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

Abstract

This application provides a data processing method, apparatus, medium, and program for determining employee work quality. The method includes: acquiring production information completed by a target employee within a target time period; wherein the production information includes production time, product type, and product difficulty level; determining a first multi-objective optimization index for products at each product difficulty level based on the production information; wherein the first multi-objective optimization index indicates the overall quality of products within each difficulty level; summing the products of the weight percentage 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; determining the work quality of the target employee based on the product quality evaluation index Q; wherein the work quality of the target employee is positively correlated with the magnitude of the product quality evaluation index Q. The solution provided in this application provides high accuracy and reliability in determining employee work quality.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a data processing method, apparatus, medium, and program for determining the quality of employee work. Background Technology

[0002] Traditionally, the quality evaluation of production employees is conducted by integrating the quality of all products produced by the employee for a unified evaluation. There is no corresponding difficulty weight assessment for each product. The quality of a high-difficulty product is not comparable to that of a low-difficulty product, resulting in an unobjective and inaccurate evaluation of the employee. Summary of the Invention

[0003] This application provides a data processing method, apparatus, medium, and program for determining the quality of employee work, so as to improve the reliability of the quality evaluation of products produced by target employees.

[0004] In a first aspect, this application provides a data processing method for determining employee work quality, comprising:

[0005] Obtain production information completed by the target employee within the target time; wherein, the production information includes production time, product type, and product difficulty level;

[0006] Based on the production information, a first multi-objective optimization index is determined for each of the product difficulty levels; wherein, the first multi-objective optimization index is used to indicate the overall quality of the product within each product difficulty level;

[0007] The product quality evaluation index Q is obtained by summing the product of the weight ratio W corresponding to each product difficulty level and the first multi-objective optimization index corresponding to each product difficulty level.

[0008] The work quality of the target employee is determined based on the product quality evaluation index Q; wherein the work quality of the target employee is positively correlated with the magnitude of the product quality evaluation index Q.

[0009] Secondly, this application provides a data processing apparatus for determining employee work quality, comprising:

[0010] The acquisition unit is used to acquire production information completed by the target employee within a target time; wherein, the production information includes production time, product type, and product difficulty level;

[0011] The processing unit is configured to determine a first multi-objective optimization index for products in each of the product difficulty levels based on the production information; wherein the first multi-objective optimization index is used to indicate the overall quality of products in each difficulty level.

[0012] The processing unit is also used to sum the product of the weight ratio W corresponding to each product difficulty level and the first multi-objective optimization index corresponding to each product difficulty level to obtain the product quality evaluation index Q.

[0013] A determining unit is configured to determine the work quality of the target employee based on the product quality evaluation index Q; wherein the work quality of the target employee is positively correlated with the magnitude of the product quality evaluation index Q.

[0014] Thirdly, this application provides a server including 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 include instructions for performing steps as described in the data processing method for determining employee work quality, or the programs include instructions for performing steps as described in the data processing apparatus for determining employee work quality.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to execute the method described above for the data processing method for determining employee work quality.

[0016] As can be seen, in this embodiment, the server can first obtain the production information completed by the target employee within the target time, including production time, product type, and product difficulty level. Based on this production information, a first multi-objective optimization index is determined for each product difficulty level. This first multi-objective optimization index indicates the overall quality of the product within each difficulty level. Then, the product of the weight ratio W corresponding to each product difficulty level and the first multi-objective optimization index corresponding to each product difficulty level is summed to obtain the product quality evaluation index Q. Finally, the work quality of the target employee is determined based on the product quality evaluation index Q. The work quality of the target employee is positively correlated with the magnitude of the product quality evaluation index Q. Thus, the server's intelligent and automated processing flow makes the final output evaluation result of the employee's work ability more accurate, improving the user experience. Furthermore, by incorporating the product difficulty level as a weight in the confirmation process of the product quality evaluation index Q, the server can further improve the accuracy and reliability of the work quality assessment of the target employee. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a structural block diagram of a server provided in an embodiment of the present invention;

[0019] Figure 2 A flowchart illustrating a data processing method for determining employee work quality, provided in an embodiment of the present invention;

[0020] Figure 3 This is a partial example diagram of a work order and work hour database provided in an embodiment of the present invention;

[0021] Figure 4 This is a partial example diagram of a product quality database provided in an embodiment of the present invention;

[0022] Figure 5A A functional unit block diagram of a data processing device for determining employee work quality provided in an embodiment of the present invention;

[0023] Figure 5B A functional unit block diagram of another data processing device for determining employee work quality provided in an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0025] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0026] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] The following is an introduction to the relevant terminology used in this application.

[0028] A production task refers to a work assignment that satisfies the scheduling of a specific continuous production batch. Under a production task, the input materials, products, and production lines are all the same. For example, in production task number 20210100, the target product is product X, the input material is material Y, and the production line number is 1. This production task includes at least one process of processing material Y through at least one production device under production line 1 to finally generate product X.

[0029] Production Batch: A production batch is a process in which input materials are processed by at least one production device to obtain a product. A production task includes at least one production batch. For example, in production task number 20210100, the target product is product X, which corresponds to three production batches: batch A, batch B, and batch C. Each production batch corresponds to at least one production device Pn. For example, batch A corresponds to two production devices P1 and P2. That is, in batch A, P1 processes material Y to obtain intermediate material Z, and P2 processes material Z to generate product X.

[0030] Production work orders: These are lists used to track employee work hours. Recording production work orders facilitates month-end performance evaluations. One employee's shift (e.g., morning, afternoon, and evening shifts) corresponds to one work order. A production task may contain multiple production work orders for different time periods, and a single production work order for a given time period may contain one or more production batches from one or more production tasks. This allows for easy tracing of the operators responsible for each work order when tracking production tasks.

[0031] Currently, both the pre-production machine learning process and the actual mass production process place high demands on the skills of dispatchers. Identifying whether personnel possess excellent operational skills and assigning tasks accordingly is a crucial issue that needs to be addressed in the production dispatch process. It's understandable that an employee's operational skills can be judged based on the quality of the products they produce. However, traditional assessments of employee work quality lack weighting based on the production difficulty of the corresponding products. Clearly, the production quality of a high-difficulty product is incomparable to that of a low-difficulty product. Therefore, the assessment of employee work quality in related technologies is not comprehensive or objective enough, resulting in incomplete and unobjective performance evaluations. Furthermore, the quality evaluation data derived from these technologies cannot track the specific production batches of each employee, leading to inaccurate data. In conclusion, the quality evaluation methods employed in these technologies fail to maximize employee productivity when assigning production tasks, resulting in low production efficiency.

[0032] To address the aforementioned issues, this application provides a data processing method and related apparatus for determining employee work quality. This method can be applied to the manufacturing industry. The server first acquires production information completed by the target employee within a target timeframe, including production time, product type, and product difficulty level. Based on this production information, a first multi-objective optimization index is determined for each product difficulty level. This first multi-objective optimization index indicates the overall quality of the product within each difficulty level. Then, the product quality evaluation index Q is obtained by summing the product weight W corresponding to each product difficulty level and the first multi-objective optimization index corresponding to each product difficulty level. Finally, the work quality of the target employee is determined based on the product quality evaluation index Q. The work quality of the target employee is positively correlated with the magnitude of the product quality evaluation index Q. This application is applicable to application scenarios requiring evaluation of employee work quality, evaluation of employee production operation skills, and evaluation of machine learning performance, including but not limited to the aforementioned application scenarios.

[0033] The embodiments of this application will now be described with reference to the accompanying drawings.

[0034] The related apparatus provided in this application includes a server 10, and the composition structure of the server 10 in this application can be as follows: Figure 1As shown. Server 10 may include processor 110, memory 120, 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 the one or more programs 121 include instructions for performing any step in an embodiment of a data processing method for determining employee work quality. Alternatively, the one or more programs 121 shown may include instructions for a data processing step for determining employee work quality.

[0035] The communication interface 130 is used to support communication between the server 10 and other devices.

[0036] Processor 110 may include one or more processing cores. Processor 110 connects to various parts within server 10 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 120, and by calling data stored in memory 120. Optionally, 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 devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, units, and circuits described in conjunction with the embodiments disclosed in this application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0037] The memory 120 can be volatile memory or non-volatile memory, or may include both. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but 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 synchronous SDRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM). Memory 120 can be used to store instructions, programs, code, code sets, or instruction sets. Memory 120 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc. The data storage area may also store data created by the server 10 during use.

[0038] In a specific implementation, the processor 110 is used to execute any step performed by the server 10 in the following method embodiments, and when performing data transmission such as sending, it may selectively call the communication interface 130 to complete the corresponding operation.

[0039] It should be noted that the above schematic diagram of server 10 is only an example, and the actual number of components included may be more or less, and no single limitation is made here.

[0040] The following describes a data processing method for determining employee work quality, provided by an embodiment of this application.

[0041] Please see Figure 2 , Figure 2This is a flowchart illustrating a data processing method for determining employee work quality, provided in an embodiment of this application. The data processing method for determining employee work quality is applied to, for example... Figure 1 The server 10 mentioned above. For example... Figure 2 As shown, the data processing method for determining employee work quality includes:

[0042] Step 210: Obtain the production information completed by the target employee within the target time; wherein the production information includes production time, product type, and product difficulty level.

[0043] In practice, production time refers to the employees' working hours. Product type refers to the target product being produced, which can be indicated by the product's name, number, or other terms that identify that type of product. Product difficulty levels can be categorized according to actual needs; for example, product difficulty levels can be divided into three levels: low difficulty, medium difficulty, and high difficulty. See also... Figure 3 and Figure 4 For ease of server recording and storage, low difficulty can be recorded as 1, medium difficulty as 2, and high difficulty as 3. Alternatively, product difficulty levels can be divided into five levels, such as: low difficulty, lower-lower difficulty, medium difficulty, higher-higher difficulty, and high difficulty. Similarly, for ease of recording and storage, low difficulty can be recorded as 1, lower-lower difficulty as 2, medium difficulty as 3, higher-higher difficulty as 4, and high difficulty as 5. For ease of explanation, the following description will use the three levels of low, medium, and high product difficulty.

[0044] The product difficulty level corresponding to a product type can be set according to the production characteristics of that product type; that is, there is a one-to-one correspondence between product types and product difficulty levels. For example, the products that employee A will produce within the target timeframe are: Product A, Product B, Product C, Product D, Product E, Product F, Product G, Product H, and Product H. Among these, Product A, Product B, and Product C have a high difficulty level; Product D, Product E, and Product F have a medium difficulty level; and Product G, Product H, and Product H have a low difficulty level.

[0045] In one possible example, obtaining the production information completed by the target employee within a target time includes: receiving an evaluation request from a user device; determining the target time based on the evaluation request; obtaining all production work orders of the target employee within the target time based on the target time; and recording the product type and the product difficulty level corresponding to the product type in the production work order.

[0046] To facilitate information retrieval and improve the efficiency of evaluation result statistics, the product type and corresponding difficulty level of all produced products are recorded in the production work order. For ease of recording, storage, and retrieval, the production work order in this example is an electronic work order, and all production work orders can be uniformly stored in the work order time database for server retrieval of historical production work orders. It should be noted that production information may also include production tasks and the production batches corresponding to those tasks. In other embodiments, the production time, product type, product difficulty level, corresponding production task, and production batch information in the production information can be recorded in different locations, as long as the relevant production information can be retrieved for calculating the product quality evaluation index; no further restrictions are imposed here.

[0047] In this embodiment, the server can first receive the evaluation request sent by the user equipment and then calculate and evaluate the quality of the products produced by the target employee. The evaluation request sent by the user equipment may include a target time, so that the server can determine the target time based on the evaluation request. When obtaining the production information of the target employee within the target time, all production work orders of the target employee within the target time can be obtained directly by filtering the target employee and target time information. Alternatively, in other examples, all production work orders within the target time can be obtained from the work order time database based on the production time in the production work orders, and then the production work orders of the target employee corresponding to the target time can be obtained from all the obtained production work orders. Or, in other examples, all production work orders of the target employee can be obtained from the work order time database first, and then the production work orders of the target employee within the target time can be obtained from all the obtained production work orders.

[0048] In practice, the target time is typically a past period, such as the last six months or a year, which can be selected based on requirements. Within this target timeframe, the work order time database stores multiple production work orders for multiple employees. For an example, see [link to example]. Figure 3 , Figure 3 This is a partial example diagram of a work order and work hour database provided in an embodiment of the present invention, such as... Figure 3As shown, the production work orders recorded in the work order and work hour database for the target employee within the target time period can include: When the target time is from March 2022 to May 2022, and the target employee is employee A (employee number 108866), the production information recorded for all production work orders corresponding to employee A includes: Employee A worked from 9:00 AM to 11:00 AM on March 1, 2022, producing product A of high difficulty; Employee A worked from 3:00 PM to 5:00 PM on March 2, 2022, producing product D of medium difficulty; Employee A worked from 8:00 AM to 11:00 AM on May 3, 2022, producing product G of low difficulty, etc. Further, see... Figure 3 The production work order can also record information such as the production task corresponding to the type of product and the production time, and the production batch corresponding to the production task.

[0049] Step 220: Based on the production information, determine the first multi-objective optimization index of the product in each of the product difficulty levels.

[0050] The first multi-objective optimization index is used to indicate the overall quality of products within each product difficulty level.

[0051] Specifically, the first multi-objective optimization index is used to indicate the overall quality of products of the same product difficulty level within each product difficulty level. Within the same product difficulty level, multiple product types can be included. For example, among all products produced by employee A within a target time period, products A, B, and C belong to the high-difficulty category. In this case, the first multi-objective optimization index of the high-difficulty products is used to indicate the overall quality of high-difficulty products A, B, and C produced by employee A within the target time period.

[0052] In one possible example, determining the first multi-objective optimization index for all product types in each product difficulty level includes: obtaining a second multi-objective optimization index for the products produced in each production batch in all production tasks; wherein the second multi-objective optimization index is used to indicate the quality of the products produced in each production batch; classifying the second multi-objective optimization index according to the product difficulty level of the products produced in each production batch; and determining the mean of the second multi-objective optimization index for all production batches in each product difficulty level to obtain the first multi-objective optimization index.

[0053] Different production batches can be used to produce the same type of product or different types of products. Each production batch corresponds to a second multi-objective optimization index. The second multi-objective optimization index represents the quality of the product produced in its corresponding production batch. The second multi-objective optimization index can be recorded using a ten-point or percentage scale for easy calculation and evaluation. It is understood that using a percentage scale facilitates calculation and allows for a more accurate assessment of product quality. The second multi-objective optimization index is an inherent parameter of that production batch. The second multi-objective optimization indices for all production batches can be stored in the product quality database for unified storage and easy retrieval later. See [link to database]. Figure 4 When the server obtains all production work orders for the target employee within the target time period, it can retrieve the second multi-objective optimization index for that production batch from the product quality database based on the production batch recorded on the production work order.

[0054] For example, if the evaluation request asks for an assessment of employee A (employee ID 108866)'s work quality from March to May 2022, then in this request, the target period is March to May 2022, and the target employee is employee A (employee ID 108866). The server can obtain corresponding production information based on this evaluation request, such as... Figure 3 and Figure 4 As shown in the figure, employee A participated in the production of 18 batches of products within the target time. Among them, 6 production batches were for producing products of high difficulty, 7 production batches were for producing products of medium difficulty, and 5 production batches were for producing products of low difficulty. The second multi-objective optimization index of each batch of products is as follows: Figure 4 As shown. The first multi-objective optimization index corresponding to a product of high difficulty is denoted as Ph, the first multi-objective optimization index corresponding to a product of medium difficulty is denoted as Pm, and the first multi-objective optimization index corresponding to a product of low difficulty is denoted as Pl. In this example, Ph = (86+82+84+88+80+81) / 6 = 83.5, Pm = (92+84+85+85+84+85+86) / 7 = 85.9, and Pl = (92+8+94+90+96) / 5 = 92.

[0055] In this embodiment, the first multi-objective optimization index is determined by averaging the second multi-objective optimization index of all production batches at the same product difficulty level. This can improve the accuracy of the first multi-objective optimization index in assessing the product production quality of its corresponding product difficulty level, and avoid the problem that the first multi-objective optimization index may be inaccurate in assessing product quality due to a small sample size of some product types and a large quality gap with other product types.

[0056] In one possible example, determining the first multi-objective optimization index for all product types across each product difficulty level includes: obtaining a second multi-objective optimization index for the products produced in each production batch across all production tasks; wherein the second multi-objective optimization index indicates the quality of the products produced in each production batch; classifying the second multi-objective optimization index according to the product type of the products produced in each production batch; determining the mean of the second multi-objective optimization indices for all production batches of the same product type to obtain a third multi-objective optimization index corresponding to the product type; wherein the third multi-objective optimization index indicates the overall 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; and determining the mean of the third multi-objective optimization index for all product types across each product difficulty level to obtain the first multi-objective optimization index.

[0057] The third multi-objective optimization index is used to indicate the overall quality of products of the same type across all production batches in all production tasks. A production task may include at least one production batch; when there are at least two production batches, these batches can be used to produce the same type of product. Conversely, production batches in different production tasks may be used to produce the same type of product or different types of product. For example, production task number 20220100 includes two production batches numbered 20220101 and 20220102; and production task number 20220200 includes three production batches numbered 20220201, 20220202, and 20220203. Among them, two production batches with production batch numbers 20220101 and 20220102 are used to produce product A, and three production batches with production batch numbers 20220201, 20220202 and 20220203 are used to produce product B. In this case, the third multi-objective optimization index of product A is used to indicate the overall quality of the types of products produced by the target employee A within the target time. The third multi-objective optimization index of product B is used to indicate the overall quality of the types of products produced by the target employee A within the target time.

[0058] For example, if the evaluation request asks for an assessment of employee A (employee ID 108866)'s work quality from March to May 2022, then in this request, the target period is March to May 2022, and the target employee is employee A (employee ID 108866). The server can obtain corresponding production information based on this evaluation request, such as... Figure 3 and Figure 4 As shown in the figure, employee A participated in the production of 18 batches of products within the target time. The second multi-objective optimization index of each batch of products produced is as follows: Figure 4 As shown, the server can classify products into nine categories, from Product A to Product I, based on the product category name. By determining the average of the second multi-objective optimization indices for the same product type, a third multi-objective optimization index corresponding to the product type can be obtained. For example, the third multi-objective optimization index for Product A (denoted as PA) is the average of the second multi-objective optimization index (denoted as PA1) corresponding to production batch 20220101 and the second multi-objective optimization index (denoted as PA2) corresponding to production batch 20220102, i.e., PA = (PA1 + PA2) / 2 = (86 + 82) / 2 = 84. Similarly, the third multi-objective optimization index (denoted as PB) for product B is 84, for product C it is 81, for product D it is 88, for product E it is 85, for product F it is 85, for product G it is 92, for product H it is 91, and for product I it is 93. Then, the server can classify the nine product categories according to their difficulty level: A, B, and C are classified as high-difficulty products; D, E, and F as medium-difficulty products; and G, H, and I as low-difficulty products. Finally, the average of the third multi-objective optimization indices within each product difficulty level is calculated to determine the first multi-objective optimization index corresponding to each product difficulty level. For example, the first multi-objective optimization index (denoted as Ph) for a product of high difficulty is equal to the average of the third multi-objective optimization indices of product A, product B, and product C, which is Ph = (PA + PB + PC) / 3 = (84 + 84 + 81) / 3 = 83. Similarly, the first multi-objective optimization index Pm for a product of medium difficulty is Pm = (88 + 85 + 85) / 3 = 86; and the first multi-objective optimization index Pl for a product of low difficulty is Pl = (92 + 91 + 93) = 92.

[0059] It is understandable that grouping production batches of products of the same type together and averaging the second multi-objective optimization index of all batches within the same product type to obtain the third multi-objective optimization index can improve the accuracy and stability of the third multi-objective optimization index in assessing the quality of products of the same type. The target employee may produce multiple product types within the target timeframe, each corresponding to a different product difficulty level. Averaging the third multi-objective optimization index of all product types within the same product difficulty level to obtain the first multi-objective optimization index can further improve the accuracy and stability of the first multi-objective optimization index in assessing the quality of products of the same difficulty level, thereby improving and ensuring the reliability of the final product quality evaluation index.

[0060] Based on the above embodiments, in order to further improve the accuracy of the first multi-objective optimization index in evaluating the quality of manufactured products, when calculating the third multi-objective optimization index, data for product types with fewer samples can be removed; that is, data for that product type is not used to evaluate the work quality of target employees. Specifically, if the sample size for a certain product type is less than a preset number, then data related to that product type is removed. Here, the sample size is the number of production batches, and the preset number can be two, three, or more, which can be set according to actual needs and is not further limited here.

[0061] As can be seen, the solution in this embodiment can not only obtain the first multi-objective optimization index, but also the third multi-objective optimization index obtained in the process of obtaining the first multi-objective optimization index can be used to evaluate the ability of the target employee to produce products of this type, thereby improving data utilization and reducing the data processing and data storage pressure on the server.

[0062] Step 230: Sum the product of the weight ratio W corresponding to each product difficulty level and the first multi-objective optimization index corresponding to each product difficulty level to obtain the product quality evaluation index Q.

[0063] It is understandable that this step involves weighting the product using a first multi-objective optimization index corresponding to different product difficulty levels. This helps to improve the reliability and accuracy of the overall quality assessment of the products produced by the target employees.

[0064] In one possible embodiment, the weight percentage W corresponding to each product difficulty level is positively correlated with the level of product difficulty; wherein, the rating criteria for the product difficulty level include at least one of the following: the soundness of the work specification standards, the number of steps in the process flow, the measurability of product process parameters, and the degree of automation of the equipment.

[0065] In this system, all products produced by employees at different difficulty levels can be calculated using a uniform weighting, enhancing the fairness and reliability of comparing employee production skills through a product quality evaluation index. The weighting of different product difficulty levels can be set according to actual circumstances; specifically, it can be based on the difficulty of product production, with higher difficulty levels receiving higher weightings. This is because higher production difficulty demands higher levels of employee production skills. Therefore, setting the weighting of each product difficulty level proportionally to its difficulty level allows for a more accurate evaluation of employee production skills. For example, the weighting of each product difficulty level could be set as follows: 60% for high difficulty, 30% for medium difficulty, and 10% for low difficulty. Of course, in the example, if the production difficulty of a high-difficulty product is much greater than that of a medium-difficulty or low-difficulty product, then the weighting of the high-difficulty product can be increased. For example, the weighting of each product difficulty level can also be set as follows: 75% for high difficulty, 20% for medium difficulty, and 5% for low difficulty.

[0066] It is understandable that factors influencing a product's difficulty level include the completeness of operational standards, the number of steps in the production process, the measurability of process parameters, and the degree of equipment automation. Operational standards refer to the standardized requirements and regulations established to standardize employee operating procedures for various production tasks. Employees can produce qualified products according to these standards; therefore, the more complete the operational standards, the lower the difficulty of product production, and thus the lower the product difficulty level. The number of steps in the production process determines the complexity of the production process; more steps mean higher complexity, and thus a higher product difficulty level. Process parameters include the quality parameters of intermediate materials and the final product, as well as relevant process parameters. Higher measurability of process parameters indicates higher controllability of the production process, and thus a lower product difficulty level. When the measurability of process parameters is low, more employee experience is required for judgment, resulting in a higher product difficulty level. A higher degree of equipment automation means less human influence during production, and therefore a lower product difficulty level.

[0067] In one possible example, the product difficulty level is divided according to Table 1 below.

[0068]

[0069] Table 1

[0070] In practice, when classifying the difficulty level of a product, the products of each product category can be scored according to Table 1 above. If the product scores 4 to 6 points, it is a product of low difficulty; if the product scores 7 to 9 points, it is a product of medium difficulty; and if the product scores 10 to 12 points, it is a product of high difficulty.

[0071] It is understandable that the influence of various factors on the product difficulty level is not uniform. For example, the influence of the degree of automation of the equipment and the measurability of the product process parameters is greater than the influence of the number of steps in the process flow, and far greater than the influence of the completeness of the work specifications and standards. Furthermore, to improve the accuracy of product difficulty level classification, each influencing factor can be weighted according to its influence on the product difficulty level when calculating the product score. For example, the weights of each influencing factor can be set as follows: the degree of automation of the equipment is 35%, the measurability of the product process parameters is 35%, the number of steps in the process flow is 20%, and the completeness of the work specifications and standards is 10%. In this case, if the product score falls within the range...

[0072]

[0073] Then the product is of low difficulty; if the product's score is within the range

[0074]

[0075] This product is of medium difficulty; the score for this product falls within the range.

[0076]

[0077] Therefore, the product is classified as a high-difficulty product. For example, if product A scores 2 points for the completeness of its work specifications, 2 points for the number of steps in its process flow, 3 points for the measurability of its process parameters, and 3 points for the degree of automation of its equipment, then product A's total score is: 2*10% + 2*20% + 3*35% + 3*35% = 2.7. As can be seen, product A is a high-difficulty product.

[0078] Alternatively, when classifying the difficulty level of a product, the server can store all scores for high, medium, and low difficulty levels, and then categorize the product difficulty level according to these scores. This allows the server to quickly determine the corresponding difficulty level of the target product based on its score, thus improving the confirmation speed. For example, a product with a score of 4, 5, or 6 is considered low difficulty; a product with a score of 7, 8, or 9 is considered medium difficulty; and a product with a score of 10, 11, or 12 is considered high difficulty. It can be understood that the product difficulty level determination method described in this example can also be achieved by weighting each influencing factor according to its strength and storing all possible scores after weighting. Further details are omitted here.

[0079] Step 240: Determine the work quality of the target employee based on the product quality evaluation index Q; wherein the work quality of the target employee is positively correlated with the magnitude of the product quality evaluation index Q.

[0080] It is understandable that the product quality evaluation index of all employees within the target time can be obtained according to the above steps. The higher the product quality evaluation index of the products produced by the target employee within the target time, the higher the overall quality level of the products produced by the target employee within the target time, that is, the high work quality of the target employee within the target time.

[0081] In practical implementation, to facilitate comparison of the operational skill levels of all employees within the same target timeframe, the product quality evaluation indices of all employees within the same target timeframe can be ranked sequentially. The higher an employee's ranking, the higher their production quality among all employees. This ranking method allows for a more intuitive comparison of the operational skill levels of all employees. Furthermore, the factory can use this ranking result to categorize multiple employees into different levels, thereby quickly identifying employees with satisfactory work quality performance and facilitating rewards and recognition based on the categorized list. For example, when the product quality evaluation indices of pre-selected rewardable employees are all higher than preset values, i.e., the pre-selected rewardable employees meet the reward criteria, the employees can be rewarded according to their level. For instance, if there are eleven reward slots, the product quality evaluation indices of the top eleven employees can be compared with the factory's expected preset values. If all eleven employees meet the factory's expected target, then these eleven employees can be rewarded. When rewarding these eleven employees, the rewards can be tiered according to their ranking. For example, the top three employees can be placed in the first-prize list, the fourth to sixth in the second-prize list, and the seventh to eleventh in the third-prize list.

[0082] The first multi-objective optimization index can not only be used to calculate the product quality evaluation index to comprehensively evaluate the quality of products produced by target employees within the target time, but also to determine what level of product difficulty the target employees are suitable for producing.

[0083] In one possible example, the method further includes: obtaining preset parameters for each of the product difficulty levels; comparing the preset parameters for each of the product difficulty levels with the first multi-objective optimization index corresponding to the product difficulty level, and obtaining a comparison result; determining the adaptation difficulty evaluation index D corresponding to the target employee based on the comparison result; and determining the product difficulty level for which the target employee is adapted to production based on the adaptation difficulty evaluation index D.

[0084] In this example, for the products produced by the target employee within the target time, it can be determined whether the target employee is suitable for producing products of that difficulty level by comparing the first multi-objective optimization index corresponding to different product difficulty levels with their corresponding preset parameters. In specific implementation, if the first objective index of a product of a certain difficulty level produced by the target employee is greater than or equal to the preset parameter corresponding to that product difficulty level, then the target employee is suitable for producing products of that difficulty level. The preset parameters are reference values ​​pre-set for each product difficulty level, and can be specifically set according to the factory's production quality requirements for different product difficulty levels. For example, if the first multi-objective optimization index uses a percentage system, then the preset parameters for high-difficulty products, medium-difficulty products, and low-difficulty products can all be set to 80 points; that is, 80 points is the passing score for judging whether an employee possesses the corresponding production operation skills.

[0085] It can be understood that the operational difficulty of producing high-difficulty products is greater than that of producing medium-difficulty products, which is greater than that of producing low-difficulty products. Therefore, employees skilled in producing higher-difficulty products typically also possess the skills to produce lower-difficulty products. Thus, when using preset parameters to compare and determine which difficulty level an employee can produce, a downward compatibility approach can be adopted. Specifically, if the first multi-objective optimization index for high-difficulty products, medium-difficulty products, and low-difficulty products produced by the target employee are all greater than or equal to their corresponding preset parameters, then the target employee's suitability difficulty evaluation index can be marked as Level 1 (here, it can be denoted as D=1 for easy recording and storage). A suitability difficulty evaluation index marked as Level 1 means that the target employee is suitable for producing products of all difficulty levels. When the first multi-objective optimization index for a high-difficulty product produced by a target employee is less than its corresponding preset parameter, if the first multi-objective optimization index for a medium-difficulty product and the first multi-objective optimization index for a low-difficulty product produced by the target employee are both greater than or equal to their respective preset parameters, then the suitability evaluation index for that target employee can be marked as Level 2 (here, D=2). A Level 2 suitability evaluation index means that the target employee is suited for producing medium- and low-difficulty products. When the first multi-objective optimization index for a high-difficulty product and the first multi-objective optimization index for a medium-difficulty product produced by a target employee are both less than their respective preset parameters, if the first multi-objective optimization index for a low-difficulty product produced by the target employee is greater than or equal to its corresponding preset parameter, then the suitability evaluation index for that target employee can be marked as Level 3 (here, D=3). A Level 3 suitability evaluation index means that the target employee is suited for producing low-difficulty products.

[0086] In one possible embodiment, the method further includes: arranging the first multi-objective optimization indices corresponding to each of the product difficulty levels in descending order; determining the product difficulty level that the target employee is suited to produce based on the arrangement order; wherein, the product difficulty level corresponding to the first multi-objective optimization index that appears earlier in the arrangement order has a higher degree of suitability with the target employee.

[0087] In this example, among the products produced by the target employee within the target time, the first multi-objective optimization index corresponding to different product difficulty levels is arranged in order. This makes it easier and more intuitive to identify the target employee's skill level in producing products of different difficulty levels, which is helpful in determining which level of product difficulty the target employee is more suitable for producing. This allows for more appropriate work assignments to the target employee in subsequent tasks, thereby improving the quality and efficiency of product production to a greater extent. For example, if the target employee is employee B, the first multi-objective optimization index for employee B producing high-difficulty products is 83, the first multi-objective optimization index for employee A producing medium-difficulty products is 86, and the first multi-objective optimization index for employee A producing low-difficulty products is 92, with the order being 92 > 86 > 83. Therefore, the quality of low-difficulty products produced by employee A within the target time is higher than the quality of medium-difficulty products, which is higher than the quality of high-difficulty products. Thus, employee A is more suitable for producing low-difficulty products, and employee B can be assigned more tasks related to producing low-difficulty products in subsequent task assignments.

[0088] Optionally, data such as the product quality evaluation index, adaptation difficulty evaluation index, and the ranking of the first multi-objective optimization index can be stored in the employee performance skill tag database. The data in the product quality database, work order and work hour database, and employee performance skill tag database can be stored in different blockchain nodes. For example, data from the product quality database can be stored on the first blockchain node, data from the work order and work hour database on the second blockchain node, and data from the employee performance skill tag database on the third blockchain node. The product quality evaluation index, adaptation difficulty evaluation index, and ranking of the first multi-objective optimization index generated by the server can be stored in the corresponding blockchain nodes. When the server needs to obtain data such as the target employee's production work order within the target time and the second multi-objective optimization index of each production batch in the production work order during the calculation of the product quality evaluation index, the server needs to access the corresponding blockchain node using a key to obtain the relevant data. For example, when the second multi-objective optimization index needs to be obtained, the server needs to access the first blockchain node using the first key. It is understood that the data mentioned above are all important production information of the factory, which are related to the factory's operation, production and management. Therefore, only authorized managers have the keys used to obtain data in different blockchain nodes. That is, only authorized managers have the right to access blockchain nodes to obtain or call their corresponding stored data, thereby enhancing the confidentiality of the data and the security of the system.

[0089] This application can divide the server into functional units based on the above method examples. For example, each function can be divided into its own functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0090] Consistent with the embodiments shown above, Figure 5A This is a functional unit block diagram of a data processing device 30 for determining employee work quality, provided in an embodiment of this application. This data processing device 30 for determining employee work quality can be applied to, for example... Figure 1 The server shown is as follows: Figure 5A As shown, the data processing device 30 for determining employee work quality includes:

[0091] The acquisition unit 310 is used to acquire production information completed by the target employee within a target time; wherein, the production information includes production time, product type, and product difficulty level;

[0092] The processing unit 320 is configured to determine a first multi-objective optimization index for each product difficulty level based on the production information; wherein the first multi-objective optimization index is used to indicate the overall quality of the product within each difficulty level; the processing unit 320 is also configured to sum the product of the weight ratio W corresponding to each product difficulty level and the first multi-objective optimization index corresponding to each product difficulty level to obtain the product quality evaluation index Q.

[0093] The determining unit 330 is used to determine the work quality of the target employee based on the product quality evaluation index Q; wherein the work quality of the target employee is positively correlated with the magnitude of the product quality evaluation index Q.

[0094] In one possible example, regarding the determination of the first multi-objective optimization index for all product types in each of the product difficulty levels, the processing unit 320 is specifically configured to: obtain a second multi-objective optimization index for the products produced in each production batch in all the production tasks; wherein the second multi-objective optimization index is used to indicate the quality of the products produced in each of the production batches; classify the second multi-objective optimization index according to the product difficulty level of the products produced in each production batch; and determine the mean of the second multi-objective optimization index for all production batches in each of the product difficulty levels to obtain the first multi-objective optimization index.

[0095] In one possible example, the production information further includes production tasks and production batches corresponding to the production tasks. Regarding obtaining a third multi-objective optimization index for the same product type based on the production information, the processing unit 320 is specifically configured to: obtain a second multi-objective optimization index for the products produced by each production batch in all the production tasks; wherein the second multi-objective optimization index is used to indicate the quality of the products produced by each production batch; classify the second multi-objective optimization index according to the product type of the products produced by the production batch; determine the mean of the second multi-objective optimization indices corresponding to all production batches of the same product type to obtain a third multi-objective optimization index corresponding to the product type; wherein the third multi-objective optimization index is used to indicate the overall quality of products of the same product type; classify the third multi-objective optimization index according to the product difficulty level corresponding to the product type; determine the mean of the third multi-objective optimization index for all product types in each product difficulty level to obtain a first multi-objective optimization index.

[0096] In one possible example, regarding the acquisition of production information completed by the target employee within a target time, the acquisition unit 310 is specifically configured to: receive an evaluation request from a user device; determine the target time based on the evaluation request; acquire all production work orders of the target employee within the target time based on the target time; and record the production information in the production work orders.

[0097] In one possible example, the processing unit 320 specifically includes: the weight ratio W corresponding to each of the product difficulty levels is positively correlated with the level of the product difficulty; wherein, the rating criteria for the product difficulty level include at least one of the following: the soundness of the work specification standard, the number of steps in the process flow, the measurability of the product process parameters, and the degree of automation of the equipment.

[0098] In one possible example, the acquisition unit 310 is specifically used to: acquire preset parameters for each of the product difficulty levels. The processing unit 320 is specifically used to: compare the preset parameters for each of the product difficulty levels with the first multi-objective optimization index corresponding to the product difficulty level, and obtain a comparison result; determine the adaptation difficulty evaluation index D corresponding to the target employee based on the comparison result; and determine the product difficulty level for which the target employee is adapted to production based on the adaptation difficulty evaluation index D.

[0099] In one possible example, the processing unit 320 is specifically used to: arrange the first multi-objective optimization indices corresponding to each of the product difficulty levels in descending order; determine the product difficulty level that the target employee is suited to produce according to the arrangement order; wherein, the product difficulty level corresponding to the first multi-objective optimization index that appears earlier in the arrangement order is more suitable for the target employee.

[0100] It is understood that since the method embodiments and the device embodiments are different presentations of the same technical concept, the content of the method embodiment section in this application should be adapted to the device embodiment section in a synchronous manner, and will not be repeated here.

[0101] When using integrated units, the functional unit composition block diagram of the data processing device for determining employee work quality provided in the embodiments of this application is as follows: Figure 5B As shown. In Figure 5B The data processing device 30 for determining employee work quality includes a communication module 340 and a processing module 350. The processing module 350 controls and manages the actions of the data processing device 30, such as executing the steps of the acquisition unit 310, processing unit 320, and determination unit 330, and / or other processes using the techniques described herein. The communication module 340 supports interaction between the data processing device 30 and other devices. Figure 5B As shown, the data processing device 30 for determining employee work quality may further include a storage module 360, which stores program code and data of the data processing device 30 for determining employee work quality.

[0102] The processing module 350 can be a processor or controller, such as a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication module 340 can be a transceiver, RF circuitry, or a communication interface, etc. The storage module 360 ​​can be a memory.

[0103] All relevant content in each scenario involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here. The data processing device 30 for determining employee work quality described above can all execute the above... Figure 2 The steps performed by the server in the data processing method shown for determining employee work quality.

[0104] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0105] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.

[0106] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0107] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0108] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0109] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0110] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing module, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0111] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, electronic device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0112] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0113] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A data processing method for determining the work quality of an employee, characterized in that, The method comprises: obtaining production information of a target employee 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 products of each product difficulty level according to the production information; the first multi-objective optimization index is used to indicate the comprehensive quality of products in each product difficulty level; summing the product of the weight proportion 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; wherein the weight proportion W corresponding to each product difficulty level is positively correlated with the high and low of the product difficulty level; the same product difficulty level includes multiple product types; determining the work quality of the target employee according to the product quality evaluation index Q; wherein the work quality of the target employee is positively correlated with the size of the product quality evaluation index Q; wherein the production information further comprises production tasks and production batches corresponding to the production tasks, and the determination of the first multi-objective optimization index of products in each product difficulty level comprises: obtaining a second multi-objective optimization index of products corresponding to each production batch in all production tasks; wherein the second multi-objective optimization index is used to indicate the quality of products corresponding to each production batch; classifying the second multi-objective optimization index according to the product difficulty level of products corresponding to the production batch; determining the average value of the second multi-objective optimization index corresponding to all production batches in each product difficulty level to obtain the first multi-objective optimization index; or the production information further comprises production tasks and production batches corresponding to the production tasks, and the determination of the first multi-objective optimization index of products in each product difficulty level comprises: obtaining a second multi-objective optimization index of products corresponding to each production batch in all production tasks; wherein the second multi-objective optimization index is used to indicate the quality of products corresponding to each production batch; classifying the second multi-objective optimization index according to the product type of products corresponding to the production batch; determining the average value of the second multi-objective optimization index corresponding to all production batches in the same product type to obtain a third multi-objective optimization index corresponding to the product type; wherein the third multi-objective optimization index is used to indicate the comprehensive 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 average value of the third multi-objective optimization index of all product types in each product difficulty level to obtain the first multi-objective optimization index.

2. The method of claim 1, wherein, The method further comprises: receiving an evaluation request of a user device; determining the target time according to the evaluation request. According to the target time, all production work orders of the target employee within the target time are obtained; the product type and the product difficulty level corresponding to the product type are recorded in the production work order.

3. The method of claim 1, wherein, The rating standard of the product difficulty level includes at least one of the following: the soundness of the operation specification standard, the number of steps of the process flow, the measurability of the product process parameter, and the automation degree of the equipment.

4. The method of claim 1, wherein, The method further comprises: obtaining preset parameters of each product difficulty level; comparing the preset parameters of each product difficulty level with the first multi-objective optimization index corresponding to the product difficulty level, and obtaining a comparison result; determining an adaptive difficulty evaluation index D corresponding to the target employee according to the comparison result; determining the product difficulty level that the target employee is suitable for producing according to the adaptive difficulty evaluation index D.

5. The method of claim 4, wherein, The method further comprises: arranging the first multi-objective optimization index corresponding to each product difficulty level in order from high to low; determining the product difficulty level that the target employee is suitable for producing according to the arrangement order; wherein the product difficulty level corresponding to the first multi-objective optimization index with a higher arrangement order has a higher degree of adaptation to the target employee.

6. A data processing device for determining the work quality of an employee, characterized in that comprises: an obtaining unit, configured to obtain production information completed by a target employee within a target time; wherein the production information includes production time, product type, and product difficulty level; a processing unit, 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 multi-objective optimization index is used to indicate the comprehensive quality of the product in each difficulty level; the processing unit is further configured to sum the product of the weight proportion 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; wherein the weight proportion W corresponding to each product difficulty level is positively correlated with the high and low of the product difficulty level; and the same product difficulty level includes multiple product types; a determining unit, 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 employee is positively correlated with the size of the product quality evaluation index Q; wherein the production information further includes production tasks and production batches corresponding to the production tasks, and the determination of the first multi-objective optimization index of the product in each product difficulty level comprises: obtaining a second multi-objective optimization index of the product corresponding to each production batch in all production tasks; wherein the second multi-objective optimization index is used to indicate the quality of the product corresponding to each production batch; classifying the second multi-objective optimization index according to the product difficulty level of the product corresponding to the production batch; determining the mean value of the second multi-objective optimization index corresponding to all production batches in each product difficulty level to obtain the first multi-objective optimization index; or The production information also includes production tasks and production batches corresponding to the production tasks. Determining the first multi-objective optimization index for each of the product difficulty levels includes: Obtain a second multi-objective optimization index for the products produced in each production batch across all production tasks; wherein the second multi-objective optimization index is used to indicate the quality of the products produced in each production batch. The second multi-objective optimization index is classified according to the product type of the products produced in the corresponding production batch; The mean of the second multi-objective optimization index corresponding to all production batches of the same product type is determined to obtain a third multi-objective optimization index corresponding to the product type; wherein, the third multi-objective optimization index is used to indicate the overall quality of products of the same product type; The third multi-objective optimization index is classified according to the product difficulty level corresponding to the product type; The mean of the third multi-objective optimization index for all product types in each product difficulty level is determined to obtain the first multi-objective optimization index.

7. A server, characterized by The server includes 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 include instructions for performing steps in the method as claimed in any one of claims 1 to 5, or the programs include instructions for performing steps in the apparatus of claim 6.

8. A computer readable storage medium or computer program product, characterized in that, A computer-readable storage medium stores a computer program for electronic data interchange, wherein the computer program causes a computer to perform the method as described in any one of claims 1 to 5; Alternatively, the computer program product causes the computer to perform the method as described in any one of claims 1 to 5.

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