CN114066590A - Salary data processing method, device and equipment based on big data and storage medium - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence, and provides a salary data processing method, device, equipment and storage medium based on big data. The method comprises the following steps: receiving a salary calculation request and a salary task to be calculated, and acquiring attribute information and sales data of salary personnel to be calculated from a preset database according to task identification; when the sales data is judged to be larger than the preset value, matching each salary item in the task with a preset salary calculation rule set according to the attribute information to obtain a salary calculation rule of each salary item; performing disassembly calculation on the salary calculation rule of each salary item to obtain a result value of each salary item; and filling the result value of each compensation item into a preset compensation template and summing to obtain a target compensation table, and feeding back the target compensation table to the user. The invention also relates to the technical field of block chains, and the sales data and the result value can be stored in a node of a block chain.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a salary data processing method, device, equipment and storage medium based on big data.
Background
When an enterprise has different sales channels and the contribution coefficient and the value range of a salesman in each channel are different, in the past compensation calculation, financial staff of the enterprise manually records the sales data of each salesman into an excel document, sets a relatively complex formula in the excel document to assist in calculation, inputs the contribution coefficient and the value range of each salesman into the formula in the calculation process, calculates the result of each project one by one, and sums the results of each project to obtain the wage of each salesman. The calculation mode of the manual formula can only realize the wage calculation of one salesman at a time, and cannot realize the wage calculation of all the salesmens in a large batch and obtain results at the same time, so that the work of financial staff is repeatedly input, and sometimes the calculation results have errors.
Disclosure of Invention
In view of the above, the present invention provides a salary data processing method, apparatus, device and storage medium based on big data, and aims to solve the technical problems in the prior art that only payroll calculation of one salesman can be realized each time, and that payroll calculation of all salesmen in large batch cannot be realized while obtaining results.
In order to achieve the above object, the present invention provides a compensation data processing method based on big data, which comprises:
receiving a salary calculation request sent by a user and a salary task to be calculated, and acquiring attribute information of salary personnel to be calculated and sales data in a preset time period from a preset database according to an identifier of the task;
when the sales data is judged to be larger than a preset value, matching each salary item in the task with a preset salary calculation rule set according to the attribute information to obtain a salary calculation rule of each salary item;
performing a disassembling calculation on the compensation calculation rule of each compensation item to obtain a result value of each compensation item;
and filling the result value of each compensation item into a preset compensation template and summing to obtain a target compensation table, and feeding back the target compensation table to the user.
Preferably, before the task of receiving the compensation calculation request issued by the user and compensation to be calculated, the method further includes:
setting a salary formula of each salary item and a plurality of factors of the salary formula according to the attribute information of the salary personnel to be billed;
establishing a relation table of the attribute information of the salary staff to be calculated and the plurality of factors, and setting the value range of each factor according to the relation table;
combining the compensation formula and each factor into a plurality of compensation calculation rules, and constructing the preset compensation calculation rule set according to the compensation calculation rules.
Preferably, the attribute information includes area information, organization information, and post information, and the matching of each compensation item in the task with a preset compensation calculation rule set according to the attribute information to obtain the compensation calculation rule of each compensation item includes:
a1, when any salary item in the task is successfully matched with the preset salary calculation rule set, matching the region information with the salary item rule set to obtain a region calculation rule set, wherein the preset salary calculation rule set comprises at least one salary item rule set;
a2, matching the mechanism information with the region calculation rule set to obtain a mechanism calculation rule set;
a3, matching the position information with the institution calculation rule set to obtain the compensation calculation rule of the compensation item.
A4, repeating A1-A3 until each compensation item in the task matches the preset compensation calculation rule set to obtain compensation calculation rules for all compensation items.
Preferably, the performing a disassembly calculation on the compensation calculation rule of each compensation item to obtain a result value of each compensation item includes:
b1, performing decomposition on a compensation calculation rule corresponding to any compensation item to obtain a compensation formula of the compensation calculation rule and each factor of the compensation formula;
b2, obtaining a plurality of initial values by carrying out value taking on each factor according to a preset value taking method, inputting the initial values into the compensation formula for calculation, and obtaining the result value of the compensation item;
b3, repeat B1-B2 until the result values of all compensation items in the task are obtained.
Preferably, the obtaining a plurality of initial values by dereferencing each of the factors according to a preset dereferencing method includes:
inquiring the relation table according to the attribute information of the salary staff to be calculated to obtain the value range of each factor;
and assigning each factor as an initial value according to a preset value method and the sales data from the value range.
Preferably, the step of filling the result value of each compensation item into a preset compensation template and summing the result values to obtain a target compensation table includes:
and filling the result value of each compensation calculation rule into the corresponding compensation item position in the preset compensation template according to the positioning rule of the preset compensation template to obtain the target verification compensation table.
Preferably, the feeding back the target check compensation table to the user includes:
and receiving feedback information sent by the user according to the target compensation table, and storing the target compensation table into the preset database according to the feedback information.
To achieve the above object, the present invention further provides a compensation data processing apparatus, including:
the acquisition module is used for receiving a salary calculation request sent by a user and a salary task to be calculated, and acquiring attribute information of salary personnel to be calculated and sales data in a preset time period from a preset database according to an identifier of the task;
the matching module is used for matching each salary item in the task with a preset salary calculation rule set according to the attribute information when the sales data is judged to be larger than a preset value, so as to obtain a salary calculation rule of each salary item;
the calculation module is used for performing disassembly calculation on the salary calculation rule of each salary item to obtain a result value of each salary item;
and the feedback module is used for filling the result value of each compensation item into a preset compensation template and summing the result values to obtain a target compensation table, and feeding the target compensation table back to the user.
In order to achieve the above object, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a program executable by the at least one processor to enable the at least one processor to perform a big data based compensation data processing method according to any one of claims 1 to 7.
To achieve the above object, the present invention further provides a computer readable storage medium storing a compensation data processing program, which when executed by a processor, implements the steps of the big data based compensation data processing method according to any one of claims 1 to 7.
The invention provides a salary data processing method, a device, equipment and a storage medium based on big data, wherein a salary calculation rule of each salary item is set according to regional information, mechanism information and job information of each salary person to be calculated, a preset salary calculation rule set is constructed in advance according to a plurality of salary calculation rules, and the salary calculation of a plurality of salary persons to be calculated can be processed simultaneously by utilizing the preset salary calculation rule set. When a task of salary to be calculated is received, each salary item in the task can be matched with a preset salary calculation rule set in sequence according to the region information, the mechanism information and the job information of a plurality of salary personnel to be calculated to obtain a salary calculation rule of each salary item, a formula can be automatically set in the calculation process, the salary calculation rule of each salary item is disassembled and calculated to obtain a result value of each salary item, a target salary checking table is generated according to all the result values, salary of a large number of salary personnel to be calculated can be calculated simultaneously, calculation results are obtained, and the calculation results are more accurate.
Drawings
FIG. 1 is a schematic flow chart diagram illustrating a big data based compensation data processing method according to a preferred embodiment of the present invention;
FIG. 2 is a block diagram of a compensation data processing apparatus according to a preferred embodiment of the present invention;
FIG. 3 is a diagram of an electronic device according to a preferred embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The invention provides a salary data processing method based on big data. Referring to fig. 1, a method flow chart of an embodiment of a big data-based compensation data processing method according to the invention is shown. The method may be performed by an electronic device, which may be implemented by software and/or hardware. The salary data processing method based on big data comprises the following steps:
step S10: receiving a salary calculation request sent by a user and a salary task to be calculated, and acquiring attribute information of salary personnel to be calculated and sales data in a preset time period from a preset database according to an identifier of the task;
in this embodiment, the task to be compensated includes, but is not limited to, an identification of more than one compensation staff (for example, task a to be compensated includes tasks to be compensated of all salespeople of section a, and obtains attribute information and sales data of each salespeople of section a according to the identification of all salespeople of section a), a plurality of compensation items and a plurality of time periods for calculation (for example, 2021.9.1-2021.9.30 whole month 9). The identifier of the salary waiting staff can refer to a staff number or an identity card ID of the salary waiting staff, and the salary items comprise floating salary, monthly prize drawing, quarterly prize drawing and other salary items.
The user may be a financial staff or manager of the enterprise. The candidate may be a salesman inside the enterprise, or may be a third-party salesman (e.g., a part-time salesman or an outsourcing sales team) who cooperates with the enterprise, because the regional information, the institution information, and the job information in the attribute information of each salesman are different, the value range, the basic salary, and the like of the contribution coefficient sold by the salesman are different.
The attribute information of the staff to be paid comprises the staff number of the salesman, regional information, institution information, job information and the like. The sales data refers to determined sales data that is approved by a manager of the enterprise according to the employee number of the salesperson, and the determined sales data is set according to the actual business scene of the enterprise, for example, according to a contract or a policy that the salesperson has signed, when the contract or the policy that has signed receives all sales money, the contract or the policy that has signed is added to the sales data of the salesperson.
The preset database refers to a preset database built in an enterprise, and the preset database stores personal information (such as names, employee numbers, ID cards and the like) of staff to be paid, regional information, organization information, job information and other information and sales performance data. And acquiring attribute information of the salary staff to be calculated and sales data in a preset time period (for example, 2021.9.1-2021.9.30 in the whole 9 months) from a preset database according to the staff number of the salary staff to be calculated.
Step S20: when the sales data is judged to be larger than a preset value, matching each salary item in the task with a preset salary calculation rule set according to the attribute information to obtain a salary calculation rule of each salary item;
in this embodiment, the sales data of the salary to be calculated is obtained from the preset database according to the employee number of the salary to be calculated, and when the sales data is determined to be less than or equal to the preset value (for example, the preset value is set to zero), it is described that the sales data of the salary to be calculated is not stored in the preset database, the salary calculation of the salary to be calculated is completed, and the data of the salary to be calculated without the sales data is stored in a zero sales file in the preset database, so that the calculation task is reduced, the running time of the CPU is saved, and the financial staff can directly issue basic salaries to the salary to be calculated without the sales data. The non-sales data may be that the candidate has no sales data of the determined contract or policy recorded in the predetermined database for a predetermined period of time, for example, the contract or policy signed by the candidate has no sales money received.
When the sales data is judged to be larger than the preset value, the sales data of the salary to be calculated is stored in the preset database, and the sales data are respectively matched with a preset salary calculation rule set according to the region information, the mechanism information and the job information of the salary to be calculated in sequence to obtain a salary calculation rule corresponding to each salary item in the salary to be calculated task.
When the salary items in the task are matched with the preset salary calculation rule set, the network node which can adopt the Rete algorithm can filter the salary calculation rules matched with the salary items.
For example, when the floating salary (salary item) of the salesman B in the task of salary calculation is successfully matched with the preset salary calculation rule set, the type node of the Rete algorithm matches the regional information of the salesman B with the salary item rule set (salary item rule set of floating salary) to obtain the regional calculation rule set of the salesman B, the selectnode matches the mechanism information of the salesman B with the regional calculation rule set to obtain the mechanism calculation rule set of the salesman B, and the alphanode matches the job information of the salesman B with the mechanism calculation rule set to obtain the salary calculation rule of the floating salary of the salesman B.
In this embodiment, the predefined payroll rule sets, the payroll item rule sets, the region calculation rule sets, the institution calculation rule sets, and the payroll calculation rules are in a progressive relationship from large to small (for example, the predefined payroll rule sets include all payroll item rule sets, and the payroll item rule sets include all region calculation rule sets).
In one embodiment, before the task of receiving the salary calculation request issued by the user and the salary to be calculated, the method further comprises:
setting a salary formula of each salary item and a plurality of factors of the salary formula according to the attribute information of the salary personnel to be billed;
establishing a relation table of the attribute information of the salary staff to be calculated and the plurality of factors, and setting the value range of each factor according to the relation table;
combining the compensation formula and each factor into a plurality of compensation calculation rules, and constructing the preset compensation calculation rule set according to the compensation calculation rules.
Setting factors of a salary formula and a salary formula of each salary item according to regional information, organization information and job information of salary personnel to be calculated and calculation requirements of salary tasks to be calculated by financial personnel of enterprises, combining the factors of different salary formulas and salary formulas into different salary calculation rules, and constructing a preset salary calculation rule set according to the different salary calculation rules.
For example, a salesman H in a vehicle-agency channel (organization information) in wuhan district, financial staff of an enterprise sets a salary formula and a factor for monthly prize drawing of the salesman H according to attribute information of the salesman H, wherein the salary formula is as follows:
Q＝X+a+Y/100
wherein, Q is the result value of monthly prize drawing, X is the real premium of monthly prize drawing, a is the coefficient of drawing the prize of the staff to be billed, Y is the monthly KPI of the staff to be billed, the real premium, the coefficient of drawing the prize, monthly KPI are the factors in the salary formula of monthly prize drawing, combine the salary formula and these three factors into a salary calculation rule of monthly prize drawing (salary item), the salary calculation rule is suitable for the salesman with the same attribute information as salesman H.
In one embodiment, the attribute information includes region information, organization information, and job information, and the matching of each compensation item in the task with a preset compensation calculation rule set according to the attribute information to obtain the compensation calculation rule of each compensation item includes:
a1, when any salary item in the task is successfully matched with the preset salary calculation rule set, matching the region information with the salary item rule set to obtain a region calculation rule set, wherein the preset salary calculation rule set comprises at least one salary item rule set;
a2, matching the mechanism information with the region calculation rule set to obtain a mechanism calculation rule set;
a3, matching the position information with the institution calculation rule set to obtain the compensation calculation rule of the compensation item.
A4, repeating A1-A3 until each compensation item in the task matches the preset compensation calculation rule set to obtain compensation calculation rules for all compensation items.
| Staff name | Employee number | Regional information | Organization information | Post information |
| Salesman B | 2299 | Shanghai area | Channel for life insurance | Senior manager |
Watch 1
For example, the preset salary calculation rule set includes salary item rule sets such as monthly lottery, floating cloud salary and quarterly lottery, according to table one, the salary calculation rule of monthly lottery (salary item) of salesman B is matched with the regional information of salesman B, the regional information of salesman B is matched with the salary item rule set (monthly lottery) to obtain a preset salary calculation rule set in shanghai district, the life insurance (mechanism information) in the attribute information of salesman B is matched with the regional calculation rule set in shanghai district to obtain an mechanism calculation rule set of life insurance, and finally the senior manager (job information) in the attribute information of salesman B is matched with the mechanism calculation rule set to obtain the salary calculation rule of monthly lottery of salesman B.
Step S30: performing a disassembling calculation on the compensation calculation rule of each compensation item to obtain a result value of each compensation item;
specifically, step S30 includes:
b1, performing decomposition on a compensation calculation rule corresponding to any compensation item to obtain a compensation formula of the compensation calculation rule and each factor of the compensation formula;
b2, obtaining a plurality of initial values by carrying out value taking on each factor according to a preset value taking method, inputting the initial values into the compensation formula for calculation, and obtaining the result value of the compensation item;
b3, repeat B1-B2 until the result values of all compensation items in the task are obtained.
In an embodiment, the taking values of each of the factors according to a preset value taking method to obtain a plurality of initial values includes:
inquiring the relation table according to the attribute information of the salary staff to be calculated to obtain the value range of each factor;
and assigning each factor as an initial value according to a preset value method and the sales data from the value range.
For example, matching the salary calculation rule to obtain monthly prize drawing of the salary staff to be billed, and performing decomposition on the salary calculation rule to obtain a salary formula and a factor, wherein the salary formula is as follows:
Q＝X+a+Y/100
wherein Q is a result value of monthly prize drawing, X is an actual premium of monthly prize drawing, a is a prize drawing coefficient of the staff to be paid, Y is a monthly KPI of the staff to be paid, the actual premium, the prize drawing coefficient and the monthly KPI are factors in a pay formula of monthly prize drawing, each factor is assigned as an initial value from a value range value according to a preset value taking method, the preset value taking method can be a Policy Account value taking method (get Policy Account method), the value ranges of each factor are different, some factors are fixed values, some factors are range values, for example, the actual premium factor is fixed, the actual premium is 10000 yuan, and the actual premium value is 10000 yuan when the calculation rule of the pay is calculated. The prize drawing coefficient factor and the policy quantity factor are range values, if the policy quantity is 10-20 parts, the prize drawing coefficient value is 0.1 when the salary calculation rule is calculated, and if the policy quantity is 20-30 parts, the prize drawing coefficient value is 0.2 when the salary calculation rule is calculated.
| Staff name | Employee number | Regional information | Organization information | Post information |
| Salesman H | 1266 | Wuhan district | Vehicle channel | Middle manager |
| Regional patch | Real premium | Prize drawing systemNumber of | Monthly KPI | Number of policy |
| 0 | 100000 | 1％ | 90 minutes | 10 |
Watch two
For example, according to the information in table two, it is known that the real premium paid by the salesman H in the month is 10 ten thousand yuan, the KPI score is 90 minutes, the policy number is 10 shares, and the prize drawing coefficient takes a value of 0.1, and the compensation formula for monthly prize drawing by inputting these three factors is: 100000 x 0.1 x 90/100 ═ 9000 yuan, obtain salesman's H monthly prize-drawing amount 9000 yuan, according to different regional information, organization information, job information, the factor quantity of the salary formula of each salesman monthly prize-drawing is different (for example, Shanghai district has higher life cost relative to Wuhan district, salesman in Shanghai district has regional subsidy factor, salesman in Wuhan district has no regional subsidy factor), and the value corresponding to each factor is different.
Step S40: and filling the result value of each compensation item into a preset compensation template and summing to obtain a target compensation table, and feeding back the target compensation table to the user.
In this embodiment, a result value of the salary calculation rule is calculated according to the salary formula, the result value is filled into a corresponding salary item position in the preset salary template to obtain a target salary checking table, and the target salary checking table is fed back to the financial staff. The preset compensation template may be an EXCEL form or a WORD form, for example, a floating salary cell is in the form, and then the blank cell below the floating salary cell is the amount number for filling the floating salary. The target compensation check table may be a table that fills each result value into a blank cell corresponding to compensation items in a preset compensation template, and sums all the result values to obtain a target value to generate an actual payroll table of the compensation staff, that is, the actual income of each compensation item of the compensation staff is displayed as money, and the total sum of each compensation item is also actual payroll. The task of the salary to be calculated is input into a preset salary calculation rule set, and the result values of a plurality of salary personnel to be calculated can be obtained.
Table three is a target checking salary table
For example, according to the third table, the result value of the floating salary item of the salesman B is calculated to be 2000 yuan and the result value of the monthly prize-raising salary item is calculated to be 10000 yuan, the result value of the floating salary (2000 yuan) is filled into the blank cells of the floating salary in the preset salary template, the result value of the monthly prize-raising (10000 yuan) is filled into the blank cells of the monthly prize-raising in the preset salary template, the result values in each salary item are summed to obtain the actual salary of the salesman B, and the target check salary table of the salesman B is obtained.
In one embodiment, the filling and summing the result value of each compensation item into a preset compensation template to obtain a target checking compensation table includes:
and filling the result value of each compensation calculation rule into the corresponding compensation item position in the preset compensation template according to the positioning rule of the preset compensation template to obtain the target verification compensation table.
The positioning rule of the preset compensation template can be a rule preset by financial staff or IT staff of an enterprise according to the functions of searching and positioning of an EXCEL table or a WORD table.
For example, the result value of the floating salary item of salesman B is calculated to be 2000 yuan, and according to the preset rule, the result value of the floating salary (2000 yuan) is automatically filled into the cell of the floating salary in the preset salary template.
Further, summing the result values in each cell in the preset compensation template to obtain a target value of the task of compensation to be calculated, and filling the target value to a corresponding position in the preset compensation template according to the positioning rule.
In one embodiment, the feeding back the target check compensation table to the user comprises:
and receiving feedback information sent by the user according to the target compensation table, and storing the target compensation table into the preset database according to the feedback information.
And feeding back the target salary checking table to the financial staff, sending confirmation information to the server after the financial staff checks the amount of each salary item in the target salary checking table, storing the target salary checking table into a completed calculation salary file of a preset database after the server receives the confirmation information fed back by the financial staff, and confirming and issuing salaries to the salary staff to be counted according to the target salary checking table after the salary checking table is checked to be correct.
Referring to FIG. 2, a functional block diagram of a compensation data processing apparatus 100 according to the present invention is shown.
The compensation data processing apparatus 100 of the present invention can be installed in an electronic device. According to the implemented functions, the compensation data processing device 100 may include an obtaining module 110, a matching module 120, a calculating module 130 and a feedback module 140. A module according to the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In this embodiment, the functions of the modules/units are as follows:
the obtaining module 110 is configured to receive a salary calculation request sent by a user and a task of salary to be calculated, and obtain attribute information of a salary staff to be calculated and sales data in a preset time period from a preset database according to an identifier of the task.
A matching module 120, configured to match each compensation item in the task with a preset compensation calculation rule set according to the attribute information when it is determined that the sales data is greater than a preset value, so as to obtain a compensation calculation rule for each compensation item.
The calculation module 130: and the system is used for performing decomposition calculation on the compensation calculation rule of each compensation item to obtain a result value of each compensation item.
The feedback module 140 is configured to fill the result value of each compensation item into a preset compensation template and sum the result values to obtain a target compensation table, and feed the target compensation table back to the user.
In one embodiment, before the task of receiving the salary calculation request issued by the user and the salary to be calculated, the method further comprises:
setting a salary formula of each salary item and a plurality of factors of the salary formula according to the attribute information of the salary personnel to be billed;
establishing a relation table of the attribute information of the salary staff to be calculated and the plurality of factors, and setting the value range of each factor according to the relation table;
combining the compensation formula and each factor into a plurality of compensation calculation rules, and constructing the preset compensation calculation rule set according to the compensation calculation rules.
In one embodiment, the attribute information includes region information, organization information, and job information, and the matching of each compensation item in the task with a preset compensation calculation rule set according to the attribute information to obtain the compensation calculation rule of each compensation item includes:
a1, when any salary item in the task is successfully matched with the preset salary calculation rule set, matching the region information with the salary item rule set to obtain a region calculation rule set, wherein the preset salary calculation rule set comprises at least one salary item rule set;
a2, matching the mechanism information with the region calculation rule set to obtain a mechanism calculation rule set;
a3, matching the position information with the institution calculation rule set to obtain the compensation calculation rule of the compensation item.
A4, repeating A1-A3 until each compensation item in the task matches the preset compensation calculation rule set to obtain compensation calculation rules for all compensation items.
In one embodiment, the performing a decomposition calculation on the compensation calculation rule of each compensation item to obtain a result value of each compensation item includes:
b1, performing decomposition on a compensation calculation rule corresponding to any compensation item to obtain a compensation formula of the compensation calculation rule and each factor of the compensation formula;
b2, obtaining a plurality of initial values by carrying out value taking on each factor according to a preset value taking method, inputting the initial values into the compensation formula for calculation, and obtaining the result value of the compensation item;
b3, repeat B1-B2 until the result values of all compensation items in the task are obtained.
In an embodiment, the taking values of each of the factors according to a preset value taking method to obtain a plurality of initial values includes:
inquiring the relation table according to the attribute information of the salary staff to be calculated to obtain the value range of each factor;
and assigning each factor as an initial value according to a preset value method and the sales data from the value range.
In one embodiment, the filling and summing the result value of each compensation item into a preset compensation template to obtain a target checking compensation table includes:
and filling the result value of each compensation calculation rule into the corresponding compensation item position in the preset compensation template according to the positioning rule of the preset compensation template to obtain the target verification compensation table.
In one embodiment, the feeding back the target check compensation table to the user comprises:
and receiving feedback information sent by the user according to the target compensation table, and storing the target compensation table into the preset database according to the feedback information.
Fig. 3 is a schematic diagram of an electronic device 1 according to a preferred embodiment of the invention.
The electronic device 1 includes but is not limited to: memory 11, processor 12, display 13, and network interface 14. The electronic device 1 is connected to a network through a network interface 14 to obtain raw data. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System for Mobile communications (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, or a communication network.
The memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 11 may be an internal storage unit of the electronic device 1, such as a hard disk or a memory of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like equipped with the electronic device 1. Of course, the memory 11 may also comprise both an internal memory unit and an external memory device of the electronic device 1. In this embodiment, the memory 11 is generally used for storing an operating system installed in the electronic device 1 and various application software, such as program codes of the compensation data processing program 10. Further, the memory 11 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 12 is typically used for controlling the overall operation of the electronic device 1, such as performing data interaction or communication related control and processing. In this embodiment, the processor 12 is configured to run the program code stored in the memory 11 or process data, such as the program code of the compensation data processing program 10.
The display 13 may be referred to as a display screen or display unit. In some embodiments, the display 13 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an Organic Light-Emitting Diode (OLED) touch screen, or the like. The display 13 is used for displaying information processed in the electronic device 1 and for displaying a visual work interface, e.g. displaying the results of data statistics.
The network interface 14 may optionally comprise a standard wired interface, a wireless interface (e.g. WI-FI interface), the network interface 14 typically being used for establishing a communication connection between the electronic device 1 and other electronic devices.
FIG. 3 shows only the electronic device 1 having the components 11-14 and the compensation data handler 10, but it is to be understood that not all of the shown components are required and that more or fewer components may alternatively be implemented.
Optionally, the electronic device 1 may further comprise a user interface, the user interface may comprise a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface may further comprise a standard wired interface and a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an Organic Light-Emitting Diode (OLED) touch screen, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
The electronic device 1 may further include a Radio Frequency (RF) circuit, a sensor, an audio circuit, and the like, which are not described in detail herein.
In the above embodiment, the processor 12 executing the compensation data processing program 10 stored in the memory 11 may implement the following steps:
receiving a salary calculation request sent by a user and a salary task to be calculated, and acquiring attribute information of salary personnel to be calculated and sales data in a preset time period from a preset database according to an identifier of the task;
when the sales data is judged to be larger than a preset value, matching each salary item in the task with a preset salary calculation rule set according to the attribute information to obtain a salary calculation rule of each salary item;
performing a disassembling calculation on the compensation calculation rule of each compensation item to obtain a result value of each compensation item;
and filling the result value of each compensation item into a preset compensation template and summing to obtain a target compensation table, and feeding back the target compensation table to the user.
The storage device may be the memory 11 of the electronic device 1, or may be another storage device communicatively connected to the electronic device 1.
For a detailed description of the above steps, please refer to the above description of fig. 2 regarding a functional block diagram of an embodiment of the compensation data processing apparatus 100 and fig. 1 regarding a flowchart of an embodiment of a compensation data processing method based on big data.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium may be non-volatile or volatile. The computer readable storage medium may be any one or any combination of hard disks, multimedia cards, SD cards, flash memory cards, SMCs, Read Only Memories (ROMs), Erasable Programmable Read Only Memories (EPROMs), portable compact disc read only memories (CD-ROMs), USB memories, etc. The computer readable storage medium includes a stored data area and a stored program area, the stored data area stores data created according to the use of the blockchain node, the stored program area stores a compensation data processing program 10, and when executed by the processor, the compensation data processing program 10 implements the following operations:
receiving a salary calculation request sent by a user and a salary task to be calculated, and acquiring attribute information of salary personnel to be calculated and sales data in a preset time period from a preset database according to an identifier of the task;
when the sales data is judged to be larger than a preset value, matching each salary item in the task with a preset salary calculation rule set according to the attribute information to obtain a salary calculation rule of each salary item;
performing a disassembling calculation on the compensation calculation rule of each compensation item to obtain a result value of each compensation item;
and filling the result value of each compensation item into a preset compensation template and summing to obtain a target compensation table, and feeding back the target compensation table to the user.
The embodiment of the computer readable storage medium of the present invention is substantially the same as the embodiment of the method for processing salary data based on big data, and will not be described herein again.
In another embodiment, in order to further ensure the privacy and security of all the presented data, all the data may be stored in a node of a block chain. Such as sales data, result values, which may all be stored in block link points.
It should be noted that the blockchain in the present invention is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. The block chain (Blockchain), which is essentially a decentralized preset database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, which is used for verifying the validity (anti-counterfeiting) of the information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
It should be noted that the above-mentioned numbers of the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention essentially or contributing to the prior art can be embodied in the form of a software product, which is stored in a medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (such as a mobile phone, a computer, an electronic device, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A big data-based compensation data processing method, the method comprising:
receiving a salary calculation request sent by a user and a salary task to be calculated, and acquiring attribute information of salary personnel to be calculated and sales data in a preset time period from a preset database according to an identifier of the task;
when the sales data is judged to be larger than a preset value, matching each salary item in the task with a preset salary calculation rule set according to the attribute information to obtain a salary calculation rule of each salary item;
performing a disassembling calculation on the compensation calculation rule of each compensation item to obtain a result value of each compensation item;
and filling the result value of each compensation item into a preset compensation template and summing to obtain a target compensation table, and feeding back the target compensation table to the user.
2. The big-data based compensation data processing method of claim 1, wherein before receiving a compensation calculation request from a user and a compensation task to be calculated, the method further comprises:
setting a salary formula of each salary item and a plurality of factors of the salary formula according to the attribute information of the salary personnel to be billed;
establishing a relation table of the attribute information of the salary staff to be calculated and the plurality of factors, and setting the value range of each factor according to the relation table;
combining the compensation formula and each factor into a plurality of compensation calculation rules, and constructing the preset compensation calculation rule set according to the compensation calculation rules.
3. The big data-based salary data processing method of claim 1, wherein the attribute information includes regional information, institution information and post information, and the matching each salary item in the task with a preset salary calculation rule set according to the attribute information to obtain a salary calculation rule for each salary item includes:
a1, when any salary item in the task is successfully matched with the preset salary calculation rule set, matching the region information with the salary item rule set to obtain a region calculation rule set, wherein the preset salary calculation rule set comprises at least one salary item rule set;
a2, matching the mechanism information with the region calculation rule set to obtain a mechanism calculation rule set;
a3, matching the position information with the institution calculation rule set to obtain the compensation calculation rule of the compensation item.
A4, repeating A1-A3 until each compensation item in the task matches the preset compensation calculation rule set to obtain compensation calculation rules for all compensation items.
4. The big data based compensation data processing method of claim 1 or 2, wherein the performing a decomposition calculation on the compensation calculation rule of each compensation item to obtain a result value of each compensation item comprises:
b1, performing decomposition on a compensation calculation rule corresponding to any compensation item to obtain a compensation formula of the compensation calculation rule and each factor of the compensation formula;
b2, obtaining a plurality of initial values by carrying out value taking on each factor according to a preset value taking method, inputting the initial values into the compensation formula for calculation, and obtaining the result value of the compensation item;
b3, repeat B1-B2 until the result values of all compensation items in the task are obtained.
5. The big data-based compensation data processing method of claim 4, wherein the evaluating each of the factors according to a predetermined evaluation method to obtain a plurality of initial values comprises:
inquiring the relation table according to the attribute information of the salary staff to be calculated to obtain the value range of each factor;
and assigning each factor as an initial value according to a preset value method and the sales data from the value range.
6. The big data based compensation data processing method of claim 1, wherein the filling and summing the result value of each compensation item into a predetermined compensation template to obtain a target checking compensation table comprises:
and filling the result value of each compensation calculation rule into the corresponding compensation item position in the preset compensation template according to the positioning rule of the preset compensation template to obtain the target verification compensation table.
7. The big data based compensation data processing method of claim 1, wherein the feeding back the target check compensation table to the user comprises:
and receiving feedback information sent by the user according to the target compensation table, and storing the target compensation table into the preset database according to the feedback information.
8. A big data based compensation data processing apparatus, the apparatus comprising:
the acquisition module is used for receiving a salary calculation request sent by a user and a salary task to be calculated, and acquiring attribute information of salary personnel to be calculated and sales data in a preset time period from a preset database according to an identifier of the task;
the matching module is used for matching each salary item in the task with a preset salary calculation rule set according to the attribute information when the sales data is judged to be larger than a preset value, so as to obtain a salary calculation rule of each salary item;
the calculation module is used for performing disassembly calculation on the salary calculation rule of each salary item to obtain a result value of each salary item;
and the feedback module is used for filling the result value of each compensation item into a preset compensation template and summing the result values to obtain a target compensation table, and feeding the target compensation table back to the user.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a program executable by the at least one processor to enable the at least one processor to perform a big data based compensation data processing method according to any one of claims 1 to 7.
10. A computer readable storage medium storing a compensation data processing program which when executed by a processor implements the steps of the big data based compensation data processing method according to any one of claims 1 to 7.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110184993A1 (en) * | 2010-01-27 | 2011-07-28 | Vmware, Inc. | Independent Access to Virtual Machine Desktop Content |
| CN108428104A (en) * | 2018-01-03 | 2018-08-21 | 平安科技（深圳）有限公司 | Wages computational methods, application server and computer readable storage medium |
| CN108510238A (en) * | 2018-03-12 | 2018-09-07 | 平安普惠企业管理有限公司 | Compensation data processing method, device, computer equipment and storage medium |
| CN109472544A (en) * | 2018-09-27 | 2019-03-15 | 平安科技（深圳）有限公司 | Compensation data processing method, device, computer equipment and storage medium |
| CN112712347A (en) * | 2021-01-06 | 2021-04-27 | 福建亿能达信息技术股份有限公司 | Salary calculation method, device, equipment and medium based on AI |
-
2021
- 2021-11-30 CN CN202111437938.6A patent/CN114066590B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110184993A1 (en) * | 2010-01-27 | 2011-07-28 | Vmware, Inc. | Independent Access to Virtual Machine Desktop Content |
| CN108428104A (en) * | 2018-01-03 | 2018-08-21 | 平安科技（深圳）有限公司 | Wages computational methods, application server and computer readable storage medium |
| CN108510238A (en) * | 2018-03-12 | 2018-09-07 | 平安普惠企业管理有限公司 | Compensation data processing method, device, computer equipment and storage medium |
| CN109472544A (en) * | 2018-09-27 | 2019-03-15 | 平安科技（深圳）有限公司 | Compensation data processing method, device, computer equipment and storage medium |
| CN112712347A (en) * | 2021-01-06 | 2021-04-27 | 福建亿能达信息技术股份有限公司 | Salary calculation method, device, equipment and medium based on AI |
Non-Patent Citations (1)
| Title |
|---|
| 董智勇等: "基干三层B/S结构的绩效评价数据采集系统", 石油规划设计, no. 06, 20 November 2008 (2008-11-20), pages 22 - 24 * |
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