score is as follows: Identify the assessment semantics of the assessment data through natural language processing technology, and split the assessment semantics into assessment text and assessment numbers; The assessment numbers in the assessment data with the assessment words of ability, attitude and progress are recorded as work ability score, work attitude score and work progress score respectively; According to the assessment priority value from large to small, the assessment texts in the assessment data of A assessors are identified one by one to obtain A work ability scores, A work attitude scores and A work progress scores.

[0011] Furthermore, the calculation method of the performance appraisal index is: The work ability scores, work attitude scores and work progress scores of A assessors are assigned corresponding weight coefficients and added together to obtain A performance assessment index; The identification method for persons to be reviewed is: Compare the performance appraisal indexes of A appraisers one by one with the preset first performance appraisal threshold; When the performance appraisal index of the appraiser is greater than or equal to the preset first performance appraisal threshold, the appraiser is recorded as a person to be reviewed, and B persons to be reviewed are obtained; When the performance appraisal index of the appraiser is less than the preset first performance appraisal threshold, the appraiser will not be recorded as a person to be reviewed.

[0012] Furthermore, the assessment period is the period between the time when the first assessment data is entered and the time when the last assessment data is entered for all personnel to be reviewed; The collection method of effective adoption rate is: During the assessment period, the total number of suggestions put forward by the B persons to be reviewed is queried one by one through the database, and the total value of B suggestions is obtained; The suggestions adopted and applied are recorded as valid suggestions, and the number of valid suggestions from B persons to be reviewed is counted to obtain B valid values; Compare the B effective values ​​with the corresponding B total recommended values ​​one by one to obtain B effective adoption rates; The expression of effective adoption rate is: ; In the formula, For the The effective adoption rate of the personnel to be reviewed, =1,2,...,B, For the The effective value of the person to be reviewed, For the The total recommended value for persons to be reviewed.

[0013] Furthermore, the method for collecting decision execution degree is: During the assessment period, the real-time progress of all the execution decisions received by the B reviewers for the first time is queried one by one, and recorded as the initial progress; At the last moment of the assessment cycle, the real-time progress of all the execution decisions of the B personnel to be reviewed is queried one by one and recorded as the current progress; The progress difference between the current progress and the initial progress is recorded as the execution progress, and the execution progress of all the execution decisions of the B persons to be reviewed is accumulated and averaged to obtain the B decision execution degrees; The expression of the second-order review value is: ; In the formula, For the The second-order review value of the personnel to be reviewed, For the The degree of decision execution of the personnel to be reviewed, , All of them are weight coefficients greater than 0.

[0014] Further, performance levels include excellent, good, qualified, and unsatisfactory; The methods for determining excellent, good, qualified and unqualified are: Compare the second-order review values ​​of the B persons to be reviewed with the preset second-order review thresholds one by one; When the second-order review value of the person to be reviewed is greater than or equal to the preset second-order review threshold, the performance level of the person to be reviewed is recorded as excellent; When the second-order review value of the person to be reviewed is less than the preset second-order review threshold, the performance level of the person to be reviewed is recorded as good; Remove B persons to be reviewed, and compare the performance appraisal indexes of the remaining appraisers one by one with the preset second performance appraisal threshold; When the performance appraisal index of the appraiser is greater than or equal to the preset second performance appraisal threshold, the performance level of the appraiser is recorded as qualified; When the performance appraisal index of the appraiser is less than the preset second performance appraisal threshold, the performance level of the appraiser is recorded as unqualified.

[0015] Technical effects and advantages of a performance appraisal data management platform of the present invention: (1): By dividing the assessors into subsets and filtering the assessment data from the database, it is possible to identify and divide assessors of different types and levels in an orderly and accurate manner, thereby using the assessment attributes as the basis for data screening and quickly and accurately filtering out the assessment data that matches the assessors from the database, thereby improving the accuracy of assessment data screening.

[0016] (2): By extracting the assessment ranking parameters from the assessment data and calculating the assessment priority value of the assessor, the assessment priority value can be used as the standard for the order of performance assessment calculation for each assessor. The performance assessment order of the assessors can be limited in an orderly manner, so that each assessor can maintain an independent performance assessment status, preventing interference and influence that may occur when multiple similar assessment data are calculated synchronously, thereby achieving an orderly performance assessment calculation effect.

[0017] (3): By extracting performance parameters from the assessment data, calculating the performance assessment index of the assessee, and identifying the assessees to be reviewed from the assessees, a low-level performance assessment can be conducted on the assessees, achieving a one-time performance assessment effect for the assessees, and providing an accurate basis for the identification of the assessees to be reviewed.

[0018] (4): By collecting the second-order parameters of the personnel to be reviewed during the assessment period and calculating the second-order review values ​​of the personnel to be reviewed, a deeper review calculation operation can be performed on the performance assessment of the personnel to be reviewed, thus achieving the effect of secondary performance assessment of the assessors. This can avoid the inaccurate calculation phenomenon existing in the traditional one-time performance assessment, and can also avoid the problem of the limitation of the assessment data dimension in the one-time performance assessment, thus greatly improving the accuracy of the performance assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the architecture of a performance appraisal data management platform provided in the first embodiment of the present invention; Figure 2 A module diagram of the assessment server provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Example 1: Please refer to Figure 1 and Figure 2As shown, a performance appraisal data management platform described in this embodiment is applied to an appraisal server and includes: The data screening module divides the assessors into subsets according to their assessment attributes and screens out the assessment data corresponding to the assessors from the database; Appraisers refer to the personnel who participate in this performance appraisal. Appraisal attributes are used to specifically indicate the types of different appraisers in this performance appraisal, so as to serve as the basis for identifying different types of appraisers and accurately distinguishing different types of appraisers. The appraisal attributes include grassroots attributes, middle-level attributes and high-level attributes; grassroots attributes refer to the appraised personnel being grassroots personnel, and their performance appraisal data remain at the basic level; middle-level attributes refer to the appraised personnel being middle-level personnel, and their performance appraisal data remain at the middle level; high-level attributes refer to the appraised personnel being high-level personnel, and their performance appraisal data remain at the high level; After identifying the assessment attributes of the assessors, it is necessary to divide the different types of assessors based on the assessment attributes, so that assessors with the same assessment attributes can be in the same set and form sub-sets; The sub-sets include a first sub-set, a second sub-set and a third sub-set; the first sub-set is used to summarize the assessors whose assessment attributes are grassroots attributes, the second sub-set is used to summarize the assessors whose assessment attributes are middle-level attributes, and the third sub-set is used to summarize the assessors whose assessment attributes are high-level attributes; The method of dividing the subsets is: The identity information of each assessor is retrieved one by one through the personnel management system, and the agency level and assessor name of the assessor are marked in the identity information; The level fields in the A agency levels are identified one by one through natural language processing technology, and the character part in the level field is recorded as the level status; the level field is used to concisely represent the specific content information of the agency level, and the character part is the character in the level field that can directly represent the accurate data of the agency level and serves as the basis for identifying different assessment attributes; The level field with the level status of "A-0" is recorded as the basic field, and the assessment attribute of the assessor corresponding to the basic field is recorded as the basic attribute; The level field with the level status of "A-1" is recorded as the middle-level field, and the assessment attribute of the assessor corresponding to the middle-level field is recorded as the middle-level attribute; The level field with the level status of "A-2" is recorded as a high-level field, and the assessment attribute of the assessor corresponding to the high-level field is recorded as a high-level attribute; The assessors whose assessment attributes are grassroots attributes, middle-level attributes and high-level attributes are respectively summarized to generate a first subset, a second subset and a third subset.

[0022] After the assessors are divided into three different subsets, it is necessary to filter out the assessment data corresponding to the assessors in each subset from the database so that the assessment data can be used as the overall data for judging the performance assessment results of the assessors; The screening method for assessment data is: The level status of all comprehensive data in the database is queried one by one, and the comprehensive data with the level status of low level, medium level and high level are summarized respectively to generate the first data layer, the second data layer and the third data layer; the level status is used to indicate the level of the comprehensive data in the database, and serves as the basis for matching with assessors of different assessment attributes. Specifically, the level status includes low level, medium level and high level, and the low level corresponds to the assessors of grassroots attributes, the medium level corresponds to the assessors of middle-level attributes, and the high level corresponds to the assessors of high-level attributes; Mark the names of the personnel in the comprehensive data in the first data layer, the second data layer, and the third data layer one by one, and compare the names of the personnel in the comprehensive data with the assessment names of the assessors; Record the comprehensive data whose names of personnel in the first data layer are consistent with the assessment names of the assessors in the first subset as assessment data; Record the comprehensive data whose personnel names in the second data layer are consistent with the assessment names of the assessors in the second subset as assessment data; The comprehensive data whose personnel names in the third data layer are consistent with the assessment names of the assessors in the third subset is recorded as assessment data.

[0023] It should be noted that, although the order and method of extracting and identifying the assessment data of different types of assessors are different, the specific meanings represented by the assessment data finally screened out are of the same type, that is, they are used to meet the calculation and management needs of the performance assessment of the assessors.

[0024] The assessment ranking module extracts the assessment ranking parameters from the assessment data, calculates the assessment priority values ​​of the assessors, and ranks the assessors; Appraisal ranking parameters refer to specific parameters that can affect the calculation order of relevant numerical values ​​of the performance appraisal of the appraiser, and serve as the data basis for calculating the appraisal priority value. At the same time, they can also indicate the importance of the appraiser in the performance appraisal calculation; The assessment ranking parameters include effective input time, data completeness rate and effective quantity value; among them, effective input time is used to express the overall time for collecting and entering the assessment data of the assessor into the database, data completeness rate is used to express the numerical value of the collection completeness of the assessment data of the assessor, and effective quantity value is used to express the quantity of the assessment data of the assessor that can be effectively used; The method for extracting effective input duration, data completeness rate and effective quantity values ​​is as follows: Match A assessors with corresponding assessment data one by one to generate A data packets; The entry time of the first assessment data and the last assessment data in A data packets are queried one by one through the timestamps, and recorded as the entry start time and the entry end time respectively; The time between the recording start time and the recording end time of A data packets is recorded as the effective recording time, and A effective recording time is obtained; The filling status of all the assessment data in A data packets is queried one by one, and the assessment data with the filling status of complete filling is recorded as complete data; the filling status is used to indicate whether the assessment data in the data packet is completely filled. Specifically, the filling status includes complete filling and defective filling. Complete filling means that the assessment data in the data packet is completely filled, and defective filling means that the assessment data in the data packet is not completely filled; The number of complete data and the total number of test data in A data packets are counted respectively, and the number of complete data is compared with the total number of test data to obtain the data completeness rate; The expression of data integrity rate is: ; In the formula, For the The data integrity rate of the data packets, =1,2,...,A, For the The number of complete data in a packet, For the The total number of assessment data in a data package; All the complete data in A data packets are security checked one by one, and the number of complete data that pass the security check is counted to obtain A valid number of values. Security check refers to the operation of correct format and data security of complete data, which is implemented through the security check system to achieve effective detection effect on each complete data.

[0025] After obtaining the assessment ranking parameters of the assessment data, the assessment priority value of the assessor can be calculated according to the obtained effective input time, data completeness rate and effective quantity value, so that the assessment priority value can be used as the basis for calculating the order of performance assessment of the assessor; The calculation method of assessment priority value is: The effective input duration, data integrity rate and effective quantity of A data packets are assigned corresponding weight coefficients and compared to obtain A assessment priority values; The expression of the assessment priority value is: ; In the formula, For the The assessment priority value of each assessor. No. The effective recording time of a data packet, No. The effective number of packets. , , All of them are weight coefficients greater than 0.

[0026] in, , , , It is used to balance the proportion of effective input time, data completeness rate and effective quantity value in the assessment priority value, so as to adjust the influence of effective input time, data completeness rate and effective quantity value on the assessment priority value.

[0027] After obtaining the assessment priority value of each assessor, the performance assessment order of the assessors can be arranged according to the size of the assessment priority value. Specifically, when the assessment priority value is larger, it means that the assessment data of the assessor has a higher assessment calculation value. At this time, the higher the importance of the assessor in the performance assessment, the higher the ranking of the assessor in the assessment ranking, and vice versa. In this way, the performance assessment calculation order of A assessors can be arranged in order, and the phenomenon of disorderly performance assessment order can be avoided.

[0028] The assessment calculation module extracts the performance parameters of the assessors from the assessment data, calculates the performance assessment index of the assessors, and identifies the assessors to be reviewed; Performance parameters refer to the comprehensive values ​​in the appraisal data that can represent the actual performance appraisal situation of the appraiser and serve as the data basis for calculating the performance appraisal index of the appraiser; Performance parameters include work ability score, work attitude score and work progress score; Specifically, the work ability score refers to the numerical expression of the assessor's work ability, thereby numerically quantifying the assessor's work ability; the work attitude score refers to the numerical expression of the assessor's work attitude, thereby numerically quantifying the assessor's work attitude; the work progress score refers to the numerical expression of the assessor's work progress, thereby numerically quantifying the assessor's work progress; The extraction method of work ability score, work attitude score and work progress score is as follows: The assessment semantics of the assessment data are identified through natural language processing technology, and the assessment semantics are split into assessment text and assessment numbers; the assessment semantics is used to express the true meaning of the assessment data, and the assessment text and assessment numbers are used to express the text and numbers corresponding to the true meaning in the assessment semantics, and provide a direct basis for the subsequent identification of performance parameters; Record the assessment semantics of the assessment text as the ability as the first semantics, and record the assessment number in the first semantics as the work ability score; The assessment semantics of the assessment word "attitude" is recorded as the second semantics, and the assessment number in the second semantics is recorded as the work attitude score; The assessment semantics of the assessment text "progress" is recorded as the third semantics, and the assessment number in the third semantics is recorded as the work progress score; According to the assessment arrangement from front to back, the assessment texts in the assessment data of A assessors are identified one by one to obtain A work ability scores, A work attitude scores and A work progress scores.

[0029] After the performance parameters of the assessors are extracted, the performance parameters of each assessor can be combined to calculate a performance assessment index that can numerically represent the performance assessment scores of the assessors; The calculation method of performance appraisal index is: The work ability scores, work attitude scores and work progress scores of A assessors are assigned corresponding weight coefficients and added together to obtain A performance assessment index; The expression of performance appraisal index is: ; In the formula, For the The performance appraisal index of each appraiser, For the The work ability score of each assessor is For the The work attitude rating of each assessor is For the The work progress rating of each assessor, , , are all weight coefficients greater than 0; and , , The setting logic is the same as above , , The setting logic is consistent.

[0030] In this embodiment, when obtaining the work ability score, work attitude score and work progress score of the assessor, the method adopted is to send a questionnaire with scoring options to the leaders of the department where the assessor is located, colleagues of the department, the assessor himself and the general public, and collect the scores of the assessor from the leaders of the department, colleagues of the department, the assessor himself and the general public, and then take the average value to obtain the score. In this way, the multi-dimensional collection effect of the assessment parameters of the assessor can be achieved, avoiding the limitations brought by the single-dimensional collection method. In the scoring options of the questionnaire, a scoring method with a full score of 5 is adopted, and the scoring options are very satisfied, satisfied, average, passing and dissatisfied, among which the scores corresponding to very satisfied, satisfied, average, passing and dissatisfied are 5, 4, 3, 2 and 1 respectively, so that the scores on the questionnaire can provide a data basis for the assessment parameters of the assessor.

[0031] Personnel to be reviewed refer to those with higher performance appraisal indexes. At this time, the scores of the personnel to be reviewed in several performance appraisal parameters are relatively high. Therefore, the better the performance of the personnel to be reviewed in the lower-level performance appraisal; The identification method for persons to be reviewed is: The performance appraisal indexes of A appraisers are compared one by one with the preset first performance appraisal threshold value; the preset first performance appraisal threshold value refers to the minimum value of the performance appraisal indexes of the appraisers identified as the persons to be reviewed, which can provide a numerical basis for the identification of the persons to be reviewed; the preset first performance appraisal threshold value is obtained by collecting a large number of minimum values ​​of the performance appraisal indexes of the appraisers identified as the persons to be reviewed in history and calculating their average value; When the performance appraisal index of the appraiser is greater than or equal to the preset first performance appraisal threshold, it means that the appraiser has obtained a high score in the low-level performance appraisal calculation and the performance of the performance appraisal is better. Then the appraiser is recorded as a person to be reviewed, and B persons to be reviewed are obtained; When the performance appraisal index of the appraiser is less than the preset first performance appraisal threshold, it means that the appraiser obtained a low score in the low-level performance appraisal calculation. The worse the performance appraisal performance, the less likely the appraiser will be recorded as a person to be reviewed.

[0032] The review calculation module formulates the assessment cycle, collects the second-order parameters of the personnel to be reviewed during the assessment cycle, calculates the second-order review values ​​of the personnel to be reviewed, and arranges the review of the personnel to be reviewed; The appraisal cycle refers to the length of time that the performance appraisal of the person to be reviewed corresponds to, and is used as the collection time limit for the second-order data to ensure that the collected second-order data is reasonable and accurate; In this embodiment, the assessment period is the period from the time when the first assessment data of all the personnel to be reviewed is entered to the time when the last assessment data is entered; illustratively, for the regularity and rationality of the assessment period, the assessment period is usually one month, three months, six months or twelve months; Since the performance appraisal index is calculated based on the performance parameters, the performance appraisal level of the personnel to be reviewed is relatively low. In order to improve the accuracy of the performance appraisal of the personnel to be reviewed, it is necessary to conduct a deeper review calculation on the performance appraisal of the personnel to be reviewed. At this time, the second-order parameters of the personnel to be reviewed should be extracted so that the second-order parameters can represent the performance appraisal status of the personnel to be reviewed at a deeper level. The second-order parameters include effective adoption rate and decision execution; The effective adoption rate refers to the ratio of the number of suggestions put forward by the candidates for review during this assessment cycle that were collected and applied by the agency to the total number of suggestions, which can be used to indicate the degree of understanding of the policy by the candidates for review; The collection method of effective adoption rate is: During the assessment period, the total number of suggestions put forward by the B persons to be reviewed is queried one by one through the database, and the total value of B suggestions is obtained; The suggestions adopted and applied are recorded as valid suggestions, and the number of valid suggestions from B persons to be reviewed is counted to obtain B valid values; Compare the B effective values ​​with the corresponding B total recommended values ​​one by one to obtain B effective adoption rates; The expression of effective adoption rate is: ; In the formula, For the The effective adoption rate of the personnel to be reviewed, =1,2,...,B, For the The effective value of the person to be reviewed, For the The total recommended value for persons to be reviewed.

[0033] The decision execution degree refers to the progress of the decision made by the person to be reviewed during the current assessment cycle, which can be used to indicate the execution effect of the decision made by the person to be reviewed; The method for collecting decision execution is: During the assessment period, the real-time progress of all the execution decisions received by the B reviewers for the first time is queried one by one, and recorded as the initial progress; At the last moment of the assessment cycle, the real-time progress of all the execution decisions of the B personnel to be reviewed is queried one by one and recorded as the current progress; The progress difference between the current progress and the initial progress is recorded as the execution progress, and the execution progress of all the execution decisions of B persons to be reviewed is accumulated and averaged to obtain B decision execution degrees.

[0034] After the effective adoption rate and decision execution degree are collected, the second-order review value of the person to be reviewed is calculated based on the effective adoption rate and decision execution degree, so that the second-order review value can be used as a deeper performance appraisal result for the person to be reviewed; The expression of the second-order review value is: ; In the formula, For the The second-order review value of the personnel to be reviewed, For the The degree of decision execution of the personnel to be reviewed, , are all weight coefficients greater than 0; and , The setting logic is the same as above , , The setting logic is consistent.

[0035] After calculating the second-order review value of the person to be reviewed, it is necessary to arrange the persons to be reviewed in an orderly manner according to the size of the second-order review value, so that the persons to be reviewed after the review arrangement can be placed in a performance appraisal from high to low. Specifically, the larger the second-order review value, the better the person to be reviewed performs in a deeper level of performance appraisal, and the higher the position of the person to be reviewed in the review arrangement, and vice versa.

[0036] The assessment rating module determines the performance level of the assessee based on the performance assessment index and the second-order review value, and sends the performance assessment results to the management platform for storage and management; After calculating the performance appraisal index and the second-order review value, the performance appraisal index and the second-order review value can be used as the basis for performance appraisal to rank the performance appraisal of all appraisers, so that the performance level can represent the specific position and level of the appraiser in the performance appraisal; Performance levels include excellent, good, qualified and unqualified; among them, excellent means that the performance of the appraised person in the current appraisal cycle is at the highest level, good means that the performance of the appraised person in the current appraisal cycle is at the second highest level, qualified means that the performance of the appraised person in the current appraisal cycle is at the middle level, and unqualified means that the performance of the appraised person in the current appraisal cycle is at the lowest level; The methods for determining excellent, good, qualified and unqualified are: The second-order review values ​​of the B candidates to be reviewed are compared one by one with the preset second-order review threshold value; the preset second-order review threshold value refers to the minimum value of the second-order review values ​​of the candidates to be reviewed who are identified as excellent, which can ensure that the candidates to be reviewed of excellent level can be accurately identified; the preset second-order review threshold value is obtained by collecting a large number of minimum values ​​of the second-order review values ​​of the candidates to be reviewed who are identified as excellent in history, and then calculating their average value; When the second-order review value of the person to be reviewed is greater than or equal to the preset second-order review threshold, it means that the person to be reviewed has performed best in the deeper performance appraisal, and the performance level of the person to be reviewed is recorded as excellent; When the second-order review value of the person to be reviewed is less than the preset second-order review threshold, it means that the person to be reviewed performs averagely in the deeper performance appraisal, and the performance level of the person to be reviewed is recorded as good; Remove B persons to be reviewed, and compare the performance appraisal indexes of the remaining appraisers one by one with the preset second performance appraisal threshold; the preset second performance appraisal threshold refers to the minimum value of the performance appraisal index of the appraisers identified as qualified, which can ensure that the appraisers of the qualified level can be accurately identified; the preset second performance appraisal threshold is obtained by collecting the minimum values ​​of the performance appraisal indexes of a large number of appraisers identified as qualified in history and calculating their average value; When the performance appraisal index of the appraiser is greater than or equal to the preset second performance appraisal threshold, it means that the appraiser has performed well in the low-level performance appraisal, and the performance level of the appraiser is recorded as qualified; When the performance appraisal index of the appraiser is less than the preset second performance appraisal threshold, it means that the appraiser's performance in the low-level performance appraisal is poor, and the performance level of the appraiser is recorded as unqualified.

[0037] After the performance level of the assessor is determined, it is necessary to combine the performance appraisal index, second-order review value and performance level of A assessors during the performance appraisal one by one to generate a performance appraisal result corresponding to A assessors, and finally send the A performance appraisal result to the management platform synchronously, so that the management platform can store and manage the performance appraisal results of A assessors, so as to facilitate subsequent query and verification operations on the assessors.

[0038] In this embodiment, by dividing the assessors into subsets and filtering the assessment data from the database, assessors of different types and levels can be identified and divided in an orderly and accurate manner, so that assessment data matching the assessors can be quickly and accurately filtered out from the database based on the assessment attributes, thereby improving the accuracy of the assessment data screening; By extracting the assessment ranking parameters from the assessment data and calculating the assessment priority value of the assessor, the assessment priority value can be used as the standard for the order of performance assessment calculation for each assessor, and the performance assessment order of the assessors can be limited in an orderly manner, so that each assessor can maintain an independent performance assessment status, preventing interference and influence that may occur when multiple similar assessment data are calculated synchronously, thereby achieving an orderly performance assessment calculation effect; By extracting performance parameters from the assessment data, calculating the performance assessment index of the assessee, and identifying the assessees to be reviewed from the assessees, it is possible to conduct low-level performance assessment on the assessees, achieve the one-time performance assessment effect of the assessees, and provide an accurate basis for identifying the assessees to be reviewed; By collecting the second-order parameters of the personnel to be reviewed during the assessment cycle and calculating the second-order review values ​​of the personnel to be reviewed, a deeper review calculation operation can be performed on the performance assessment of the personnel to be reviewed, achieving the effect of the second performance assessment of the assessor, thereby avoiding the inaccurate calculation phenomenon existing in the traditional one-time performance assessment, and also avoiding the problem of the limitation of the assessment data dimension in the one-time performance assessment, thus greatly improving the accuracy of the performance assessment results; By determining the performance level of the appraiser and sending the performance appraisal results to the management platform, the performance appraisal results of the appraiser can be accurately and completely displayed and stored, providing management personnel with reasonable and effective performance appraisal data to facilitate subsequent performance management operations.

[0039] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A performance appraisal data management platform, applied to an appraisal server, characterized in that: include: The data screening module is used to divide the assessors into subsets according to their assessment attributes and screen the assessment data from the database; The assessment ranking module is used to extract assessment ranking parameters from the assessment data. The assessment ranking parameters include effective input time, data completeness rate and effective quantity value, and calculate the assessment priority value of the assessor; The assessment calculation module is used to extract performance parameters from the assessment data, calculate the performance assessment index of the assessee, and identify the assessees to be reviewed from among the assessees; The review calculation module is used to collect the second-order parameters of the personnel to be reviewed during the assessment period. The second-order parameters include the effective adoption rate and the decision execution degree, and calculate the second-order review value of the personnel to be reviewed; The assessment rating module is used to determine the performance level of the assessee and send the performance assessment results to the management platform.

2. A performance appraisal data management platform according to claim 1, characterized in that: The subsets include a first subset, a second subset and a third subset; The division method of the first subset, the second subset and the third subset is: The identity information of each assessor is retrieved one by one through the personnel management system, and the agency level and assessor name of the assessor are marked in the identity information; The level fields in the A levels of the agencies are identified one by one through natural language processing technology, and the character parts in the level fields are recorded as the level status; The assessment attributes corresponding to the level fields with level statuses of "A-0", "A-1" and "A-2" are recorded as basic-level attributes, middle-level attributes and high-level attributes respectively; The assessors whose assessment attributes are grassroots attributes, middle-level attributes and high-level attributes are respectively summarized to generate a first subset, a second subset and a third subset.

3. A performance appraisal data management platform according to claim 2, characterized in that: The screening method for assessment data is: The level status of all comprehensive data in the database is queried one by one, and the comprehensive data with the level status of low level, medium level and high level are respectively summarized to generate the first data layer, the second data layer and the third data layer; Mark the names of the personnel in the comprehensive data in the first data layer, the second data layer, and the third data layer one by one, and compare the names of the personnel in the comprehensive data with the assessment names of the assessors; Record the comprehensive data whose names of personnel in the first data layer are consistent with the assessment names of the assessors in the first subset as assessment data; Record the comprehensive data whose personnel names in the second data layer are consistent with the assessment names of the assessors in the second subset as assessment data; The comprehensive data whose personnel names in the third data layer are consistent with the assessment names of the assessors in the third subset is recorded as assessment data.

4. A performance appraisal data management platform according to claim 3, characterized in that: The method for extracting effective input duration, data completeness rate and effective quantity values ​​is as follows: Match A assessors with corresponding assessment data one by one to generate A data packets; The entry time of the first assessment data and the last assessment data in A data packets are queried one by one through the timestamps, and recorded as the entry start time and the entry end time respectively; The time between the recording start time and the recording end time of A data packets is recorded as the effective recording time, and A effective recording time is obtained; Query the filling status of all the assessment data in the A data packets one by one, and record the assessment data with a filling status of complete filling as complete data; The number of complete data and the total number of test data in A data packets are counted respectively, and the number of complete data is compared with the total number of test data to obtain the data completeness rate; The expression of data integrity rate is: ； In the formula, For the The data integrity rate of the data packets, =1,2,...,A, For the The number of complete data in a packet, For the The total number of assessment data in a data package; Perform security checks on all the complete data in the A data packets one by one, and count the number of complete data that pass the security check to obtain A valid number values.

5. A performance appraisal data management platform according to claim 4, characterized in that: The calculation method of assessment priority value is: The effective input duration, data integrity rate and effective quantity of A data packets are assigned corresponding weight coefficients and compared to obtain A assessment priority values; The expression of the assessment priority value is: ； In the formula, For the The assessment priority value of each assessor. No. The effective recording time of a data packet, No. The effective number of packets. , , All of them are weight coefficients greater than 0.

6. A performance appraisal data management platform according to claim 5, characterized in that: Performance parameters include work ability score, work attitude score and work progress score; The extraction method of work ability score, work attitude score and work progress score is as follows: Identify the assessment semantics of the assessment data through natural language processing technology, and split the assessment semantics into assessment text and assessment numbers; The assessment numbers in the assessment data with the assessment words of ability, attitude and progress are recorded as work ability score, work attitude score and work progress score respectively; According to the assessment priority value from large to small, the assessment texts in the assessment data of A assessors are identified one by one to obtain A work ability scores, A work attitude scores and A work progress scores.

7. A performance appraisal data management platform according to claim 6, characterized in that: The calculation method of performance appraisal index is: The work ability scores, work attitude scores and work progress scores of A assessors are assigned corresponding weight coefficients and added together to obtain A performance assessment index; The identification method for persons to be reviewed is: Compare the performance appraisal indexes of A appraisers one by one with the preset first performance appraisal threshold; When the performance appraisal index of the appraiser is greater than or equal to the preset first performance appraisal threshold, the appraiser is recorded as a person to be reviewed, and B persons to be reviewed are obtained; When the performance appraisal index of the appraiser is less than the preset first performance appraisal threshold, the appraiser will not be recorded as a person to be reviewed.

8. A performance appraisal data management platform according to claim 7, characterized in that: The assessment period is the period between the time when the first assessment data of all personnel to be reviewed is entered and the time when the last assessment data is entered; The collection method of effective adoption rate is: During the assessment period, the total number of suggestions put forward by the B persons to be reviewed is queried one by one through the database, and the total value of B suggestions is obtained; The suggestions adopted and applied are recorded as valid suggestions, and the number of valid suggestions from B persons to be reviewed is counted to obtain B valid values; Compare the B effective values ​​with the corresponding B total recommended values ​​one by one to obtain B effective adoption rates; The expression of effective adoption rate is: ； In the formula, For the The effective adoption rate of the personnel to be reviewed, =1,2,...,B, For the The effective value of the person to be reviewed, For the The total recommended value for persons to be reviewed.

9. A performance appraisal data management platform according to claim 8, characterized in that: The method for collecting decision execution is: During the assessment period, the real-time progress of all the execution decisions received by the B reviewers for the first time is queried one by one, and recorded as the initial progress; At the last moment of the assessment cycle, the real-time progress of all the execution decisions of the B personnel to be reviewed is queried one by one and recorded as the current progress; The progress difference between the current progress and the initial progress is recorded as the execution progress, and the execution progress of all the execution decisions of the B persons to be reviewed is accumulated and averaged to obtain the B decision execution degrees; The expression of the second-order review value is: ； In the formula, For the The second-order review value of the personnel to be reviewed, For the The degree of decision execution of the personnel to be reviewed, , All of them are weight coefficients greater than 0.

10. A performance appraisal data management platform according to claim 9, characterized in that: Performance levels include excellent, good, acceptable, and unsatisfactory; The determination methods for excellent, good, qualified and unqualified are: Compare the second-order review values ​​of the B persons to be reviewed with the preset second-order review thresholds one by one; When the second-order review value of the person to be reviewed is greater than or equal to the preset second-order review threshold, the performance level of the person to be reviewed is recorded as excellent; When the second-order review value of the person to be reviewed is less than the preset second-order review threshold, the performance level of the person to be reviewed is recorded as good; Remove B persons to be reviewed, and compare the performance appraisal indexes of the remaining appraisers one by one with the preset second performance appraisal threshold; When the performance appraisal index of the appraiser is greater than or equal to the preset second performance appraisal threshold, the performance level of the appraiser is recorded as qualified; When the performance appraisal index of the appraiser is less than the preset second performance appraisal threshold, the performance level of the appraiser is recorded as unqualified.

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