CN112990646A - Performance assessment and evaluation method for workers - Google Patents
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Abstract
The invention relates to the technical field of performance assessment, in particular to a performance assessment method for workers, which comprises the following steps: s1, collecting daily work data of workers; s2, carrying out multi-dimensional statistics on the daily working data according to types, departments, jobs, time and events to obtain statistical data; s3, calculating the statistical data by adopting spark distributed algorithm to obtain objective performance; s4, performing periodic scoring evaluation according to a scoring period according to a competence model of an omnibearing assessment level to obtain subjective results, wherein the competence model consists of five dimensions of moral, energy, duty, performance and cheapness, and a scoring item is preset in each dimension; and S5, obtaining a comprehensive result by weighting the average objective result and the subjective result. The invention solves the technical problem that the prior art can only carry out assessment on workers from an objective angle and can not carry out assessment and evaluation on the workers comprehensively.
Description
Technical Field
The invention relates to the technical field of performance assessment, in particular to a performance assessment method for workers.
Background
At present, the performance assessment work of workers is mainly achieved through manual summarization, the speed is low, the efficiency is low, and open, efficient, quick and fair assessment is difficult to achieve. Meanwhile, part of the work content of the workers is difficult to quantify and accurate evaluation is difficult, so that the evaluation result is difficult to achieve the expected incentive effect, the enthusiasm, the initiative and the creativity of the workers cannot be mobilized, and the strength of the prize is insufficient.
In this regard, document CN107767057A discloses an "internet +" worker performance assessment system, which includes: the data entry module is used for acquiring attendance data, GPS (global positioning system) position information, daily work data of workers and case detection data; the statistical analysis module is used for carrying out multi-dimensional statistics on the data acquired by the data acquisition module according to types, departments, jobs, time and events, and then calculating the statistical data by spark distributed calculation to obtain the performance assessment scores of the workers; the data storage module is used for storing the data acquired by the data entry module and storing the result data obtained by statistical analysis of the statistical analysis module; and the display module is used for displaying the data acquired by the data entry module and the calculation result of the statistical analysis module.
In the technical scheme, an all-directional and multi-level performance assessment system is established for the workers, the working states and working attitudes of the workers can be analyzed and predicted by assessing and analyzing the performance data of the workers, the requirements of whole-person examination and large-ranking are fully met, and the working enthusiasm of the workers is really mobilized. However, in the prior art, only quantifiable data is used, that is, only the staff can be assessed objectively, and whether the quality, performance, cheapness and the like of the staff reach the standard cannot be reflected, so that the staff cannot be assessed and evaluated comprehensively.
Disclosure of Invention
The invention provides a performance assessment method for workers, which solves the technical problem that the prior art can only assess the workers from an objective angle and can not comprehensively assess and evaluate the workers.
The basic scheme provided by the invention is as follows: the performance assessment evaluation method for the workers comprises the following steps:
s1, collecting daily work data of workers;
s2, carrying out multi-dimensional statistics on the daily working data according to types, departments, jobs, time and events to obtain statistical data;
s3, calculating the statistical data by adopting spark distributed algorithm to obtain objective performance;
s4, performing periodic scoring evaluation according to a scoring period according to a competence model of an omnibearing assessment level to obtain subjective results, wherein the competence model consists of five dimensions of moral, energy, duty, performance and cheapness, and a scoring item is preset in each dimension;
and S5, obtaining a comprehensive result by weighting the average objective result and the subjective result.
The working principle and the advantages of the invention are as follows: firstly, collecting daily work data of workers; then, calculating objective scores of the workers according to the daily work data, namely carrying out multi-dimensional statistics on the daily work data according to types, departments, jobs, time and events to obtain statistical data, and calculating the statistical data through a spark distributed algorithm to obtain the objective scores; secondly, according to a competence model of an omnibearing assessment level, wherein the competence model consists of five dimensions of moral, energy, duty, performance and expense, and each dimension is preset with a scoring item; carrying out periodic scoring evaluation according to a scoring period to obtain subjective results; and finally, obtaining the comprehensive achievement by weighting the average objective achievement and the subjective achievement.
According to the comprehensive assessment method, the objective performance is obtained by calculating the statistical data through the spark distributed algorithm, the subjective performance is obtained by periodically scoring and evaluating according to the scoring period according to the competence model of the omnibearing assessment level, and the comprehensive performance is obtained by weighting the average objective performance and the subjective performance, so that the technical problem that in the prior art, the workers can only be assessed from an objective angle and cannot be assessed and evaluated comprehensively is solved.
Further, in S4, the full score and the default score of a single dimension are customized in units of departments, and the default score is a score for which evaluation is not completed within a time limit.
Has the advantages that: by the mode, each department can select and set full scores and default scores suitable for the department according to actual conditions or statistics and scoring habits, so that score entry and statistics are facilitated.
Further, in S4, the single dimension includes two scoring methods: the scoring adopts an adding and subtracting button, the plus button is clicked once to add a metering unit, and the minus button is clicked once to subtract a metering unit; the score is entered by itself by clicking on the score value.
Has the advantages that: by adopting the mode, when the score is not greatly different from the default score, the score can be recorded by adding one metering unit or subtracting one metering unit by adopting the addition and subtraction button, so that the method is convenient and quick, and errors are not easy to make; on the contrary, when the score is greatly different from the default score, the score can be automatically input by clicking the score value, so that the efficiency of recording the score is improved.
Further, in S4, a first score threshold value and a second score threshold value are set, and when the score is lower than the first score threshold value or higher than the second score threshold value, a reason for the score is presented and input, and the first score threshold value is smaller than the second score threshold value.
Has the advantages that: by the mode, when the score is too high or too low, the input of the score reason is prompted, so that the input of the check score is correct or not can be reminded, and the condition that the score is not in accordance with the actual condition can be avoided being input randomly or without any foundation.
Further, in S4, the single dimension score is the top score of the straight genus × weight 1+ the weighted average of the same grade score × weight 2+ the weighted average of the next grade score × weight 3+ the self-score × weight 4, and weight 1, weight 2, weight 3, and weight 4 are customized in units of departments; when the sibling score and the subordinate score are absent, a single dimension score is directly superior score multiplied by weight 1+ self score multiplied by weight 4.
Has the advantages that: by means of the method, the directly superior, the peer, the subordinate and the self evaluate at the same time, different weights are given to the directly superior, the peer, the subordinate and the self evaluate in a weighted average mode, and compared with the evaluation performed by the directly superior, the peer, the subordinate and the self independently, the obtained score can reflect the actual situation more truly.
Further, daily work data includes location punch-card, work, duty and field work.
Has the advantages that: generally, the data can reflect the work content and the work state of workers, and is beneficial to accurately assessing the performance.
Drawings
FIG. 1 is a flow chart of an embodiment of a performance assessment evaluation method for workers according to the invention.
Detailed Description
The following is further detailed by the specific embodiments:
example 1
The embodiment of the performance assessment evaluation method for the workers is basically as shown in the attached figure 1, and comprises the following steps:
s1, collecting daily work data of workers;
s2, carrying out multi-dimensional statistics on the daily working data according to types, departments, jobs, time and events to obtain statistical data;
s3, calculating the statistical data by adopting spark distributed algorithm to obtain objective performance;
s4, performing periodic scoring evaluation according to a scoring period according to a competence model of an omnibearing assessment level to obtain subjective results, wherein the competence model consists of five dimensions of moral, energy, duty, performance and cheapness, and a scoring item is preset in each dimension;
and S5, obtaining a comprehensive result by weighting the average objective result and the subjective result.
The specific implementation process is as follows:
and S1, collecting daily work data of workers.
In this embodiment, the acquired data includes attendance data transmitted by an attendance system, GPS position information uploaded by a mobile phone of a worker, and daily work data of the worker; the daily work data comprises positioning card punching, working, duty and outwork data. The quantifiable data, such as attendance data and GPS information, are automatically recorded and stored by the system; and as for the unquantizable data, for example, daily work data of workers are input by the workers through a computer or mobile equipment, the input data are audited by a department leader, and the audited data are input into the performance assessment system as effective data after being audited.
And S2, carrying out multi-dimensional statistics on the daily working data according to the category, the department, the job, the time and the event to obtain statistical data.
That is to say, the collected attendance data transmitted by the attendance system, the GPS position information uploaded by the mobile phone of the staff, the daily work data of the staff and the case detection data transmitted by the case detection management platform are subjected to multi-dimensional statistics according to the category, the department, the duty, the time and the event.
And S3, calculating the statistical data by adopting spark distributed algorithm to obtain objective performance.
When a Spark program is prepared to run, data sources need to be provided, for example, common read data sources are HDFS (Hadoop distributed file system) and Hive in a Hadoop cluster, and also can be HBase built on the cluster, and DB databases such as MySQL (structured query language) and the like; or, it is set as aggregate data in the program. Distributed computing is the most basic characteristic of Spark, and data can be stored in each cluster node in a distributed manner during computing to realize parallel distributed computing. For example, for the first operation map, map operator operation is performed on data on nodes 1, 2, and 3, and the processed data may be transferred to memories of other nodes, assuming that nodes 4, 5, and 6 are obtained; the second operation reduce is to process the map-processed data again. Moreover, Spark is an iterative computation model, and one computation logic can be divided into N stages, and the result data of the previous stage becomes the input data of the next stage, thereby providing a more powerful computation logic function. Through the mode, the statistical data are calculated, and the performance assessment result of the worker can be obtained.
And S4, performing periodic scoring evaluation according to the scoring period according to the competence model of the omnibearing assessment level to obtain subjective results, wherein the competence model consists of five dimensions of moral, energy, duty, performance and cheapness, and a scoring item is preset in each dimension.
In this embodiment, the full score and the default score of a single dimension are defined by taking a department as a unit, the default score is a score which does not finish evaluation within a time limit, for example, the full score of moral, energetic, duty, performance and cheapness is defined as 100, and the default score is defined as 80. A single dimension includes two scoring modes: the scoring adopts an adding and subtracting button, the plus button is clicked once to add a metering unit, and the minus button is clicked once to subtract a metering unit; the score is entered by itself by clicking on the score value. For example, for moral, energy, duty, performance and cheapness, the metering unit is 5 points, 5 points are added on the basis of 80 points by clicking the plus button once, and 5 points are subtracted on the basis of 80 points by clicking the minus button once; or self-input a score, such as 83.
Meanwhile, a first score threshold value and a second score threshold value are also set, when the score is lower than the first score threshold value or higher than the second score threshold value, the input of a score reason is prompted, and the first score threshold value is smaller than the second score threshold value. For example, the first score threshold is 60 points, the second score threshold is 90 points, and when the score is too high or too low, that is, the score is higher than 90 points or lower than 60 points, it is prompted that a text description score reason needs to be input.
In this embodiment, for a single dimension score, the calculation formula is that the single dimension score is directly superior score × weight 1+ weighted average of sibling scores × weight 2+ weighted average of subordinate scores × weight 3+ self-scoring × weight 4, and weight 1, weight 2, weight 3, and weight 4 are customized in units of departments; when the sibling score and the subordinate score are absent, a single dimension score is directly superior score multiplied by weight 1+ self score multiplied by weight 4. For example, the weight 1 is 0.3, the weight 2 is 0.3, the weight 3 is 0.3, and the weight 4 is 0.1.
And S5, obtaining a comprehensive result by weighting the average objective result and the subjective result.
Specifically, the calculation formula of the overall result is α × objective result + β × subjective result; wherein alpha is more than 0 and less than 1, beta is more than 0 and less than 1, alpha is the weight of objective achievement, and beta is the weight of subjective achievement. For example, α is set to 0.7 and β is set to 0.3.
Example 3
The difference from embodiment 2 is only that, in this embodiment, the card punching data of the employee needs to be collected in advance. When the card punching data of the staff is collected, the amount of exercise, the face state and the card punching background of the staff in unit time are gradually collected. Specifically, when the employee walks to the card terminal, first, the amount of movement of the employee per unit time is collected. In this embodiment, the amount of exercise of the employee per unit time is the number of exercise steps of the employee per unit time, and can be calculated by inputting the exercise data on the WeChat by the employee himself. For example, if the time from home to unit is 20 minutes and the number of steps of the exercise is 3600 steps, the amount of exercise of the employee per unit time is 180 steps/minute. If the amount of exercise per unit time exceeds a preset threshold, for example, the preset threshold is 150 steps/minute, the staff is reminded to reasonably plan the travel time in a voice mode. Then, the face state of the employee is collected. In the embodiment, the facial images of the employees are collected through the camera, and the facial states of the employees, namely the facial emotional expressions, are identified through FaceReader software. If the employee's facial state is negative, such as "sad" or angry ", the employee's body temperature is detected by the infrared thermometers: if the body temperature of the staff exceeds a temperature threshold value, for example, the temperature threshold value is 37.2 ℃, the staff is reminded to wear the mask, and body temperature data is reported. And finally, collecting a background picture of the employee's card punching. In this embodiment, a camera is used to capture pictures of a preset frame number, for example, 10 pictures, when the employee makes a card, and a motion recognition algorithm is used to determine whether the body of the employee is still or moving. If the body of the employee is moving, the employee is prompted to punch the card in advance through voice, which indicates that the employee is possibly busy; on the contrary, if the body of the employee is static, which indicates that the employee is not particularly busy, the employee prompts the other person to punch the card in advance through voice.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (6)
1. The performance assessment evaluation method for the workers is characterized by comprising the following steps of:
s1, collecting daily work data of workers;
s2, carrying out multi-dimensional statistics on the daily working data according to types, departments, jobs, time and events to obtain statistical data;
s3, calculating the statistical data by adopting spark distributed algorithm to obtain objective performance;
s4, performing periodic scoring evaluation according to a scoring period according to a competence model of an omnibearing assessment level to obtain subjective results, wherein the competence model consists of five dimensions of moral, energy, duty, performance and cheapness, and a scoring item is preset in each dimension;
and S5, obtaining a comprehensive result by weighting the average objective result and the subjective result.
2. The performance assessment evaluation method for workers of claim 1, wherein in S4, the full score and default score of a single dimension are customized in units of departments, and the default score is a score that does not complete evaluation within a time limit.
3. The performance assessment evaluation method for workers of claim 2, wherein in S4, a single dimension comprises two scoring modes: the scoring adopts an adding and subtracting button, the plus button is clicked once to add a metering unit, and the minus button is clicked once to subtract a metering unit; the score is entered by itself by clicking on the score value.
4. The performance assessment evaluation method for workers according to claim 3, wherein in S4, a first score threshold value and a second score threshold value are set, when the score is lower than the first score threshold value or higher than the second score threshold value, a prompt is made to input a score reason, and the first score threshold value is smaller than the second score threshold value.
5. The performance assessment evaluation method for workers according to claim 4, wherein in S4, a single dimension score is orthodox superior score x weight 1+ weighted average of sibling scores x weight 2+ weighted average of subordinate scores x weight 3+ self-scoring x weight 4, and weight 1, weight 2, weight 3 and weight 4 are customized in units of departments; when the sibling score and the subordinate score are absent, a single dimension score is directly superior score multiplied by weight 1+ self score multiplied by weight 4.
6. The performance assessment evaluation method for workers of claim 5, wherein daily work data includes location punch, work, duty and field.
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| CN112507972A (en) * | 2020-12-28 | 2021-03-16 | 贵州东冠科技有限公司 | Performance assessment system based on block chain |
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| CN114936814A (en) * | 2022-07-26 | 2022-08-23 | 希望知舟技术(深圳)有限公司 | Employee grade determination method and related device |
| CN114971434A (en) * | 2022-08-01 | 2022-08-30 | 广州天维信息技术股份有限公司 | Performance comparison analysis system based on distributed computation |
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| CN114971434A (en) * | 2022-08-01 | 2022-08-30 | 广州天维信息技术股份有限公司 | Performance comparison analysis system based on distributed computation |
| CN114971434B (en) * | 2022-08-01 | 2022-10-21 | 广州天维信息技术股份有限公司 | Performance comparison analysis system based on distributed computation |
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