CN112507972B - Performance assessment system based on blockchain - Google Patents
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Abstract
The invention relates to the technical field of performance assessment, in particular to a performance assessment system based on a blockchain, which comprises the following steps: the input unit is used for uploading the work record of each employee; the statistics unit is used for the staff to check the work records in the blockchain, and score the work records to obtain a scoring table; the judging unit is used for comparing the effective working time with the preset rated working time to obtain a comparison result, and judging whether the scores in the scoring table are effective or not according to the comparison result; the correction unit is used for analyzing reasonable working time length of which the working state in the monitoring video meets the preset requirement, correcting the effective score according to the reasonable working time length and the effective working time length, and obtaining a corrected scoring table; and the output unit is used for outputting the corrected scoring table in a visual mode. The invention solves the technical problems that the prior art only can qualitatively check the performance of staff and cannot quantitatively check the performance of staff.
Description
Technical Field
The invention relates to the technical field of performance assessment, in particular to a performance assessment system based on blockchain.
Background
Performance assessment is an extremely important part of human resource management, both for enterprises and factories. The good performance assessment method can effectively play an excitation role, avoid the waste of human resources and further reduce the human cost. At present, common performance assessment methods comprise balanced scorecards, key performance indexes and the like, and the performance assessment methods are low in transparency, easy to tamper with data and low in credibility.
The development of the blockchain technology provides a new thought for establishing a performance assessment method which has high transparency and reliability and is difficult to tamper with data. For example, chinese patent CN111275395A discloses a block chain-based decentralised enterprise performance assessment method, comprising the steps of: constructing a private block chain inside a company; each employee becomes a user node in the blockchain; uploading information such as monthly work records, attendance records and the like of each employee to a blockchain; staff check the work records of others in the blockchain and score the work conditions of others; and the scoring table is taken as a performance assessment score result, is summarized and stored in a blockchain, and cannot be tampered.
In the technical scheme, staff mutually score the working conditions of each other, the obtained scoring table is used as a performance assessment score result and is summarized and stored in the blockchain, and compared with the traditional performance assessment mode, the performance assessment method has the advantages that the transparency is high, the data are not easy to tamper, and the reliability is high. However, staff often cannot accurately know the workload of each other, and even if they know, they are quite often rough and approximate, so that the scores between staff cannot accurately reflect the corresponding workload. That is, the prior art only can qualitatively check the performance of the staff, but cannot quantitatively check the performance of the staff.
Disclosure of Invention
The invention provides a performance assessment system based on a blockchain, which solves the technical problems that the prior art only can qualitatively assess the performance of staff and cannot quantitatively assess the performance of the staff.
The basic scheme provided by the invention is as follows: a blockchain-based performance assessment system, comprising:
the input unit is used for uploading the work record of each employee, wherein the work record comprises card punching data and monitoring video;
the statistics unit is used for enabling staff to mutually check the work records in the blockchain, scoring the work records to obtain a scoring table, and storing the scoring table in the blockchain; the method is also used for acquiring trace information from the work records of staff, calculating effective working time according to the trace information and storing the effective working time into a blockchain;
the judging unit is used for comparing the effective working time length with the preset rated working time length to obtain a comparison result, and judging whether the scores in the scoring table are effective or not according to the comparison result: if the effective working time length is longer than or equal to the preset rated working time length, judging that the score is effective; if the effective working time length is smaller than the preset rated working time length, judging that the score is invalid;
The correction unit is used for extracting effective scores in the scoring table and corresponding monitoring videos, analyzing reasonable working time lengths of which the working states meet preset requirements in the monitoring videos, correcting the effective scores according to the reasonable working time lengths and the effective working time lengths, and obtaining a corrected scoring table;
and the output unit is used for outputting the corrected scoring table in a visual mode.
The working principle and the advantages of the invention are as follows:
(1) If the effective working time length of the staff is longer than or equal to the preset rated working time length, indicating that the workload is oversaturated and saturated, and grading on the basis is meaningful; otherwise, if the effective working time of the staff is smaller than the preset rated working time, the working amount is unsaturated, and the score has no reference value. In this way, effective scores are selected from the scoring table to accurately evaluate the performance of the employee.
(2) Even if the effective working time length of the staff is longer than or equal to the preset rated working time length, it is difficult to ensure that the staff is always in a high-quality working state, and the low-quality working state is more concealed and is not easy to be found by other colleagues, and the influence of the low-quality working state is difficult to reflect in the scoring. Therefore, it is necessary to determine a reasonable working time length for which the working state meets the preset requirement, that is, a time length of the high-quality working state, according to the monitoring video, and correct the score according to the time length. By the method, the corrected scoring table can accurately reflect the working time corresponding to the high-quality working state, so that the performance assessment is ensured to be fairer.
The corrected scoring table obtained by the invention can accurately reflect the working time length corresponding to the high-quality working state, and solves the technical problems that the prior art can only qualitatively check the performance of staff and cannot quantitatively check the performance of staff.
Further, the system also comprises a storage unit for storing standard images of the action behaviors of the staff when the working state meets the preset requirement; the correction unit is also used for acquiring a standard image, training the standard image to obtain an effective working state identification model, identifying action behaviors in the monitoring video through the effective working state identification model, and eliminating action behaviors in the monitoring video which cannot be identified by the effective working state identification model.
The beneficial effects are that: by the mode, the effective working state identification model obtained by training the standard image can identify the action behaviors in the monitoring video, and the reasonable working time length of the working state in the monitoring video meeting the preset requirement can be accurately obtained after the action behaviors which cannot be identified by the effective working state identification model in the monitoring video are removed.
Furthermore, the correction unit also eliminates video frames which are not in the identification range in the monitoring video in advance, wherein the video frames which are not in the identification range are video frames without action behaviors.
The beneficial effects are that: by the mode, misjudgment can be avoided, so that judgment of reasonable working time length for which the working state in the monitoring video meets the preset requirement is affected.
Furthermore, the correction unit is also used for presetting an accuracy threshold value and filtering out video frames with the accuracy value smaller than the accuracy threshold value in the monitoring video through the effective working state identification model.
The beneficial effects are that: by means of the method, the accuracy threshold is preset to filter the video frames in the monitoring video, and therefore high accuracy of the action behavior recognized by the effective working state recognition model can be guaranteed.
Further, the correction unit is also used for extracting dialogue voice from the monitoring video and recognizing the content of the dialogue voice; and eliminating the video frames smaller than the precision threshold value in the monitoring video when the content of the dialogue voice can not reasonably explain the video frames smaller than the precision threshold value in the monitoring video.
The beneficial effects are that: by the method, misjudgment caused by unclear shooting of the monitoring video can be avoided, and accuracy of correction of the scoring table is improved.
The correction unit is further used for identifying the number of action behaviors according to the effective working state identification model, generating effective workload and correcting the scores according to the effective workload.
The beneficial effects are that: in this way, the effect of lazy and small differences can be greatly subtracted from the action statistics of the effective workload.
Further, the correction unit is also used for carrying out face detection on the monitoring video by adopting a face recognition algorithm to obtain face information; and determining the employee identity according to the face information, and associating the action behavior which can be identified by the effective working state identification model with the employee identity.
The beneficial effects are that: by the mode, the action behavior is corresponding to the employee identity, so that the follow-up checking is facilitated.
Drawings
Fig. 1 is a block diagram of a system architecture of an embodiment of a blockchain-based performance assessment system of the present invention.
Detailed Description
The following is a further detailed description of the embodiments:
Example 1
An example is substantially as shown in figure 1: comprising the following steps:
the input unit is used for uploading the work record of each employee, wherein the work record comprises card punching data and monitoring video;
the statistics unit is used for enabling staff to mutually check the work records in the blockchain, scoring the work records to obtain a scoring table, and storing the scoring table in the blockchain; the method is also used for acquiring trace information from the work records of staff, calculating effective working time according to the trace information and storing the effective working time into a blockchain;
the judging unit is used for comparing the effective working time length with the preset rated working time length to obtain a comparison result, and judging whether the scores in the scoring table are effective or not according to the comparison result: if the effective working time length is longer than or equal to the preset rated working time length, judging that the score is effective; if the effective working time length is smaller than the preset rated working time length, judging that the score is invalid;
The correction unit is used for extracting effective scores in the scoring table and corresponding monitoring videos, analyzing reasonable working time lengths of which the working states meet preset requirements in the monitoring videos, correcting the effective scores according to the reasonable working time lengths and the effective working time lengths, and obtaining a corrected scoring table;
and the output unit is used for outputting the corrected scoring table in a visual mode.
In this embodiment, the input unit, the statistics unit, the judgment unit, the correction unit, the storage unit and the output unit are integrated on the server, and the functions thereof are realized through software/program/code; the private blockchain inside the company is built on the server, each employee becomes a node in the blockchain, and P2P communication transmission is used among the nodes.
The specific implementation process is as follows:
first, the work records of all staff are uploaded, and each work record of staff comprises card punching data of working, going out and the like and monitoring videos shot in real time in a working area (such as an office and a workshop). The work records of all employees are packaged into a block, the block is stored to the chain tail of the blockchain, the block is stored at each node in the private blockchain in a redistribution mode, and the computer nodes of all employees can see the work records of all employees.
The employees then view the work records from each other in the blockchain, score each other according to the work records to obtain a score table, and save the score table to the blockchain. For example, staff links the private blockchain by using their own computer nodes, checks work records uploaded by other staff in the private blockchain, scores work contributions of other staff, and the score of each staff can generate a scoring table and calculate the average score of each staff in the scoring table. Meanwhile, trace information is obtained from the work records of the staff, effective working time length is calculated according to the trace information, and the effective working time length is stored in the blockchain. Trace information is left in the working process, for example, card punching is carried out when the person goes out, and the person can perform attendance card punching when the working place on a certain day is inconsistent with the working place at ordinary times; by these time points of the punch, the effective working time period per day as well as per month can be obtained.
Then, comparing the effective working time length with a preset rated working time length to obtain a comparison result, and judging whether the scores in the scoring table are effective one by one according to the comparison result: if the effective working time length is longer than or equal to the preset rated working time length, judging that the score is effective; and if the effective working time is smaller than the preset rated working time, judging that the score is invalid. For example, the preset rated working time period of each day is set to 7.5 hours, if the effective working time period is 8 hours and 7.5 hours, the working time period is greater than or equal to the preset rated working time period, the working load is supersaturated and saturated, and the grading is judged to be effective; otherwise, if the effective working time is 7.2 hours and is smaller than the preset rated working time, indicating that the workload is unsaturated, and judging that the score is invalid.
And then, extracting effective scores in the scoring table and corresponding monitoring videos, analyzing reasonable working time length of which the working state meets the preset requirement in the monitoring videos, and correcting the effective scores according to the reasonable working time length and the effective working time length to obtain a corrected scoring table. Specifically, the server stores in advance standard images of the behavior of each employee when the working state satisfies the preset requirement, for example, the images are captured in advance by a whole-body swing method. And acquiring the standard images, and repeatedly training the standard images by adopting a neural network algorithm to acquire an effective working state identification model. After an effective working state identification model is obtained, video frames which are not in an identification range in the monitoring video, namely video frames without action behaviors, are removed in advance; meanwhile, filtering out video frames with the precision value smaller than the precision threshold value in the monitoring video through an effective working state identification model according to the preset precision threshold value; and identifying the action behaviors in the monitoring video (the video frames with no action behaviors and the accuracy value smaller than the accuracy threshold value are removed) through the effective working state identification model, removing the action behaviors in the monitoring video which cannot be identified by the effective working state identification model, wherein the time length in the monitoring video corresponding to the rest action behaviors is reasonable working time length. If the reasonable working time length is 7.2 hours and the effective working time length is 7.5 hours, a certain effective score is 8 points, and the calculation formula of the corrected effective score is that the corrected effective score=the reasonable working time length/the effective working time length×the effective score=7.2/7.5x8=7.7 points.
Finally, the revised scoring table, for example in the form of an Excel table, is visually exported.
Example 2
The difference from embodiment 1 is only that dialogue speech is extracted from the monitor video before eliminating video frames smaller than the accuracy threshold value in the monitor video, and the content of the dialogue speech is recognized; and carrying out semantic recognition on the content, and eliminating the video frames smaller than the precision threshold value in the monitoring video when the content of the dialogue voice can not reasonably explain the video frames smaller than the precision threshold value in the monitoring video. For example, the content of the dialogue voice shows that the office fails or lights out on the same day, which causes insufficient light, and certain video frames in the monitoring video are reasonably smaller than the precision threshold value, so that the video frames are not rejected; otherwise, rejecting is carried out.
In addition, the number of the action behaviors is identified according to the effective working state identification model, effective workload is generated, and the score is corrected according to the effective workload. For example, if the effective workload is less than the average workload in the ordinary times, the score is subtracted on the basis of the score; if the effective workload is more than the average workload in normal times, the scoring is performed on the basis of the scores. Meanwhile, face detection is carried out on the monitoring video by adopting a face recognition algorithm to obtain face information, the identity of the staff is determined according to the face information, and the action behavior which can be recognized by the effective working state recognition model is associated with the identity of the staff, namely, the action behavior is corresponding to the staff, so that the follow-up verification and verification are facilitated.
Example 3
The only difference from embodiment 2 is that in this embodiment, the punch data of the staff needs to be collected in advance. When collecting the card punching data of the staff, gradually collecting the movement amount, the face state and the background of the card punching of the staff in unit time. Specifically, when an employee walks to the card punching terminal, first, the amount of movement of the employee per unit time is collected. In this embodiment, the movement amount of the employee in unit time is the movement step number of the employee in unit time, and the movement data on the WeChat can be input by the employee himself and then calculated. For example, from home to unit time of 20 minutes, the number of steps of exercise is 3600 steps, and the amount of exercise per unit time of the employee is 180 steps/minute. If the movement amount of the unit time exceeds a preset threshold, for example, the preset threshold is 150 steps/minute, staff is reminded of reasonably planning the travel time in a voice mode. Then, the face state of the employee is collected. In this embodiment, the facial image of the employee is acquired by the camera, and the facial state of the employee, that is, the facial emotion expression, is identified by FACEREADER software. If the employee's facial status is negative, such as "sad", "anger", the employee's body temperature is detected by an infrared thermometer: if the body temperature of the staff exceeds a temperature threshold, for example, the temperature threshold is 37.2 ℃, the staff is reminded to wear the mask, and body temperature data is reported. And finally, collecting a background picture of staff card punching. In this embodiment, the camera is used to collect photos of the employee with a preset number of frames, say 10 photos, when the employee punches a card, and the action recognition algorithm is used to determine whether the employee's body is stationary or moving. If the staff's body is moving, it indicates that the staff may be busy, and the staff is prompted to punch cards in advance by voice; conversely, if the employee's body is stationary, indicating that the employee may not be particularly busy, the voice prompts the employee to advance the card.
The foregoing is merely an embodiment of the present application, and a specific structure and characteristics of common knowledge in the art, which are well known in the scheme, are not described herein, so that a person of ordinary skill in the art knows all the prior art in the application date or before the priority date, can know all the prior art in the field, and has the capability of applying the conventional experimental means before the date, and a person of ordinary skill in the art can complete and implement the present embodiment in combination with his own capability in the light of the present application, and some typical known structures or known methods should not be an obstacle for a person of ordinary skill in the art to implement the present application. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present application, and these should also be considered as the scope of the present application, which does not affect the effect of the implementation of the present application and the utility of the patent. The protection scope of the present application is subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (7)
1. The performance assessment system based on the blockchain is characterized by comprising:
the input unit is used for uploading the work record of each employee, wherein the work record comprises card punching data and monitoring video;
the statistics unit is used for enabling staff to mutually check the work records in the blockchain, scoring the work records to obtain a scoring table, and storing the scoring table in the blockchain; the method is also used for acquiring trace information from the work records of staff, calculating effective working time according to the trace information and storing the effective working time into a blockchain;
the judging unit is used for comparing the effective working time length with the preset rated working time length to obtain a comparison result, and judging whether the scores in the scoring table are effective or not according to the comparison result: if the effective working time length is longer than or equal to the preset rated working time length, indicating that the workload is oversaturated and saturated, and judging that the score is effective; if the effective working time is smaller than the preset rated working time, indicating that the workload is unsaturated, and judging that the score is invalid;
the correction unit is used for extracting effective scores in the scoring table and corresponding monitoring videos, analyzing reasonable working time length, namely high-quality working time length, of the working states in the monitoring videos meeting preset requirements, correcting the effective scores according to the reasonable working time length and the effective working time length, and obtaining a corrected scoring table;
and the output unit is used for outputting the corrected scoring table in a visual mode.
2. The blockchain-based performance assessment system of claim 1, further comprising a storage unit for storing a standard image of an employee's action behavior when the work status meets a preset requirement; the correction unit is also used for acquiring a standard image, training the standard image to obtain an effective working state identification model, identifying action behaviors in the monitoring video through the effective working state identification model, and eliminating action behaviors in the monitoring video which cannot be identified by the effective working state identification model.
3. The blockchain-based performance assessment system of claim 2, wherein the correction unit further pre-eliminates video frames in the monitored video that are not within the identification range, the video frames that are not within the identification range being video frames that have no action.
4. The blockchain-based performance assessment system of claim 3, wherein the correction unit is further configured to preset an accuracy threshold and filter out video frames in the monitored video having an accuracy value less than the accuracy threshold by the active operational state identification model.
5. The blockchain-based performance assessment system of claim 4, wherein the correction unit is further configured to extract conversational speech from the surveillance video, identify content of the conversational speech; and eliminating the video frames smaller than the precision threshold value in the monitoring video when the content of the dialogue voice can not reasonably explain the video frames smaller than the precision threshold value in the monitoring video.
6. The blockchain-based performance assessment system of claim 5, wherein the correction unit is further configured to generate an effective workload based on the number of action actions identified by the effective work state identification model and correct the score based on the effective workload.
7. The blockchain-based performance assessment system of claim 6, wherein the correction unit is further configured to perform face detection on the surveillance video using a face recognition algorithm to obtain face information; and determining the employee identity according to the face information, and associating the action behavior which can be identified by the effective working state identification model with the employee identity.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108492031A (en) * | 2018-03-23 | 2018-09-04 | 重庆金窝窝网络科技有限公司 | Job evaluation method and device based on block chain |
| CN109190864A (en) * | 2018-06-21 | 2019-01-11 | 广东电网有限责任公司信息中心 | Performance appraisal method, apparatus, computer equipment and storage medium |
| CN110222920A (en) * | 2019-04-19 | 2019-09-10 | 平安科技（深圳）有限公司 | Performance data storage method, device, equipment and readable storage medium storing program for executing |
| CN111275395A (en) * | 2020-01-19 | 2020-06-12 | 重庆科技学院 | Decentralized enterprise performance assessment method based on block chain |
| CN111275323A (en) * | 2020-01-19 | 2020-06-12 | 重庆科技学院 | Performance distribution system based on confidence nonlinear weighting integration operator |
| CN111311067A (en) * | 2020-01-19 | 2020-06-19 | 重庆科技学院 | Performance distribution DApp system based on confidence level weighted integration operator |
| CN111461538A (en) * | 2020-03-31 | 2020-07-28 | 青岛网信信息科技有限公司 | Performance management system based on big data analysis |
| CN111489136A (en) * | 2020-04-16 | 2020-08-04 | 于洁 | Enterprise salary management system |
| CN111885360A (en) * | 2020-07-31 | 2020-11-03 | 贵州东冠科技有限公司 | Intelligent monitoring system for block chain production workshop |
| CN112990646A (en) * | 2020-12-28 | 2021-06-18 | 贵州东冠科技有限公司 | Performance assessment and evaluation method for workers |
-
2020
- 2020-12-28 CN CN202011582374.0A patent/CN112507972B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108492031A (en) * | 2018-03-23 | 2018-09-04 | 重庆金窝窝网络科技有限公司 | Job evaluation method and device based on block chain |
| CN109190864A (en) * | 2018-06-21 | 2019-01-11 | 广东电网有限责任公司信息中心 | Performance appraisal method, apparatus, computer equipment and storage medium |
| CN110222920A (en) * | 2019-04-19 | 2019-09-10 | 平安科技（深圳）有限公司 | Performance data storage method, device, equipment and readable storage medium storing program for executing |
| CN111275395A (en) * | 2020-01-19 | 2020-06-12 | 重庆科技学院 | Decentralized enterprise performance assessment method based on block chain |
| CN111275323A (en) * | 2020-01-19 | 2020-06-12 | 重庆科技学院 | Performance distribution system based on confidence nonlinear weighting integration operator |
| CN111311067A (en) * | 2020-01-19 | 2020-06-19 | 重庆科技学院 | Performance distribution DApp system based on confidence level weighted integration operator |
| CN111461538A (en) * | 2020-03-31 | 2020-07-28 | 青岛网信信息科技有限公司 | Performance management system based on big data analysis |
| CN111489136A (en) * | 2020-04-16 | 2020-08-04 | 于洁 | Enterprise salary management system |
| CN111885360A (en) * | 2020-07-31 | 2020-11-03 | 贵州东冠科技有限公司 | Intelligent monitoring system for block chain production workshop |
| CN112990646A (en) * | 2020-12-28 | 2021-06-18 | 贵州东冠科技有限公司 | Performance assessment and evaluation method for workers |
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