CN115249131A

CN115249131A - Data processing method and apparatus for determining employee work quality, medium, and program

Info

Publication number: CN115249131A
Application number: CN202211007953.1A
Authority: CN
Inventors: 郭传亮
Original assignee: Hope Zhizhou Technology Shenzhen Co ltd
Current assignee: Hope Zhizhou Technology Shenzhen Co ltd
Priority date: 2022-06-21
Filing date: 2022-06-21
Publication date: 2022-10-28
Also published as: CN114781948A; CN114781948B

Abstract

The application provides a data processing method, a device, a medium and a program for determining the working quality of an employee, wherein the method comprises the following steps: acquiring production information finished by target employees in a target time; the production information comprises production time, product type and product difficulty level; determining a first multi-objective optimization index of the product in each product difficulty grade according to the production information; wherein the first multi-objective optimization index is used for indicating the comprehensive quality of the products in each difficulty level; summing the products of the weight proportion W corresponding to each product difficulty grade and the first multi-target optimization index corresponding to each product difficulty grade to obtain a product quality evaluation index Q; determining the working quality of the target staff according to the product quality evaluation index Q; wherein the work quality of the target staff is positively correlated with the product quality evaluation index Q. The method and the device for determining the working quality of the staff are high in accuracy and reliability.

Description

Data processing method and apparatus for determining employee work quality, medium, and program

Technical Field

The present invention relates to the field of data processing technologies, and in particular, to a data processing method, apparatus, medium, and program for determining the quality of work of an employee.

Background

The traditional quality evaluation of production staff is to integrate the quality of all products produced by the staff for unified evaluation, and has no difficulty weight check of corresponding products, and the quality of a high-difficulty product and the quality of a low-difficulty product have no comparability, so that the evaluation of the staff is not objective and accurate.

Disclosure of Invention

The application provides a data processing method, a data processing device, a data processing medium and a data processing program for determining the working quality of employees, so as to improve the reliability of quality evaluation of products produced by target employees.

In a first aspect, the present application provides a data processing method for determining employee work quality, including:

acquiring production information finished by target employees within a target time; wherein the production information comprises production time, product type and product difficulty level;

determining a first multi-objective optimization index of the product in each product difficulty grade according to the production information; wherein the first multi-objective optimization index is used to indicate the integrated quality of the products within each product difficulty level;

summing the products of the weight proportion W corresponding to each product difficulty grade and the first multi-objective optimization index corresponding to each product difficulty grade to obtain a product quality evaluation index Q;

determining the working quality of the target staff according to the product quality evaluation index Q; wherein the work quality of the target staff is positively correlated with the product quality evaluation index Q.

In a second aspect, the present application provides a data processing apparatus for determining employee work quality, comprising:

the acquisition unit is used for acquiring production information finished by target employees within a target time; wherein the production information comprises production time, product type and product difficulty level;

the processing unit is used for determining a first multi-objective optimization index of the product in each product difficulty grade according to the production information; wherein the first multiobjective optimization index is used for indicating the comprehensive quality of the products in each difficulty level;

the processing unit is further used for summing the product of the weight proportion W corresponding to each product difficulty grade and the first multi-target optimization index corresponding to each product difficulty grade to obtain a product quality evaluation index Q;

the determining unit is used for determining the working quality of the target staff according to the product quality evaluation index Q; wherein the work quality of the target staff is positively correlated with the product quality evaluation index Q.

In a third aspect, the present application provides a server comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the data processing method for determining employee work quality as described above, or the programs comprising instructions for the steps of the data processing apparatus for determining employee work quality as described above.

In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to perform the method described above for the data processing method for determining employee work quality.

In the embodiment of the application, the server can obtain the production information finished by the target staff in the target time, wherein the production information comprises the production time, the product type and the product difficulty level, so as to determine the first multi-target optimization index of the product in each product difficulty level according to the production information, wherein the first multi-target optimization index is used for indicating the comprehensive quality of the product in each difficulty level, and then the product of the weight ratio W corresponding to each product difficulty level and the first multi-target optimization index corresponding to each product difficulty level can be summed to obtain the product quality evaluation index Q; finally, determining the working quality of the target staff according to the product quality evaluation index Q; wherein the work quality of the target staff is positively correlated with the product quality evaluation index Q. Therefore, the server enables the finally output evaluation result of the working capacity of the staff to be more accurate based on the intelligent and automatic processing flow, and the use experience of the user is improved. In addition, the server can further improve the accuracy and reliability of the work quality evaluation of the target staff by adding the consideration of the weight of the product difficulty level in the confirmation process of the product quality evaluation index Q.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a block diagram of a server according to an embodiment of the present invention;

fig. 2 is a schematic flowchart of a data processing method for determining employee work quality according to an embodiment of the present invention;

FIG. 3 is a partial example diagram of a work order labor hour database provided by an embodiment of the present invention;

FIG. 4 is a partial illustration of a product quality database provided by an embodiment of the present invention;

FIG. 5A is a block diagram of functional units of a data processing apparatus for determining the working quality of an employee according to an embodiment of the present invention;

fig. 5B is a block diagram of functional units of another data processing apparatus for determining the working quality of an employee according to an embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. 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 application.

The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

The following description will be given of terms related to the present application.

And (3) production tasks: the method is characterized in that the method meets the work task of scheduling specific continuous production batches, and input materials, products and production lines produced under one production task are the same. For example, in the production task of number 20210100, the target product is an X product, the input material is a Y material, and the production line number is 1, the production task includes at least one process of processing the Y material in the production line 1 by at least one production device to finally generate the X product.

Production batch: one process of the input materials processed by the at least one production device to obtain the products is a production batch, and one production task comprises at least one production batch. For example, in the production task numbered 20210100, the target product is an X product, which corresponds to three production lots, i.e., a lot a, a lot B, and a lot C, each production lot corresponds to at least one production device Pn, e.g., the lot a corresponds to two production devices P1 and P2, i.e., in the lot a, the material Y is processed by the material P1 to obtain an intermediate material Z, and the material Z is processed by the material P2 to generate the X product.

Production of a work order: the system is used for counting the list of the working hours of the staff. The system can be more convenient for the performance assessment of the employees at the end of the month according to the record of the production work order. One shift (e.g., early, middle, and late shifts) of an employee corresponds to a work order. One production task comprises a plurality of production work orders in different time periods, and one or more production batches in one or more production tasks can be contained in the production work order in one time period, so that operators corresponding to the work orders can be traced conveniently when the production tasks are unified.

At present, in the machine learning process and the actual mass production process before production, the technical ability of a person to be dispatched has high requirement standards, and how to identify whether the person has excellent operation technical ability is an important problem to be solved in the production and dispatching process. It is understood that the level of the operational skills of the employees can be judged according to the quality of the products produced by the employees. The traditional evaluation of the working quality of the staff is not subjected to weight assessment corresponding to the production difficulty of the product, obviously, the production quality of a product with high difficulty and the production quality of a product with low difficulty are not comparable, so the evaluation of the working quality of the staff in the related technology is not comprehensive and objective, namely the performance evaluation of the staff is not comprehensive and objective. Meanwhile, quality evaluation data obtained according to the evaluation method in the related art cannot track the production batch of the corresponding staff, so that the data is inaccurate. In conclusion, the quality evaluation method in the related art cannot give full play to the production advantages of the staff when the staff are allocated with the production tasks, and the production efficiency is low.

In order to solve the above problems, the present application provides a data processing method and a related apparatus for determining the working quality of an employee, and the method can be applied to the field of production and manufacturing business. The server can obtain production information finished by target employees in target time, wherein the production information comprises production time, product types and product difficulty grades, so that first multi-target optimization indexes of products in each product difficulty grade are determined according to the production information, the first multi-target optimization indexes are used for indicating the comprehensive quality of the products in each difficulty grade, and then the products of the weight proportion W corresponding to each product difficulty grade and the first multi-target optimization indexes corresponding to each product difficulty grade can be summed to obtain a product quality evaluation index Q; finally, determining the working quality of the target staff according to the product quality evaluation index Q; wherein the work quality of the target staff is positively correlated with the product quality evaluation index Q. The method and the device can be applied to application scenarios needing to evaluate the working quality of the staff, application scenarios needing to evaluate the production operation skills of the staff and application scenarios evaluating the machine learning condition, including but not limited to the application scenarios mentioned above.

The embodiments of the present application will be described below with reference to the drawings.

The related device provided by the present application includes a server 10, and the constituent structure of the server 10 in the present application may be as shown in fig. 1. The server 10 may comprise a processor 110, a memory 120, a communication interface 130, and one or more programs 121, wherein the one or more programs 121 are stored in the memory 120 and configured to be executed by the processor 110, and wherein the one or more programs 121 comprise instructions for performing any of the steps of the embodiments of the data processing method for determining the employee's work quality. Or the one or more programs 121 shown include instructions for steps of data processing to determine employee work quality.

The communication interface 130 is used to support communication between the server 10 and other devices.

Processor 110 may include one or more processing cores. The processor 110 connects various parts throughout the server 10 using various interfaces and lines, and performs various functions of the server 10 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 120, and calling data stored in the memory 120. Alternatively, the Processor 110 may be a Central Processing Unit (CPU), a general purpose Processor, a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, units, and circuits described in connection with the disclosure of the embodiments of the application. The processor may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs, and microprocessors, among others.

The memory 120 may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of example, and not limitation, many forms of Random Access Memory (RAM) are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), and direct bus RAM (DR RAM). The memory 120 may be used to store instructions, programs, code sets, or instruction sets. The memory 120 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like. The storage data area may also store data created by the server 10 in use, and the like.

In a specific implementation, the processor 110 is configured to perform any one of the steps performed by the server 10 in the method embodiments described below, and when performing data transmission such as sending, optionally invokes the communication interface 130 to complete the corresponding operation.

It should be noted that the schematic structural diagram of the server 10 is merely an example, and more or fewer components may be specifically included, which is not limited herein.

The following describes a data processing method for determining the working quality of an employee according to an embodiment of the present application.

Referring to fig. 2, fig. 2 is a schematic flowchart of a data processing method for determining the work quality of an employee according to an embodiment of the present application, where the data processing method for determining the work quality of an employee is applied to the server 10 shown in fig. 1. As shown in fig. 2, the data processing method for determining the working quality of the employee includes:

step 210, obtaining production information finished by target employees in a target time; wherein the production information comprises production time, product type, and product difficulty level.

In specific implementation, the production time refers to the working time of the staff. The product type is a target product of production, and the product type may be indicated by a name, a number, or other terms that can indicate the type of product. The product difficulty level may be divided according to actual requirements, for example, the product difficulty level may be divided into three levels, such as: low, medium, and high difficulty. Referring to fig. 3 and 4, in order to facilitate the server to record and store, the low difficulty level may be recorded as 1, the medium difficulty level may be recorded as 2, and the high difficulty level may be recorded as 3; alternatively, the product difficulty rating may be divided into five ratings, such as: similarly, for convenience of recording and storing, the low difficulty level may be recorded as 1, the low difficulty level may be recorded as 2, the medium difficulty level may be recorded as 3, the high difficulty level may be recorded as 4, and the high difficulty level may be recorded as 5. For convenience of description, the following description will be made with the product difficulty rating being divided into three product difficulty ratings, high, medium and low.

The product difficulty level corresponding to the product type can be set according to the production characteristics of the product type, namely, the product type and the product difficulty level are in one-to-one correspondence. Exemplary products produced by target employee a at the target time are: product A, product B, product C, product D, product E, product F, product G, product H, and product H. The product difficulty grades of the product A, the product B and the product C are high difficulty grades, the product difficulty grades of the product D, the product E and the product F are medium difficulty grades, and the product difficulty grades of the product G, the product H and the product H are low difficulty grades.

In one possible example, the obtaining production information that the target employee completed within the target time includes: receiving an evaluation request of user equipment; determining the target time according to the evaluation request; acquiring all production work orders of the target staff within the target time according to the target time; the product type and the product difficulty level corresponding to the product type are recorded in the production work order.

In order to facilitate information acquisition and improve evaluation result statistical efficiency, product types of all produced products and the product difficulty grades corresponding to the product types are recorded in a production work order. In order to facilitate recording, storing and querying, in this example, the production work order is an electronic work order, and all production work orders can be uniformly stored in the work order work hour database, so that the server can obtain the historical production work order. The production information may also include information such as a production task and a production lot corresponding to the production task. In other embodiments, the production time, the product type, the product difficulty level, the production task and the production batch corresponding to the product type, and other information in the production information may be recorded in different positions in a scattered manner, so long as it is ensured that the corresponding production information can be obtained by querying to calculate the product quality evaluation index, and no further limitation is imposed here.

In this embodiment, the server may receive the evaluation request sent by the user equipment and then calculate and evaluate the quality of the product produced by the target employee. The target time may be included in the evaluation request sent by the user equipment, so that the server may determine the target time according to the evaluation request. When the production information of the target staff in the target time is obtained, all production worksheets of the target staff in the target time can be directly obtained through screening of the target staff and the target time. Alternatively, in another example, all production work orders within the target time may be obtained from the work order work hour database according to the production time in the production work orders, and then the production work orders of the target employees corresponding to the target time may be obtained from all the obtained production work orders. In another example, all production work orders of the target employees may be obtained from the work order time database, and then the production work orders of the target employees in the target time may be obtained from all the obtained production work orders.

In a specific implementation, the target time is typically a certain period of time in the past, for example, the last half year or a year, which can be set and selected according to the needs. A plurality of production work orders of a plurality of employees are stored in the work order working hour database within the target time. For example, referring to fig. 3, fig. 3 is a partial exemplary diagram of a work order working hour database provided by an embodiment of the present invention, and as shown in fig. 3, the production work orders of target employees in the target time recorded in the work order working hour database may include: when the target time is from 3 months to 5 months in 2022 and the target employee is the employee a (employee number is 108866), the production information of all the production order records corresponding to the employee a includes: the production information of the employee A is that the employee A produces a product A with high difficulty at 9 am to 11 am in 3 month and 1 in 2022, the employee A produces a product D with medium difficulty at 3 pm to 5 am in 3 month and 2 in 2022, and the employee A produces a product G with low difficulty at 8 am to 11 am in 5 month and 3 in 2022. Further, referring to fig. 3, information such as a production task corresponding to the type of product to be produced and the production time, and a production lot corresponding to the production task may be recorded in the production work order.

And 220, determining a first multi-objective optimization index of the product in each product difficulty grade according to the production information.

Wherein the first multi-objective optimization index is used to indicate the integrated quality of the products within each product difficulty level.

The first multi-objective optimization index is specifically used for indicating the comprehensive quality of products of the product types with the same product difficulty level in each product difficulty level. The same product difficulty level can include a plurality of product types, illustratively, among all products produced by the target employee A within the target time, the product types belonging to the high difficulty level include a product A, a product B and a product C, and at this time, the first multi-objective optimization index of the products in the high difficulty level is used for indicating the comprehensive quality of the products A, the products B and the products C with the high difficulty level produced by the target employee A within the target time.

In one possible example, the determining a first multiobjective optimization index for all of the product types in each of the product difficulty ratings comprises: acquiring a second multi-objective optimization index of the products correspondingly produced in each production batch in all the production tasks; wherein the second multiobjective optimization index is used for indicating the quality of the products correspondingly produced by each production batch; classifying the second multi-objective optimization index according to the product difficulty level of the product correspondingly produced in the production batch; and determining the mean value of the second multi-objective optimization indexes corresponding to all the production batches in each product difficulty grade to obtain the first multi-objective optimization index.

Wherein different production batches can be used for producing products of the same product type, and can also be used for producing products of different product types. The products produced by each production batch correspondingly have a second multi-objective optimization index. The second multi-objective optimization index may represent a quality of a product produced by the production lot corresponding thereto. The second multiobjective optimization index may be recorded in a tenth or percentile manner to facilitate the calculation and evaluation. It can be understood that when the percentage system is adopted, the quality of the product can be judged more accurately while calculation is facilitated. The second multi-objective optimization indexes are parameters inherent to the production batches, and the second multi-objective optimization indexes corresponding to all the production batches can be stored and recorded in the product quality database so as to be conveniently and uniformly stored and conveniently acquired in the follow-up process, as shown in fig. 4. When the server obtains all production work orders of the target staff in the target time, the second multi-target optimization index of the production batch can be obtained from the product quality database correspondingly according to the production batch recorded on the production work orders.

Illustratively, if the content of the evaluation request is that the work quality of the employee A with the employee number of 108866 in 3 months to 5 months of 2022 is determined. Then, in the above request, the target time is 2022 to 5 months, and the target employee is employee a with employee number 108866. The server may obtain corresponding production information according to the evaluation request, as shown in fig. 3 and 4. As can be seen from the figure, employee a participates in producing 18 batches of products in the target time, wherein 6 production batches are used for producing products with high difficulty, 7 production batches are used for producing products with medium difficulty, 5 production batches are used for producing products with low difficulty, and the second multi-objective optimization index of the products of each batch is shown in fig. 4. The first multi-objective optimization index corresponding to a product with high difficulty is recorded as Ph, the first multi-objective optimization index corresponding to a product with medium difficulty is recorded as Pm, and the first multi-objective optimization index corresponding to a product with low difficulty is recorded as Pl, in this example, ph = (86 +82+84+88+80+ 81)/6 =83.5, pm = (92 +84+85 +84+85+ 86)/7 =85.9, pl = (92 +8+94+90+ 96)/5 =92.

In this embodiment, the first multi-target optimization index is determined by averaging the second multi-target optimization indexes of all production batches in the same product difficulty level, so that the accuracy of evaluating the product production quality of the product difficulty level corresponding to the first multi-target optimization index by the first multi-target optimization index can be improved, and the problem that the obtained first multi-target optimization index is inaccurate in evaluating the product quality due to the fact that part of product type samples are few and the quality difference between the part of product type samples and other product types is large is avoided.

In one possible example, the determining a first multi-objective optimization index for all of the product types in each of the product difficulty ratings comprises: acquiring a second multi-objective optimization index of the products correspondingly produced in each production batch in all the production tasks; wherein the second multiobjective optimization index is used for indicating the quality of the products correspondingly produced by each production batch; classifying the second multi-objective optimization index according to the product types of the products correspondingly produced in the production batch; determining the mean value of the second multi-objective optimization indexes corresponding to all the production batches in the same product type to obtain a third multi-objective optimization index corresponding to the product type; wherein the third multiobjective optimization index is used to indicate the combined quality of products of the same product type; classifying the third multi-objective optimization index according to the product difficulty level corresponding to the product type; determining the mean value of the third multi-objective optimization indexes of all the product types in each product difficulty grade to obtain the first multi-objective optimization index.

Wherein the third multiobjective optimization index is used to indicate the combined quality of products of the same product type in all production lots for all production tasks. Wherein a production task may comprise at least one production lot, and when there are at least two production lots, the at least two production lots may be used to produce products of the same product type. Meanwhile, production batches in different production tasks may be used for producing products of the same product type, or may also be used for producing products of different product types. Illustratively, in the production job No. 20220100, two production lots with

production lots

20220101 and 20220102 are included, and in the production job No. 20220200, three production lots with

production lots

20220201, 20220202 and 20220203 are included. The three production batches with the production batches of numbers of 20220101 and 20220102 are all used for producing a product A, the three production batches with the production batches of numbers of 20220201, 20220202 and 20220203 are all used for producing a product B, the third multi-objective optimization index of the product A is used for indicating that the type of the product produced by the target employee A in the target time is the comprehensive quality of the product A, and the third multi-objective optimization index of the product B is used for indicating that the type of the product produced by the target employee A in the target time is the comprehensive quality of the product B.

Illustratively, if the content of the evaluation request is that the work quality of the employee A with the employee number of 108866 in 3 months to 5 months of 2022 is determined. Then, in the above request, the target time is 2022 to 5 months, and the target employee is employee a with employee number 108866. The server may obtain corresponding production information according to the evaluation request, as shown in fig. 3 and 4. As can be seen, employee A participates in producing 18 batches of products together within the target time, wherein the second multiobjective optimization index of each produced batch of products is shown in FIG. 4. The server can classify the products into nine product types from the product A to the product I according to the product type names, and can obtain a third multi-objective optimization index corresponding to the product type by determining the mean value of the second multi-objective optimization indexes of the same product type. For example, the third multi-objective optimization index (here denoted as PA) of product a is the mean of the second multi-objective optimization index (here denoted as PA 1) corresponding to production lot 20220101 and the second multi-objective optimization index (here denoted as PA 2) corresponding to production lot 20220102, i.e., PA = (PA 1+ PA 2)/2 = (86 + 82)/2 =84. Similarly, product B has a third multi-objective optimization index (herein PB) of 84, product C has a third multi-objective optimization index (herein PC) of 81, product D has a third multi-objective optimization index of 88, product E has a third multi-objective optimization index of 85, product F has a third multi-objective optimization index of 85, product G has a third multi-objective optimization index of 92, product H has a third multi-objective optimization index of 91, and product I has a third multi-objective optimization index of 93. Then, the server can classify 9 product types according to the product difficulty grades, namely A, B, C is divided into products with high difficulty and the like, D, E, F is divided into products with medium difficulty, G, H, I is divided into products with low difficulty and the like, and then the mean value of third multi-objective optimization indexes in each product difficulty grade is obtained to determine the first multi-objective optimization index corresponding to each product difficulty grade. For example, the first multi-objective optimization index (denoted as Ph herein) corresponding to the product with high difficulty is equal to the average of the third multi-objective optimization index of product a, the third multi-objective optimization index of product B, and the third multi-objective optimization index of product C, i.e. Ph = (PA + PB + PC)/3 = (84 + 81)/3 =83. Similarly, a first multi-target optimization index Pm = (88 + 85)/3 =86corresponding to a product with medium difficulty; the first multi-target optimization index Pl = (92 +91+ 93) =92 corresponding to the product with low difficulty.

The method can be used for classifying the production batches for producing the products of the same product type together, obtaining the third multi-objective optimization index in a mode of averaging the second multi-objective optimization indexes of the products produced by all the production batches in the same product type, and improving the accuracy and stability of the quality judgment of the products of the same product type by the third multi-objective optimization index. The product produced by the target employee within the target time may have multiple product types, each corresponding to a different product difficulty rating. The first multi-target optimization index is obtained by averaging the third multi-target optimization indexes of all product types in the same product difficulty level, so that the accuracy and stability of quality judgment of the produced product with the same product difficulty level by the first multi-target optimization index can be further improved, and the reliability of the finally obtained product quality evaluation index is improved and ensured.

Based on the embodiment, in order to further improve the accuracy of the finally obtained first multi-objective optimization index on the quality evaluation of the produced product, when the third multi-objective optimization index is calculated, the data of the product type with fewer samples can be removed, that is, the data of the product type is not adopted to evaluate the work quality of the target staff. Specifically, if the number of samples of a certain product type is smaller than a preset number, the data related to the product type is removed, where the number of samples is the number of production batches, the preset number may be two, three, or more, and may be specifically set according to actual requirements, which is not further limited herein.

Therefore, the scheme of the embodiment can be used for solving the first multi-objective optimization index, and the third multi-objective optimization index obtained in the process of solving the first multi-objective optimization index can also be used for evaluating the capability of target employees in producing products of the product type, so that the data utilization rate can be improved, and the data processing and data storage pressure of the server can be reduced.

And step 230, summing the products of the weight proportion W corresponding to each product difficulty grade and the first multi-target optimization index corresponding to each product difficulty grade to obtain a product quality evaluation index Q.

It can be understood that the step is to perform weighting processing through the first multi-objective optimization indexes corresponding to different product difficulty levels, so that the reliability and the accuracy of comprehensive quality assessment of products produced by target employees can be improved.

In one possible embodiment, the weight ratio W corresponding to each product difficulty level is positively correlated with the level of the product difficulty level; wherein the rating criteria for the product difficulty rating comprises at least one of a health of the job specification criteria, a number of steps of the process flow, a scalability of a product process parameter, and an automation level of the equipment.

Products with different product difficulty grades produced by all the employees can be calculated according to a uniform weight ratio, so that the fairness and the reliability of comparing the production skill operation levels of the employees through product quality evaluation indexes among different employees are enhanced. The weight proportion of different product difficulty grades can be set according to actual conditions, specifically, the weight proportion of different product difficulty grades can be set according to the difficulty degree of product production, and the larger the production difficulty is, the higher the weight proportion of the produced product is. It will be appreciated that the greater the difficulty of production, the greater the requirement on the level of production skill operation of the employee. Therefore, the weight proportion corresponding to each product difficulty level is set in direct proportion to the product difficulty level, and the production operation skill level of the staff can be evaluated more accurately. For example, the weighting ratios of the various product difficulty levels may be set as the following parameters: the weight proportion of the high difficulty level is 60%, the weight proportion of the medium difficulty level is 30%, and the weight proportion of the low difficulty level is 10%. Of course, in the example, if the production difficulty of the product with high difficulty is far greater than that of the product with medium difficulty and that with low difficulty, the weight ratio of the product with high difficulty may be increased, for example, the weight ratio of each product difficulty level may also be set as the following parameter: the weight proportion of high difficulty is 75%, the weight proportion of medium difficulty is 20%, and the weight proportion of low difficulty is 5%.

It will be appreciated that factors that affect the level of difficulty of a product include the health of the operating specification standards, the number of steps in the process flow, the scalability of the process parameters of the product, the degree of automation of the equipment, and the like. The operation standard is a unified requirement and a standardized regulation which are made for various production works and are made by standardizing the operation specification of the staff. The staff can produce the qualified products according to the operation standard, so that the higher the soundness degree of the operation standard is, the lower the difficulty of the staff in producing the products is, namely the lower the product difficulty level is correspondingly. The number of steps of the process flow determines the complexity of the production process of the product, and if the number of steps of the process flow is larger, the higher the complexity of the production of the product is, i.e., the higher the difficulty level of the product is. The product process parameters comprise quality parameters of intermediate materials and final products of the product in the production process, relevant process parameters in the production process and the like, the higher the testability of the product process parameters is, the higher the controllability of the product production process is, namely, the lower the product difficulty level is correspondingly. When the measurability of the product process parameters is low, the production experience of staff is more needed for judgment, so that the product difficulty level is correspondingly higher. The higher the automation degree of the equipment is, the smaller the human influence factor in the production process of the product is, and the lower the difficulty level of the product is correspondingly.

In one possible example, the product difficulty ratings of the products are divided according to table 1 below.

TABLE 1

In specific implementation, when the product difficulty grades of the products are classified, the products of various product types can be classified according to the table 1, and when the score of the product is 4 to 6, the product is a product with low difficulty; if the score of the product is 7 to 9, the product is a medium-difficulty product; the product has a score of 10 to 12, and is a product with high difficulty.

It is understood that the influence strength of each influence factor on the product difficulty level is different, for example, the influence strength of the automation degree of the equipment and the scalability of the product process parameter is greater than the influence strength of the number of steps of the process flow and is much greater than the influence strength of the soundness degree of the operation specification standard. Furthermore, in order to improve the accuracy of the product difficulty level division, weighting processing can be performed on each influence factor when the product score is counted according to the influence strength of each influence factor on the product difficulty level. For example, the weight of each influence factor may be set as the following parameters: the automation level of the equipment is weighted 35%, the measurability of the process parameters of the product is weighted 35%, the number of steps of the process flow is weighted 20%, the health level of the operating specification standard is weighted 10%, and if the score of the product is in the interval

The product is a product with low difficulty; if the score of the product is in the interval

The product is a medium difficult product; the product has score in interval

The product is a product with high difficulty. Illustratively, if the health term of the job specification standard of product a is scored as 2 points, the number term of the steps of the process flow is scored as 2 points, the measurability term of the process parameter of the product is scored as 3 points, and the automation term of the equipment is scored as 3 points, then the total score of product a is: 2 × 10% +2 × 20% +3 × 35% =2.7, and it can be seen that product a is a highly difficult product.

Or when the product difficulty grades of the products are divided, the server can store all the scoring conditions of high difficulty, medium difficulty and low difficulty, and divide the product difficulty grades according to the scoring conditions, so that the server can quickly confirm the corresponding product difficulty grades according to the scores of the target products, and the confirmation speed is increased. For example, when the score of the product is 4 points, 5 points or 6 points, the product is a product with low difficulty; if the score of the product is 7 points, 8 points or 9 points, the product is a product with medium difficulty; the score of the product is 10 points, 11 points or 12 points, and the product is a product with high difficulty. It can be understood that, when the product difficulty level of the product is determined by using the scheme of the present example, each influence factor may also be weighted according to the influence strength of each influence factor, and all possible score conditions after the weighting processing are stored and recorded, which will not be described in detail.

Step 240, determining the working quality of the target staff according to the product quality evaluation index Q; wherein the work quality of the target staff is positively correlated with the product quality evaluation index Q.

It can be understood that the product quality evaluation indexes of all employees in the target time can be obtained according to the above steps, and the larger the product quality evaluation index of the product produced by the target employee in the target time is, the higher the comprehensive quality level of the product produced by the target employee in the target time is, that is, the higher the working quality of the target employee in the target time is.

In a specific implementation, in order to compare the levels of the operational skills produced by all the employees in the same target time, the product quality evaluation indexes of all the employees in the same target time may be sorted in sequence, and the higher the arrangement sequence of the employees is, the higher the production quality of the employees is. The production operation skill levels of all the employees can be compared more intuitively through a sequencing mode. And the factory can also divide a plurality of employees in a grading way according to the sorting result, so that the employees with qualified performance of the work quality can be quickly determined, and the awards for the employees are conveniently displayed according to the division list. For example, when the value of the product quality evaluation index of the preselected rewardable employee is higher than the preset value, that is, the preselected rewardable employee meets the reward condition, the employee may be graded for reward. For example, if there are eleven rewardable denominations, the product quality assessment index of the eleven top-ranked employees may be compared to a preset value expected by the plant, and if all meet the expected goal of the plant, the eleven employees may be rewarded. When the eleven workers are rewarded, the eleven workers can be rewarded in a grading mode according to the arrangement sequence of the eleven workers, for example, the elemen can be divided into a first-grade prize list, the fourth-sixth ranked prize list and the seventh-eleventh ranked prize list, wherein the first three ranks are divided into the first-grade prize list, the fourth-sixth ranked prize list and the third-grade prize list are divided into the third-grade prize list.

The first multi-target optimization index can be used for calculating a product quality evaluation index so as to comprehensively evaluate the quality of a product produced by a target employee within a target time, and can also be used for judging which product difficulty grade the target employee is suitable for producing.

In one possible example, the method further comprises: acquiring preset parameters of each product difficulty grade; comparing the preset parameters of the product difficulty grades with the first multi-target optimization index corresponding to the product difficulty grades to obtain comparison results; determining an adaptation difficulty evaluation index D corresponding to the target employee according to the comparison result; and determining the product difficulty level of the target staff in adaptive production according to the adaptation difficulty evaluation index D.

In this example, for a product produced by a target employee within a target time, whether the target employee is suitable for producing the product with the product difficulty level can be determined by comparing the first multi-target optimization index corresponding to different product difficulty levels with the preset parameter corresponding to the first multi-target optimization index. In a specific implementation, if a first target index of a product with a product difficulty level produced by a target employee is greater than or equal to a preset parameter corresponding to the product difficulty level, the target employee is suitable for producing the product with the product difficulty level. The preset parameters are preset reference quantities aiming at various product difficulty grades, and can be specifically set according to the production quality requirements of products with different product difficulty grades in a factory. For example, if the first multi-objective optimization index adopts a percentile system, the preset parameters of the products with high difficulty, the preset parameters of the products with medium difficulty, and the preset parameters of the products with low difficulty may all be set to eighty, that is, eighty is a qualified line for judging whether the employee production has corresponding production operation skills.

It can be understood that the production operation difficulty of products with high difficulty is greater than that of products with medium difficulty and that of products with low difficulty. Therefore, the staff with the production operation skills for producing the products with higher product difficulty levels usually have the production operation skills for producing the products with lower product difficulty levels. Therefore, when preset parameters are used for comparing and judging which product difficulty grade the employee can produce, a downward compatible mode can be adopted. Specifically, if the first multi-objective optimization index corresponding to the high-difficulty product produced by the target employee, the first multi-objective optimization index corresponding to the medium-difficulty product produced by the target employee, and the first multi-objective optimization index corresponding to the low-difficulty product produced by the target employee are all greater than or equal to the preset parameters corresponding thereto, the adaptation difficulty evaluation index corresponding to the target employee may be marked as a first grade (which may be marked as D =1 for recording and storage). When the fitting difficulty evaluation index is marked as the first grade, it means that the target employee is adapted to produce products of all the product difficulty grades. When the first multi-objective optimization index corresponding to the high-difficulty product produced by the target employee is smaller than the corresponding preset parameter, if the first multi-objective optimization index corresponding to the medium-difficulty product produced by the target employee and the first multi-objective optimization index corresponding to the low-difficulty product produced by the target employee are both greater than or equal to the corresponding preset parameters, the adaptation difficulty evaluation index corresponding to the target employee may be marked as a second grade (which may be marked as D = 2), and when the adaptation difficulty evaluation index is marked as the second grade, it means that the target employee is adapted to the production of the medium-difficulty and low-difficulty products. When the first multi-objective optimization index corresponding to the high-difficulty product produced by the target employee and the first multi-objective optimization index corresponding to the medium-difficulty product produced by the target employee are both smaller than the respective corresponding preset parameters, if the first multi-objective optimization index corresponding to the low-difficulty product produced by the target employee is greater than or equal to the corresponding preset parameters, the adaptation difficulty evaluation index corresponding to the target employee may be marked as a third level (which may be marked as D = 3), and when the adaptation difficulty evaluation index is marked as the third level, it means that the target employee is adapted to produce the low-difficulty product.

In one possible embodiment, the method further comprises: arranging the first multi-objective optimization indexes corresponding to the product difficulty grades in sequence from high to low; determining the product difficulty level of the target staff for adaptive production according to the arrangement sequence; the more front the arrangement sequence is, the higher the adaptation degree of the product difficulty grade corresponding to the first multi-objective optimization index and the target staff is.

In this example, in the products produced by the target staff within the target time, the first multi-target optimization indexes corresponding to the products with different product difficulty levels are sequentially arranged, so that the level of the production operation skill level of the target staff for producing the products with various product difficulty levels can be more simply and visually identified, and the judgment of which product difficulty level the target staff is more suitable for producing is facilitated. Therefore, more appropriate work can be correspondingly distributed to target employees during subsequent distribution work, and the quality and the efficiency of product production are improved to a greater extent. Illustratively, the target employee is employee B, the first multi-objective optimization index of the high-difficulty product produced by employee B is 83, the first multi-objective optimization index of the medium-difficulty product produced by employee A is 86, and the first multi-objective optimization index of the low-difficulty product produced by employee A is 92, and the sequence arrangement is that 92 is greater than 86 and greater than 83. Therefore, the quality of the low-difficulty products produced by the employee A in the target time is higher than that of the medium-difficulty products and that of the high-difficulty products. Therefore, compared with the method that the employee A is more suitable for producing the products with low difficulty and the task of producing the products with low difficulty can be distributed to the employee B when the work task is subsequently assigned.

Optionally, data such as the product quality evaluation index, the adaptation difficulty evaluation index, the first multi-objective optimization index ranking and the like may be stored in the employee performance skill tag database. The data in the product quality database, the data in the work order work hour database, and the data in the employee performance skill tag database may be stored in different blockchain nodes, for example, the data in the product quality database may be stored in a first blockchain node, the data in the work order work hour database may be stored in a second blockchain node, and the data in the employee performance skill tag database may be stored in a third blockchain node. The data such as the product quality evaluation index, the adaptation difficulty evaluation index and the first multi-objective optimization index sequencing which are calculated and generated by the server can be stored in the corresponding block chain nodes. When the server needs to obtain data such as a production work order of a target employee within a target time and a second multi-objective optimization index of each production batch in the production work order in the process of calculating the product quality evaluation index, the server needs to access the corresponding block chain node according to the key to obtain corresponding data, for example, when the second multi-objective optimization index needs to be obtained, the server needs to access the first block chain node by using the first key. It can be understood that the above data are all important production information of the plant, and are related to the operation, production and management of the plant, so that only an authorized administrator has the key for acquiring data in different blockchain nodes, that is, only the authorized administrator has access to the blockchain nodes to acquire or call the correspondingly stored data, thereby enhancing the confidentiality of the data and the security of the system.

The present application may perform the division of the functional units for the server according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit. It should be noted that, in the embodiment of the present application, the division of the unit is schematic, and is only one logic function division, and when the actual implementation is realized, another division manner may be provided.

Consistent with the above-described embodiments, fig. 5A is a block diagram of functional units of a data processing apparatus for determining employee work quality according to an embodiment of the present application. The data processing device 30 for determining the employee work quality may be applied to a server shown in fig. 1, and as shown in fig. 5A, the data processing device 30 for determining the employee work quality includes:

an obtaining unit 310, configured to obtain production information that a target employee completes within a target time; wherein the production information comprises production time, product type and product difficulty level;

the processing unit 320 is configured to determine a first multi-objective optimization index of a product in each product difficulty level according to the production information; wherein the first multiobjective optimization index is used for indicating the comprehensive quality of the products in each difficulty level; the processing unit 320 is further configured to sum products of the weight fraction W corresponding to each product difficulty level and the first multi-objective optimization index corresponding to each product difficulty level to obtain a product quality evaluation index Q;

the determining unit 330 is configured to determine the work quality of the target employee according to the product quality evaluation index Q; wherein the work quality of the target staff is positively correlated with the product quality evaluation index Q.

In one possible example, in the determining the first multi-objective optimization index for all of the product types in each of the product difficulty ratings, the processing unit 320 is specifically configured to: acquiring a second multi-objective optimization index of the products correspondingly produced in each production batch in all the production tasks; wherein the second multiobjective optimization index is used for indicating the quality of the products correspondingly produced by each production batch; classifying the second multi-objective optimization index according to the product difficulty level of the product correspondingly produced by the production batch; and determining the mean value of the second multi-objective optimization indexes corresponding to all the production batches in each product difficulty grade to obtain the first multi-objective optimization index.

In one possible example, the production information further includes a production task and a production lot corresponding to the production task, and in the obtaining of the third multi-objective optimization index of the same product type according to the production information, the processing unit 320 is specifically configured to: acquiring a second multi-objective optimization index of the products correspondingly produced in each production batch in all the production tasks; wherein the second multiobjective optimization index is used for indicating the quality of the products correspondingly produced by each production batch; classifying the second multi-objective optimization index according to the product types of the products correspondingly produced in the production batch; determining the mean value of the second multi-objective optimization indexes corresponding to all the production batches in the same product type to obtain a third multi-objective optimization index corresponding to the product type; wherein the third multiobjective optimization index is used to indicate a combined quality of products of the same product type; classifying the third multi-objective optimization index according to the product difficulty level corresponding to the product type; determining the mean value of the third multi-objective optimization indexes of all the product types in each product difficulty grade to obtain the first multi-objective optimization index.

In one possible example, in terms of obtaining the production information completed by the target employee within the target time, the obtaining unit 310 is specifically configured to: receiving an evaluation request of user equipment; determining the target time according to the evaluation request; acquiring all production work orders of the target staff within the target time according to the target time; and the production information is recorded in the production work order.

In one possible example, the processing unit 320 specifically includes: the weight proportion W corresponding to each product difficulty grade is in positive correlation with the height of the product difficulty grade; wherein the rating criteria for the product difficulty rating comprises at least one of a health of the job specification criteria, a number of steps of the process flow, a scalability of a product process parameter, and an automation level of the equipment.

In one possible example, the obtaining unit 310 is specifically configured to: and acquiring preset parameters of each product difficulty grade. The processing unit 320 is specifically configured to: comparing the preset parameters of the product difficulty grades with the first multi-target optimization index corresponding to the product difficulty grades to obtain comparison results; determining an adaptation difficulty evaluation index D corresponding to the target employee according to the comparison result; and determining the product difficulty level of the target staff in adaptive production according to the adaptation difficulty evaluation index D.

In one possible example, the processing unit 320 is specifically shown for: arranging the first multi-objective optimization indexes corresponding to the product difficulty grades in sequence from high to low; determining the product difficulty level of the target staff for adaptive production according to the arrangement sequence; the more front the arrangement sequence is, the higher the adaptation degree of the target staff is, and the product difficulty level corresponding to the first multi-target optimization index is higher.

It can be understood that, since the method embodiment and the apparatus embodiment are different presentation forms of the same technical concept, the content of the method embodiment portion in the present application should be synchronously adapted to the apparatus embodiment portion, and is not described herein again.

In the case of using an integrated unit, a block diagram of functional units of the data processing apparatus for determining the working quality of an employee provided in the embodiment of the present application is shown in fig. 5B. In fig. 5B, the data processing apparatus 30 for determining the employee work quality includes: a communication module 340 and a processing module 350. The processing module 350 is used to control and manage the actions of the data processing apparatus 30 for determining the employee work quality, such as performing the steps of the acquisition unit 310, the processing unit 320, the determination unit 330, and/or other processes for performing the techniques described herein. The communication module 340 is used to support the interaction between the data processing apparatus 30 for determining the employee's work quality and other devices. As shown in fig. 5B, the data processing device 30 for determining the employee work quality may further comprise a storage module 360, the storage module 360 for storing program codes and data of the data processing device 30 for determining the employee work quality.

The Processing module 350 may be a Processor or a controller, and may be, for example, a Central Processing Unit (CPU), a general-purpose Processor, a Digital Signal Processor (DSP), an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure of the embodiments of the application. The processor may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs, and microprocessors, and the like. The communication module 340 may be a transceiver, RF circuitry or a communication interface, etc. The storage module 360 may be a memory.

All relevant contents of each scene related to the method embodiment may be referred to the functional description of the corresponding functional module, and are not described herein again. The data processing device 30 for determining the employee work quality may perform the steps performed by the server in the data processing method for determining the employee work quality shown in fig. 2.

The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. The procedures or functions according to the embodiments of the present application are wholly or partially generated when the computer instructions or the computer program are loaded or executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire or wirelessly. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.

Embodiments of the present application also provide a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, the computer program enabling a computer to execute part or all of the steps of any one of the methods described in the above method embodiments, and the computer includes an electronic device.

It should be noted that for simplicity of description, the above-mentioned embodiments of the method are described as a series of acts, but those skilled in the art should understand that the present application is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to execute all or part of the steps of the above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.

The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. A data processing method for determining employee job quality, comprising:

acquiring production information finished by target employees in a target time; wherein the production information comprises production time, product type and product difficulty level;

determining a first multi-objective optimization index of each product with the product difficulty grade according to the production information; the first multi-objective optimization index is used for indicating the comprehensive quality of the products in each product difficulty level;

obtaining a product quality evaluation index Q based on the weight ratio W corresponding to each product difficulty grade and the first multi-objective optimization index corresponding to each product difficulty grade;

2. The method of claim 1, wherein the production information further includes production tasks and production lots corresponding to the production tasks, and wherein determining the first multi-objective optimization index for the products in each of the product difficulty ratings comprises:

acquiring a second multi-objective optimization index of the products correspondingly produced in each production batch in all the production tasks; wherein the second multiobjective optimization index is used for indicating the quality of the products correspondingly produced by each production batch;

classifying the second multi-objective optimization index according to the product difficulty level of the product correspondingly produced in the production batch;

and determining the mean value of the second multi-objective optimization indexes corresponding to all the production batches in each product difficulty grade to obtain the first multi-objective optimization index.

3. The method of claim 2, wherein the production information further includes production tasks and production lots corresponding to the production tasks, and wherein determining the first multi-objective optimization index for the products in each of the product difficulty ratings comprises:

acquiring a second multi-objective optimization index of a product correspondingly produced in each production batch in all the production tasks; wherein the second multiobjective optimization index is used for indicating the quality of the products correspondingly produced by each production batch;

classifying the second multi-objective optimization index according to the product types of the products correspondingly produced in the production batch;

determining the mean value of the second multi-objective optimization indexes corresponding to all the production batches in the same product type to obtain a third multi-objective optimization index corresponding to the product type; wherein the third multiobjective optimization index is used to indicate the combined quality of products of the same product type;

classifying the third multi-objective optimization index according to the product difficulty level corresponding to the product type;

determining the mean value of the third multi-objective optimization indexes of all the product types in each product difficulty grade to obtain the first multi-objective optimization index.

4. The method of any one of claims 1 to 3, wherein the obtaining production information completed by the target employee within the target time further comprises:

receiving an evaluation request of user equipment;

determining the target time according to the evaluation request;

acquiring all production work orders of the target staff within the target time according to the target time; the product type and the product difficulty level corresponding to the product type are recorded in the production work order.

5. The method according to any one of claims 1 to 4, wherein the weight ratio W corresponding to each product difficulty grade is in positive correlation with the level of the product difficulty grade; wherein the rating criteria for the product difficulty rating comprises at least one of a health of the job specification criteria, a number of steps of the process flow, a scalability of a product process parameter, and an automation level of the equipment.

6. The method according to any one of claims 1 to 5, further comprising:

acquiring preset parameters of each product difficulty grade;

comparing the preset parameters of the product difficulty grades with the first multi-target optimization index corresponding to the product difficulty grades to obtain comparison results;

determining an adaptation difficulty evaluation index D corresponding to the target employee according to the comparison result;

and determining the product difficulty level of the target staff in adaptive production according to the adaptation difficulty evaluation index D.

7. The method of claim 6, further comprising:

arranging the first multi-objective optimization indexes corresponding to the product difficulty grades in sequence from high to low;

determining the product difficulty level of the target staff for adaptive production according to the arrangement sequence; the more advanced the arrangement sequence, the higher the adaptation degree between the product difficulty grade corresponding to the first multi-objective optimization index and the target employee.

8. A data processing apparatus for determining the quality of a worker's work, comprising:

the acquisition unit is used for acquiring production information finished by target employees within target time; wherein the production information comprises production time, product type and product difficulty level;

the processing unit is further used for obtaining a product quality evaluation index Q based on the weight proportion W corresponding to each product difficulty grade and the first multi-objective optimization index corresponding to each product difficulty grade;

9. A server, characterized in that the server comprises a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any one of claims 1 to 7, or the programs comprising instructions of the steps of the apparatus of claim 8.

10. A computer-readable storage medium or computer program product, characterized in that the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program causes a computer to perform the method according to any one of claims 1 to 7;

alternatively, the computer program product causes a computer to perform the method of any of claims 1 to 7.