CN111429090A - Salary management system and method based on artificial intelligence platform - Google Patents
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Abstract
A salary management system based on an artificial intelligence platform comprises a data entry module, a data processing module and a data processing module, wherein the data entry module is configured to be connected with the data processing module and is used for data entry of collection; the data storage module is connected with the data processing module through configuration and is used for storing the data acquired by the data entry module; the data display module is configured to be connected with the data processing module and is used for displaying the data processed by the data processing module; the data communication module is connected with the data processing module through configuration, and data transmission through the internet network is achieved. By the method, an enterprise salary management system can be designed simply and quickly, superior leaders can master various working schedules in time, the working results of employees on the same day are linked with monthly salary, and the purposes of pressing work and getting more labor are really achieved.
Description
Technical Field
The invention belongs to the management range of an enterprise salary system, and particularly relates to a salary management system and method based on an artificial intelligence platform.
Background
With the continuous development of socioeconomic, a large number of small and medium-sized enterprises such as the bamboo shoots in the spring after rain are increased in quantity, the gap of the demand for talents is continuously enlarged, the recruitment of talents of the enterprise is not timely kept pace with the development of the enterprise, so that the situation that one person plays multiple posts simultaneously appears inside the enterprise, one part of people have heavy daily work and one part of people have leisure work, but the finally obtained monthly wages are not enough, and the phenomenon easily causes the mental imbalance of staff and is not beneficial to the development of the enterprise. Aiming at the problems, a plurality of enterprise management layers try to participate in enterprise performance assessment training, a training teacher designs a salary system for assessment, the final result is that the design process is too tedious, meanwhile, a large amount of manual assistance is needed, errors are easy to occur due to the complex calculation method, and the application effect is reluctant. Therefore, how to quickly find a simple and quick salary design management method can meet the development requirements of enterprises, and becomes a problem to be solved urgently by a plurality of small and medium-sized micro enterprises.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a compensation management system and method based on an artificial intelligence platform.
The technical scheme adopted by the invention is as follows: a salary management system based on an artificial intelligence platform comprises a data entry module, a data processing module and a data processing module, wherein the data entry module is configured to be connected with the data processing module and is used for data entry of collection; the data storage module is connected with the data processing module through configuration and is used for storing the data acquired by the data entry module; the data display module is configured to be connected with the data processing module and is used for displaying the data processed by the data processing module; the data communication module is connected with the data processing module through configuration, and data transmission through the internet network is achieved.
Preferably, the data entry module comprises an image acquisition unit for realizing image acquisition, a position acquisition unit for acquiring a real-time position, and a work entry unit for entering work content.
Preferably, the image acquisition unit adopts an image sensor to realize video data acquisition; the position acquisition unit adopts a satellite positioning component to realize the real-time acquisition of position information; the work input unit adopts manual input or non-contact information automatic input to realize the manual and automatic input of work contents.
Preferably, the data processing module comprises an image processing unit for analyzing and processing video images, and a trajectory analysis unit for processing data acquired by the satellite positioning component; and the performance analysis and processing unit is used for analyzing and processing the data collected by the work entry unit.
Preferably, the image processing unit adopts an image recognition method based on a neural network and an image recognition method based on a wavelet moment.
Preferably, the performance analysis processing unit may process the manually input information by using a machine learning method.
Preferably, the data storage module can adopt a mechanical hard disk and a solid state hard disk.
Preferably, the data display module can adopt L ED, O L ED and liquid crystal display screen.
Preferably, the data processing module is a processor with data analysis and calculation capability, such as a central processing unit of a computer and a single chip microcomputer.
A salary management method based on an artificial intelligence platform is disclosed, wherein the salary comprises a monthly basic salary and a monthly performance salary, and the monthly basic salary is determined according to the working capacity of staff; the monthly performance wages are obtained by quantifying according to the working conditions of the employees.
Preferably, the compensation is assessed monthly, and monthly compensation is the sum of monthly basic wages and monthly performance wages.
Preferably, the basic salaries comprise post salaries, academic subsidies, post service subsidies, technical grade, special skills, age subsidies, field subsidies, security awards and driver subsidies, wherein the post salaries are determined by referring to local salary standards and internal work posts of an enterprise to which the post salaries belong; the study calendar subsidy is determined according to the study calendar level of the staff; the post subsidy is determined according to the job condition of the staff in the enterprise; the technical grade is determined according to a professional registration certificate obtained in a national unified examination; the special skill is determined according to a special post specified by laws and regulations and acquired certificates; the working age subsidy, the field subsidy, the safety award and the driver subsidy determine the specific subsidy amount according to the enterprise condition; the basic payroll obtained according to the above specification is the quantization standard base B1.
Preferably, the ratio of the basic salary is P1, the ratio of the performance salary is P2, and P1+ P2=100%, the ratio values P1 and P2 can be adjusted according to the working property, and the performance salary calculated according to the relationship is the quantized performance base number B2.
Preferably, the monthly performance wages are determined according to the monthly performance scores of the employees, the monthly performance scores are determined according to the daily performance scores, and the daily performance scores are determined according to the scoring quantification standard items.
Preferably, the scoring quantization standard items include work integrity, work completion rate, work quality, work reporting and compliance, and 5S management, and the sum of the scores of the scoring quantization standard items is S.
Preferably, the performance analysis processing unit analyzes and learns the manually input work content, work quantity, time spent and work integrity, work completion rate, work quality, work report and compliance and 5S management score by adopting a machine learning method, and automatically scores and stores the work integrity, work completion rate, work quality, work report and compliance and 5S management according to the work content, quantity and time spent parameters.
Preferably, the total monthly performance score can be calculated according to formula 1:
Wherein D is the sum of the monthly performance scores;
i is a day of a month;
n is the actual number of days on attendance;
vi is the daily performance score;
according to formula 2, an average daily performance score can be calculated:
Wherein E is the average daily performance score;
d is the sum of the monthly performance scores;
n is the actual number of days on attendance;
the monthly performance wages are calculated according to a formula 3:
Wherein G1 is monthly performance wages;
n is the actual number of days on attendance;
n is the number of working days after deducting the legal rest day in the month;
e is the average daily performance score;
s is the sum of the addition of scoring standard terms;
b2 is a quantitative performance base
The month basic payroll is calculated according to formula 4:
Wherein G2 is the monthly basic payroll;
n is the actual number of days on attendance;
n is the number of working days after deducting the legal rest day in the month;
b1 is quantization standard base
According to the formula 3 and the formula 4, the monthly compensation can be calculated by using the formula 5:
Wherein G is monthly compensation;
g1 is month basis payroll;
g2 is monthly performance wages.
Compared with the prior art, the invention has the beneficial effects that: the invention designs the salary management system of the whole enterprise according to the working capacity of the staff, introduces a performance assessment mechanism on the basis, and calculates the salary of the staff in the working and monthly manners of daily workload filling and scoring.
Drawings
FIG. 1 is a block diagram of a schematic structure according to the present invention;
FIG. 2 is a block diagram of a compensation construct according to the present invention;
FIG. 3 is a depiction of the composition of a basic payroll according to the present invention;
FIG. 4 is a block diagram of a salary formulation flow according to the present invention;
FIG. 5 is a flow chart of monthly compensation calculation according to the present invention;
FIG. 6 is a work content entry box format according to the present invention;
FIG. 7 is a job content scoring box for scoring a normal job according to the present invention;
FIG. 8 is a work content scoring box for scoring shift overtime work in accordance with the present invention;
FIG. 9 illustrates a video capture mode according to the present invention;
fig. 10 is a display effect of a trajectory analysis unit according to the present invention after processing collected data.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 depicts a schematic block diagram of the present application. A compensation management system based on an artificial intelligence platform comprises a data entry module (1) which is configured to be connected with a data processing module (2) and used for entering collected data; the data storage module (4) is configured to be connected with the data processing module (2) and is used for storing the data acquired by the data entry module (1); the data display module (5) is configured to be connected with the data processing module (2) and is used for displaying the data processed by the data processing module (2); the data communication module (3) is connected with the data processing module (2) through configuration, and is communicated with a mobile phone terminal (301), a tablet personal computer (303) and other handheld terminals or PC terminals (302) and other equipment through an internet network, and data are stored and processed under the control of the data processing module (2).
The data entry module (1) comprises an image acquisition unit (105) for realizing image acquisition, a position acquisition unit (106) for acquiring a real-time position and a work entry unit (107) for entering work contents. The image acquisition unit (105) can be realized by adopting an image sensor (101), such as a digital camera, a network camera, an infrared camera and the like, data are acquired in real time in a video mode, acquired videos are stored in the data storage module (4) under the control of the data processing module (2), the data storage module can adopt a mechanical hard disk (401) and a solid hard disk (402), and a large amount of data storage is realized under the control of the data processing module (2).
The position acquisition unit (106) is realized by adopting a satellite positioning component (102), the satellite positioning component (102) can adopt a Beidou satellite positioning system, a GPS satellite positioning system of the United states, a Galileo satellite positioning system of Europe and a Glonass satellite positioning system of Russia in China to determine the specific positions of employees in real time, including the positions of the employees working in the company and the positions of the employees on business trip, and the specific positions are stored in the data storage module (4) in real time under the control of the data processing module (2).
The work entry unit (107) can be entered by adopting a manual input mode (103), for example, specific work content data is transmitted to the data processing module (2) through a keyboard, or can be entered by adopting a non-contact information automatic entry mode (104), for example, the acquired data can be transmitted to the data processing module (2) by adopting a bar code scanning gun, an RFID reader-writer, a two-dimensional code and other modes, and the entered data information is stored in the data storage module (4) under the control of the data processing module (2).
The data processing module (2) comprises an image processing unit (201) for analyzing and processing video images, a track analyzing unit (202) for processing data collected by the satellite positioning component (102), and a performance analyzing and processing unit (203) for analyzing and processing data collected by the work entry unit (107). The data processing module (2) is provided with a processor with data analysis and operation capacity, such as a central processing unit of a computer, a singlechip and the like. The image processing unit (201) analyzes and processes the acquired video image by using an image recognition method based on a neural network and an image recognition method based on a wavelet moment, and is used for determining the specific action condition of the employee on a normal working post, such as the state that the employee is in a normal working state, a vague state, a state of playing a mobile phone and the like, so that the real and effective working time of the employee can be obtained by the way; the track analysis unit (202) draws an action track according to the position and the positioning data acquired by the satellite positioning component (102) and grasps the action track of the staff in the working time; the performance analysis processing unit (203) performs different processing according to different data modes acquired by the work entry unit (107), for data entered by adopting a manual input (103) mode, the performance analysis processing unit (203) performs learning processing on the manual input (103) mode by adopting a machine learning mode, automatically identifies the input work content, for the data acquired by adopting a non-contact information automatic entry (104) mode by adopting the work entry unit (107), and the performance analysis processing unit (203) directly processes the data according to a set data format.
The data display module is connected with the data processing module (2) through the configuration (5), and can display the data processed by the data processing module (2) in real time by adopting L ED (501), O L ED (502) and a liquid crystal display screen (503).
FIG. 2 is a block diagram of a compensation (6) management method based on an artificial intelligence platform according to the present invention, wherein the compensation (6) comprises a monthly basic pay (7) and a monthly performance pay (8), and the monthly basic pay (7) is determined according to the working capacity of the employee; the monthly performance wages (8) are obtained by quantification according to the working conditions of the employees, the salaries (6) are assessed monthly, and the monthly salaries (6) are the sum of the monthly basic wages (7) and the monthly performance wages (8).
Fig. 3 shows the composition of a basic payroll (11) according to the present invention, said basic payroll (7) comprises post payroll (12), academic subsidy (13), post subsidy (14), technical grade (15), special skill (16), age subsidy (17), field subsidy (18), security award (19), driver subsidy (10), wherein said post payroll (12) is determined by reference to local payroll standards and the internal job post of the enterprise to which it belongs; the study calendar subsidy (13) is determined according to the study calendar level of the staff; the post subsidy (14) is determined according to the job condition of the employee in the enterprise; the technical grade (15) is determined according to a professional registration certificate obtained in a national unified examination; the special skill (16) is determined according to a special post specified by laws and regulations and acquired certificates; the working age subsidy (17), the field subsidy (18), the safety award (19) and the driver subsidy (10) determine the specific subsidy amount according to the enterprise condition; the basic payroll (11) obtained according to the above specification is the quantization standard base B1.
Fig. 4 is a block diagram of a salary (6) making process according to the present invention, wherein the base quantification standard base B1 in step 20 can be obtained according to the content of the basic salary (11) in fig. 3, and the ratio of the basic salary to the salary is P1, the ratio of the performance salary to the salary is P2, and P1+ P2=100%, the ratio values P1 and P2 can be adjusted according to the working property, the month standard quantification salary in step 21 can be obtained according to the above content, and the quantification performance base B2 in step 22 can be obtained according to the ratio of the month standard quantification salary to the performance salary; because the positions set inside the company are inconsistent, the proportion P1 and P2 of the basic salary and the performance salary are different, so that the quantitative standard base number B1 and the quantitative performance base number B2 of the employees at different positions inside the company in the step 23 can be obtained, and the establishment of the salary system of the company in the step 24 can be realized according to the quantitative standard base number B1 and the quantitative performance base number B2.
FIG. 5 is a flow chart of a month compensation (6) calculation according to the present invention. After the establishment of the company compensation system is completed, the monthly basic wage (7) and the monthly performance wage (8) of each employee are determined, and performance scoring is started on the basis of the monthly basic wage and the monthly performance wage. According to the step 30, each employee is required to record the specific content, quantity and required time of work in detail every day, the work content responsible for the employees at different posts is listed according to the mode, all the responsible work is operated once as much as possible, then the work conditions of different employees are scored and recorded according to the work integrity, work completion rate, work quality, work report and compliance and 5S management scoring items described in the step 31 by manpower, samples required by machine learning are provided, according to the step 32, a performance analysis processing unit (203) performs machine learning on the work content, quantity, time and scoring condition input by the staff (103) manually, according to the step 33, the work content, quantity and time input by the staff are scored by the manual and performance analysis processing unit at the same time, and after the machine learning is trained to a certain degree, the performance analysis processing unit directly executes the performance scoring without manually scoring, and the automatic processing is completely realized; when calculating the payroll monthly, the monthly performance score total can be calculated according to equation 1, as per step 34:
Wherein D is the sum of the monthly performance scores;
i is a day of a month;
n is the actual number of days on attendance;
vi is the daily performance score;
according to formula 2, an average daily performance score can be calculated:
Wherein E is the average daily performance score;
d is the sum of the monthly performance scores;
n is the actual number of days on attendance;
the monthly performance wages are calculated according to a formula 3:
Wherein G1 is monthly performance wage (8);
n is the actual number of days on attendance;
n is the number of working days after deducting the legal rest day in the month;
e is the average daily performance score;
s is the sum of the addition of scoring standard terms;
b2 is a quantitative performance base
The month basic payroll is calculated according to formula 4:
Wherein G2 is month basis payroll (7);
n is the actual number of days on attendance;
n is the number of working days after deducting the legal rest day in the month;
b1 is quantization standard base
According to the formula 3 and the formula 4, the monthly compensation can be calculated by using the formula 5:
Wherein G is monthly remuneration (6);
g1 is month basic payroll (7);
g2 is monthly performance wage (8).
According to the calculation process, the monthly compensation of each employee can be calculated.
Now, taking an enterprise as an example, the specific implementation method of the patent will be explained in detail. The implementation method is divided into two parts, wherein the first part is a description of the establishment process of an enterprise compensation (6) system; the second part is a performance assessment scoring process description.
First, explaining the establishment of a first part of enterprise compensation (6) system. The method comprises the steps that a company administrator combines local minimum wage requirements to design the position levels corresponding to different positions of the company, and as shown in a table-position wage (12), the table is designed by referring to a local minimum monthly wage standard of 1220 yuan, and each working position is divided into four levels. The contents in the specific table can be added and confirmed according to the actual situation.
Watch one post wage (unit: yuan)
The standard of the academic calendar subsidy (13) of the company can be determined according to the second table. The contents in the specific table can be added and confirmed according to the actual situation.
Watch two learning calendar patch (Unit: Yuan)
| Serial number | Categories | Professional to post | The profession is inconsistent with the post | Remarks for |
| 1 | Special section | 100 | 50 | |
| 2 | This section | 200 | 100 | |
| 3 | Master's soldier | 400 | 300 |
From table three, a job subsidy for the business may be determined (14). The post subsidy (14) refers to a subsidy given by a person who plays a certain organization and management role in actual work. The contents in the specific table can be added and confirmed according to the actual situation.
Watch three-post subsidy (Unit: yuan)
| Serial number | Rank of job | Amount of subsidy | Remarks for |
| 1 | Manager level | 800 | |
| 2 | Department's level | 500 | |
| 3 | Class group level | 150 |
From table four, a technical grade (15) subsidy for the enterprise can be determined. The technical level (15) subsidy refers to a professional registration certificate obtained through a national unified examination such as a human society department, a financial department, a housing construction department, and the like. The contents in the specific table can be added and confirmed according to the actual situation.
Watch four technical grade subsidy (Unit: yuan)
According to the fifth table, the subsidies of the special skills (16) of the enterprises can be known. The special skill (16) refers to the post which must be certified according to the national regulation. The contents in the specific table can be added and confirmed according to the actual situation.
Watch five special skill subsidy (Unit: yuan)
| Serial number | Skill of skill | Amount of subsidy | Remarks for |
| 1 | Welder | 100 | |
| 2 | Electrician's electric engineering | 50 | |
| 3 | Climbing operation | 50 | |
| 4 | Marine homework certificate | 50 |
The payroll (17) can be executed according to the relevant regulations of the company, and is temporarily determined as 15 yuan/year, and the payroll increases 15 yuan from the next year after the whole year of work.
The field subsidy (18) mainly aims at field service personnel, is temporarily determined to be 5 yuan/day, and can be adjusted according to field conditions.
The safety award (19) is determined according to different positions, wherein the temporary determination is that the field service personnel is 50 yuan/month, and the other position personnel are 30 yuan/month.
The driver patch (10) can be applied according to the relevant fixed monthly or daily basis of the company, and the patching is performed according to the 200-element monthly standard.
According to the post wages (12), the study calendar subsidies (13), the post affairs subsidies (14), the technical grade subsidies (15), the special skill subsidies (16), the age subsidies (17), the field subsidies (18), the safety prizes (19), the driver subsidies (10) and the like, the minimum month basic wages (11) of different post grades can be determined, and the specific conditions are shown in the table six.
Minimum monthly basic payroll
According to the method, the proportion of the basic salary (11) to the salaries (6) is P1, the proportion of the performance salary to the salaries (6) is P2, and P1+ P2=100%, the proportion of the basic salary (11) of the field service part, the production part and the comprehensive part is P1 of 40%, the proportion of the performance salary is 60%, the proportion of the basic salary (11) of the research and development part is P1 of 30%, the proportion of the performance salary is 70%, the monthly salaries (6) of each department can be obtained according to the standard, the specific salaries (6) of each post are not listed one by one, each department only selects one post to describe, and the specific salaries (6) of the company are shown in Table seven.
Pacific corporation compensation standard
Thus, company compensation (6) system is established.
And then a concrete implementation method of a performance assessment scoring process is explained.
Under the control of the data processing module (2), the specific work content, the number, the used time and the certification material (such as video, pictures and the like) after work of each employee are input by a manual input (103) through a work input unit (107), and the input data is stored in a data storage module (4), wherein the specific work content input frame format is as shown in fig. 6.
In fig. 6, the work content entry frame is entered by the work entry unit (107), and its the work entry unit (107) can be equipment such as PC terminal (302), cell phone terminal (301), panel computer (303), and here the work entry frame is manual input (103) mode, if adopt non-contact mode with information entry time, its work entry unit (107) can adopt automation equipment such as bar code rifle, RFID reader-writer, two-dimensional code scanner, through data communication module (3) after the internet protocol will be scanned data process configuration, under the control of data processing module (2), directly with data storage in data storage module (4) piece. The work content entry box shown in fig. 6 can realize the addition and deletion of work content, and simultaneously supports the uploading of attachments, such as pictures, words, excels, PPT and other documents, so that the working conditions and the time of each employee can be clearly understood.
Fig. 7 and 8 are scoring contents for scoring a specific submitted work content condition, which are divided into a normal work and an overtime work, the normal work being a work performed within a normal working time according to the national law; the overtime work is taken as the company working beyond the abnormal working time; when the work content is actually submitted, the work content is submitted according to specific conditions, and the difference between the work content and the specific conditions is whether the 5S management score item is included. The scoring is carried out on the work content which is input by each employee through the work input unit (107), and the specific meaning of each scoring content is as follows: the working integrity degree is the authenticity of the working content which is input by the appraisal staff in a manual or non-contact mode, the rating standard set here is 20 points, namely the full rating is 20 points, the specific rating detail can be determined according to the specific requirement, for example, the office content is cleaned hygienically, the hygienic processing time is about 10 minutes according to the normal cleaning speed, when the working content is actually filled, part of staff fills in 1 hour, the filling time does not accord with the actual condition, and the working integrity degree is judged according to the value; the work completion rate is set to 30 points for the work completion situation scheduled every day; the working quality is the effect condition of the completion of the work determined according to the submitted work content and the uploaded work condition accessories, and the set score is 30 points; the work reporting and compliance mainly checks the active reporting of the work engaged by the staff and the compliance of the staff to the superior leadership, and the set score is 10 points; the 5S management score mainly scores the execution condition of 5S, and the set score is 10; the sum S of all the scoring items is 100 points, the total score value S can be adjusted according to needs, such as 200 points, 300 points, 1000 points and the like, and all the scoring items are set according to the company emphasis point according to the condition of the total score value. In the initial stage of scoring execution, the specific work of scoring is responsible for by the superior leader, scoring is respectively performed from the five aspects aiming at different work contents, the specific scoring item can be adjusted and perfected according to the actual situation, and the key point is that the work contents of various different situations are accumulated. By the aid of the grading mode, the upper-minded mind of the staff can be effectively stimulated, working efficiency and working quality are improved, the mental state of the staff for working is improved, and production efficiency is improved.
After the execution is carried out for a period of time according to the graphs of fig. 6 and fig. 7, the performance analysis processing unit performs machine learning on the work content, time, the attachment content reported by the staff and the scoring condition of the superior leader in a machine learning manner, establishes templates for various work contents and stores the templates in a data storage module (4), such as a mechanical hard disk (401), a solid state hard disk (402) and the like, and on the basis of the data, the performance analysis processing unit (203) automatically scores the content added by the staff at the later stage according to the learned data templates and the scoring condition, so that the labor force can be effectively liberated.
According to the mode, the performance analysis processing unit (203) automatically scores and records the work content submitted by the staff every day, and when the next month comes, the salaries of the staff in the last month can be calculated according to the scores, the quantitative standard base number B1, the quantitative performance base number B2 and the attendance days.
The site installation service post is taken as an example here to explain the specific salary calculation process. Assuming that the number of days in a month is 30 days, the number of normal attendance days after the rest day specified by the division law is 26 days, and the calculation is carried out according to the site installation service post level I, the quantization standard cardinality B1 is 1720 yuan, the quantization performance cardinality B2 is 2580 yuan, and the following calculation is carried out according to several different attendance days:
(1) normal attendance for 26 days without overtime
Without overtime, assume that the daily job rating (i.e., performance score) is scored 90 points.
The monthly performance score is calculated according to equation 1,
Wherein D is the sum of the monthly performance scores;
i is a day of a month;
n is the actual number of days on attendance;
vi is the daily performance score;
according to the above-mentioned rule, the actual number of attendance n is 26 days, the performance score Vi per day is 90 points, and the sum D of the monthly performance scores is 2340 points.
According to formula 2, an average daily performance score can be calculated:
Wherein E is the average daily performance score;
d is the sum of the monthly performance scores;
n is the actual number of days on attendance;
according to the formula 1, the calculation result is that the sum D of the monthly performance scores is 2340 points, the actual attendance days n are 26 days, and the calculation result is that the average daily performance score E is 90 points.
From equation 3, the monthly performance wage (8) can be calculated:
Wherein G1 is monthly performance wage (8);
n is the actual number of days on attendance;
n is the number of working days after deducting the legal rest day in the month;
e is the average daily performance score;
s is the sum of the addition of scoring standard terms;
b2 is a quantitative performance base
The actual attendance days N are 26 days, the number of the working days N after the rest day is divided is 26 days, the average daily performance score E is 90 points, the sum S of the sum of the scoring standard items is 100 points, the quantitative performance base number B2 is 2580 yuan, and the monthly performance wage G1 can be calculated to be 2322 yuan.
From equation 4, the monthly basic payroll (7) can be calculated:
Wherein G2 is month basis payroll (7);
n is the actual number of days on attendance;
n is the number of working days after deducting the legal rest day in the month;
b1 is quantization standard base
Wherein the actual attendance number N is 26, the number N of the working days after the rest day is divided is 26, the quantization standard base number B1 is 1720 yuan, and the available month basic payroll G2 is 1720 yuan.
According to formula 3 and formula 4, monthly remuneration (6) can be calculated using formula 5:
Wherein G is monthly remuneration (6);
g1 is month basic payroll (7);
g2 is monthly performance wage (8).
The monthly compensation G of the employee can be calculated to be 4042 yuan according to the formula 5.
(2) The normal attendance for 26 days with overtime
Since the work can be overtime in addition to normal work, the performance score for one day ranges from 0 to 190 points, assuming that the score for each day is 120 points.
The monthly performance score is calculated according to equation 1,
Wherein D is the sum of the monthly performance scores;
i is a day of a month;
n is the actual number of days on attendance;
vi is the daily performance score;
according to the above-mentioned fixed, the actual number of attendance N is 26 days, the performance score N per day is 120 points, and the sum of the monthly performance scores D is 3120 points.
According to formula 2, an average daily performance score can be calculated:
Wherein E is the average daily performance score;
d is the sum of the monthly performance scores;
n is the actual number of days on attendance;
and (3) calculating according to the formula 1, wherein the sum D of the monthly performance scores is 3120 points, and the actual attendance days are 26 days, so that the daily performance average score E is 120 points.
From equation 3, the monthly performance wage (8) can be calculated:
Wherein G1 is monthly performance wage (8);
n is the actual number of days on attendance;
n is the number of working days after deducting the legal rest day in the month;
e is the average daily performance score;
s is the sum of the addition of scoring standard terms;
b2 is a quantitative performance base
The actual attendance days N are 26 days, the number of the working days N after the rest day is divided is 26 days, the average daily performance score E is 120 points, the sum S of the sum of the scoring standard items is 100 points, the quantitative performance base number B2 is 2580 yuan, and the monthly performance wage G1 can be calculated to be 3096 yuan.
From equation 4, the monthly basic payroll (7) can be calculated:
Wherein G2 is month basis payroll (7);
n is the actual number of days on attendance;
n is the number of working days after deducting the legal rest day in the month;
b1 is quantization standard base
Wherein the actual attendance number N is 26, the number N of the working days after the rest day is divided is 26, the quantization standard base number B1 is 1720 yuan, and the available month basic payroll G2 is 1720 yuan.
According to formula 3 and formula 4, monthly remuneration (6) can be calculated using formula 5:
Wherein G is monthly remuneration (6);
g1 is month basic payroll (7);
g2 is monthly performance wage (8).
The monthly compensation G of the employee can be calculated to be 4816 Yuan according to the formula 5.
Under the condition of normal attendance, the difference of the labor amount can be clearly contrasted in a mode of overtime or not, the final labor reward is also different, the autonomy of the staff can be greatly stimulated through the mode, the working attitude of the staff is changed from 'will work' into 'will work', and the purpose of 'more labor and more availability' is really achieved.
Fig. 9 shows a video capture mode according to the present invention. In fig. 9, the working condition of the employee is obtained in real time through the image acquisition unit (105), under the control of the data processing module (2), a video stream is obtained in real time through the image acquisition unit (105) of the data entry module (1), the image acquisition unit (105) can be implemented by using an image sensor (101), such as a webcam, an infrared camera, and the like, the image analysis unit (201) processes the image by using an image recognition method based on a neural network and an image recognition method based on a wavelet moment, judges the current working state of the employee, such as the states of normal work, cell phone stealing, not in a working post, and the like, and stores the image of the video in the data storage module (4) for later browsing and querying under the control of the data processing module (2). Through the image acquisition and automatic identification functions, the auxiliary work of the performance analysis processing unit (203) is realized, the working state of the staff in normal working time is further verified, and reliable data support is provided for the working integrity scoring.
The specific positions of employees in a company can be acquired in real time through the data entry module (1), the position acquisition unit (106) of the data entry module (1) acquires specific position information through the satellite positioning component (102), the satellite positioning component (102) can adopt a Beidou satellite positioning system in China, a GPS satellite positioning system in America, a Galileo satellite positioning system in Europe and a Glonass satellite positioning system in Russia, the satellite positioning component (102) can be an independent device and can also be integrated into other devices such as portable devices such as a mobile phone terminal (301) and a tablet computer (303), the satellite positioning component (102) sends position information to the data communication module (3) through an interer network at regular time, the positioning information is transmitted to the trajectory analysis unit (202) under the control of the data processing module (2), the trajectory analysis unit (202) stores the position information of employees in the employee track processing unit (202) after processing the employee track processing unit (202) processes acquired data, the acquired by the employee track analysis unit (202) can store the real-time, the acquired data is stored in a working track management unit (3) through a hard disk module (3), the working data processing module (3) and can display data of a working space (3) and a working space (e.g. a working space management module (3) which can display a working space which displays a working data management module (3) and a working space (3) when a working space which can be displayed by a working space (3) and a working space which displays a working space (3) through a working space (3) and a working space (e.g. a working space) through a working space which can be displayed by a working space (e.g. a working space (3) and a working space) through a working space (e.g. a working space) and a working space (e.g. a working space which can be displayed by a working space.
Claims (17)
1. A salary management system based on an artificial intelligence platform is characterized by comprising a data entry module, a data processing module and a data processing module, wherein the data entry module is configured to be connected with the data processing module and is used for entering collected data; the data storage module is connected with the data processing module through configuration and is used for storing the data acquired by the data entry module; the data display module is configured to be connected with the data processing module and is used for displaying the data processed by the data processing module; the data communication module is connected with the data processing module through configuration, and data transmission through the internet network is achieved.
2. The system of claim 1, wherein the data entry module comprises an image acquisition unit for image acquisition, a position acquisition unit for acquiring a real-time position, and a work entry unit for work content entry.
3. The system of claim 2, wherein the image capturing unit employs an image sensor to capture video data; the position acquisition unit adopts a satellite positioning component to realize the real-time acquisition of position information; the work input unit adopts manual input or non-contact information automatic input to realize the manual and automatic input of work contents.
4. The system of claim 1, wherein the data processing module comprises an image processing unit for analyzing and processing video images, a trajectory analysis unit for processing data collected by the satellite positioning component; and the performance analysis and processing unit is used for analyzing and processing the data collected by the work entry unit.
5. The system of claim 1, wherein the image processing unit employs an image recognition method based on a neural network and an image recognition method based on wavelet moment.
6. The system of claim 1, wherein the performance analysis processing unit is configured to process the manually inputted information by machine learning.
7. The system of claim 1, wherein the data storage module is a hard disk drive, a solid state drive.
8. The system of claim 1, wherein the data display module is selected from the group consisting of L ED, O L ED, and LCD.
9. The system of claim 1, wherein the data processing module is a processor with data analysis and computation capability, such as a computer cpu or a single chip microcomputer.
10. A salary management method based on an artificial intelligence platform is characterized in that the salary comprises a month basic payroll and a month performance payroll, and the month basic payroll is determined according to the working capacity of staff; the monthly performance wages are obtained by quantifying according to the working conditions of the employees.
11. The method of claim 10, wherein the compensation is assessed monthly, and monthly compensation is the sum of monthly basic salaries and monthly performance salaries.
12. The salary management method based on artificial intelligence platform as claimed in any of claims 10 or 11, wherein said basic salary includes post salary, academic subsidy, post subsidy, technical grade, special skills, age subsidy, field subsidy, security prize, driver subsidy, wherein said post salary is determined with reference to local salary standard and the affiliated enterprise internal work post; the study calendar subsidy is determined according to the study calendar level of the staff; the post subsidy is determined according to the job condition of the staff in the enterprise; the technical grade is determined according to a professional registration certificate obtained in a national unified examination; the special skill is determined according to a special post specified by laws and regulations and acquired certificates; the working age subsidy, the field subsidy, the safety award and the driver subsidy determine the specific subsidy amount according to the enterprise condition; the basic payroll obtained according to the above specification is the quantization standard base B1.
13. The method of claim 11, wherein the basic payroll is P1, the performance payroll is P2, P1+ P2 is 100%, the ratio values P1 and P2 can be adjusted according to the working property, and the performance payroll calculated according to the relationship is the quantitative performance base B2.
14. The method of any one of claims 10 or 11, wherein the monthly performance wages are determined based on a staff monthly performance score, the monthly performance score is determined based on a daily performance score, and the daily performance score is determined based on a scoring quantification criterion.
15. The compensation management method of claim 14, wherein the scoring quantization standard items include job integrity, job completion rate, job quality, job report and compliance, and 5S management, and the sum of the scores of the scoring quantization standard items is S.
16. The salary management method based on the artificial intelligence platform as claimed in claim 15, wherein the performance analysis processing unit analyzes and learns the manually inputted work content, work quantity, time spent, and work integrity after scoring, work completion rate, work quality, work report and compliance, and 5S management score by using a machine learning method, and automatically scores and stores the work integrity, work completion rate, work quality, work report and compliance, and 5S management according to the work content, quantity, and time spent parameters.
17. The method of claim 11, wherein the total monthly performance score is calculated according to equation 1:
wherein D is the sum of the monthly performance scores;
i is a day of a month;
n is the actual number of days on attendance;
vi is the daily performance score;
according to formula 2, an average daily performance score can be calculated:
wherein E is the average daily performance score;
d is the sum of the monthly performance scores;
n is the actual number of days on attendance;
the monthly performance wages are calculated according to a formula 3:
wherein G1 is monthly performance wages;
n is the actual number of days on attendance;
n is the number of working days after deducting the legal rest day in the month;
e is the average daily performance score;
s is the sum of the addition of scoring standard terms;
b2 is a quantitative performance base
The month basic payroll is calculated according to formula 4:
wherein G2 is the monthly basic payroll;
n is the actual number of days on attendance;
n is the number of working days after deducting the legal rest day in the month;
b1 is quantization standard base
According to the formula 3 and the formula 4, the monthly compensation can be calculated by using the formula 5:
G-G1 + G2 formula 5
Wherein G is monthly compensation;
g1 is month basis payroll;
g2 is monthly performance wages.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113220799A (en) * | 2021-05-13 | 2021-08-06 | 北京德风新征程科技有限公司 | Big data early warning management system |
| CN113435852A (en) * | 2021-07-03 | 2021-09-24 | 深圳市南睿信息科技有限公司 | Salary management system |
| CN113516448A (en) * | 2021-06-22 | 2021-10-19 | 汪靖源 | Efficient performance compensation management method |
| CN114037288A (en) * | 2021-11-11 | 2022-02-11 | 青岛民航凯亚系统集成有限公司 | Performance adjusting system and method based on machine learning |
| CN114971589A (en) * | 2022-07-12 | 2022-08-30 | 广东龙眼数字科技有限公司 | Salary information processing method, electronic equipment and storage medium |
| CN117764448A (en) * | 2023-12-25 | 2024-03-26 | 苏州优鲜信网络生活服务科技有限公司 | Property personnel performance assessment method and system based on visual work result |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105069550A (en) * | 2015-07-16 | 2015-11-18 | 沈阳化工大学 | Human resource management system |
| CN105719123A (en) * | 2016-01-15 | 2016-06-29 | 成都金万泰科技有限公司 | Performance management method and system within enterprise |
| CN206711145U (en) * | 2017-05-17 | 2017-12-05 | 山东省计算中心（国家超级计算济南中心） | A kind of Government Projects Performance Management System |
| CN109035472A (en) * | 2018-08-23 | 2018-12-18 | 福建汇川物联网技术科技股份有限公司 | A kind of building site field operation personnel management methods and device |
| CN110443526A (en) * | 2019-08-27 | 2019-11-12 | 上海见慧企业发展有限公司 | A kind of management platform system of working in dispersion team |
-
2020
- 2020-03-19 CN CN202010194290.3A patent/CN111429090A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105069550A (en) * | 2015-07-16 | 2015-11-18 | 沈阳化工大学 | Human resource management system |
| CN105719123A (en) * | 2016-01-15 | 2016-06-29 | 成都金万泰科技有限公司 | Performance management method and system within enterprise |
| CN206711145U (en) * | 2017-05-17 | 2017-12-05 | 山东省计算中心（国家超级计算济南中心） | A kind of Government Projects Performance Management System |
| CN109035472A (en) * | 2018-08-23 | 2018-12-18 | 福建汇川物联网技术科技股份有限公司 | A kind of building site field operation personnel management methods and device |
| CN110443526A (en) * | 2019-08-27 | 2019-11-12 | 上海见慧企业发展有限公司 | A kind of management platform system of working in dispersion team |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113220799A (en) * | 2021-05-13 | 2021-08-06 | 北京德风新征程科技有限公司 | Big data early warning management system |
| CN113516448A (en) * | 2021-06-22 | 2021-10-19 | 汪靖源 | Efficient performance compensation management method |
| CN113516448B (en) * | 2021-06-22 | 2023-03-21 | 汪靖源 | Performance salary management method |
| CN113435852A (en) * | 2021-07-03 | 2021-09-24 | 深圳市南睿信息科技有限公司 | Salary management system |
| CN114037288A (en) * | 2021-11-11 | 2022-02-11 | 青岛民航凯亚系统集成有限公司 | Performance adjusting system and method based on machine learning |
| CN114971589A (en) * | 2022-07-12 | 2022-08-30 | 广东龙眼数字科技有限公司 | Salary information processing method, electronic equipment and storage medium |
| CN117764448A (en) * | 2023-12-25 | 2024-03-26 | 苏州优鲜信网络生活服务科技有限公司 | Property personnel performance assessment method and system based on visual work result |
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