Automatic accounting method and device for attendance personnel, electronic equipment and storage medium

Abstract

The invention relates to the technical field of production management, in particular to an automatic accounting method and device for attendance personnel, electronic equipment and a storage medium, and the method comprises the steps: determining the part assembly standard man-hour and test man-hour in a to-be-produced order, and the number of product orders in each piece of wave information; respectively obtaining the standard man-hour of the whole machine and the man-hour demand of the order machine in each piece of wave information, and further obtaining the total man-hour demand of assembly according to the man-hour demands of all the wave information; based on the number of the to-be-tested order machines and the test man-hours, obtaining a total test man-hour demand of the to-be-tested order machines; and then based on the determined theoretical assembly personnel number, the theoretical test personnel number and the assembly per capita skill level working efficiency coefficient of each order machine model, obtaining an assembly personnel attendance accounting result and a test personnel attendance accounting result, thereby solving the problem that the system logic of attendance personnel accounting is lacked; and the working indexes are not matched with the attendance personnel, so that the production efficiency and the production cost are influenced, and the like.

Description

Technical Field

[0001] The present application relates to the technical field of production management, and in particular to an automatic accounting method, device, electronic equipment and storage medium for attendance personnel. Background Art

[0002] In modern manufacturing and service industries, with the changes in the market environment and the advancement of technology, users' demands for products and services are becoming increasingly diversified. Therefore, reasonable arrangement of employee attendance and flexible adjustment of staffing are key factors in ensuring operational production efficiency, controlling labor costs and meeting customer needs.

[0003] In order to adjust the attendance staff scheduling strategy, employee attendance is arranged according to the operation production plan. However, there is a lack of systematic logical support in the attendance scheduling process, which leads to chaotic attendance management and a mismatch between operation indicators and attendance staff, thus affecting production efficiency, order delivery and production costs.

[0004] In the relevant technologies for dealing with the problem of attendance personnel arrangement, the attendance manpower often exceeds the current operating indicators due to redundant attendance personnel, thereby increasing the labor cost, or due to lack of accurate calculation, the attendance manpower is lower than the current operating indicators, and the operating tasks cannot be completed. Therefore, how to effectively and reasonably arrange attendance personnel has become a difficult problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides an automatic accounting method, device, electronic device and storage medium for attendance personnel, so as to solve the problems caused by lack of system logic for attendance personnel accounting, which leads to chaotic attendance management, mismatch between work indicators and attendance personnel, and further affects production efficiency, order delivery and production costs. By combining the MES (Manufacturing Execution System) system with the DIAG (Diagnostic System) system, the production schedule for the next attendance day is calculated in advance every day, so as to automatically calculate the attendance accounting results of the attendance personnel required for each wave on that day, so as to make reasonable allocation based on the accounting results, thereby improving production efficiency and reducing costs.

[0006] The first embodiment of the present application provides a method for automatically calculating attendance of personnel, comprising the following steps:

[0007] Determine the standard assembly time, test time and product order quantity in each wave information for each order machine model in the production order to be produced;

[0008] The standard working hours for the whole machine of each order machine model are obtained according to the standard working hours for assembling the parts of each order machine model, and the working hours requirement for the order machines in each wave information is calculated based on the product order quantity in each wave information and the standard working hours for the whole machine of each order machine model, and the total assembly working hours requirement for all order machines is obtained according to the working hours requirement for the order machines in all wave information;

[0009] Determine the number of order machines to be tested in the machine resource pool to be tested, and obtain the total test man-hour requirement of the order machines to be tested based on the number of order machines to be tested and the test man-hour of each order machine model;

[0010] Determine the theoretical number of assemblers for each order machine model, the work efficiency coefficient of the average skill level of assemblers, and the theoretical number of testers for each order machine model, and obtain the attendance calculation results of the assemblers based on the theoretical number of assemblers, the work efficiency coefficient of the average skill level, and the total assembly working time requirement, and at the same time, obtain the attendance calculation results of the testers based on the theoretical number of testers and the total testing working time requirement.

[0011] Through the above technical solution, by combining the MES system with the DIAG system, the production schedule for the next attendance day can be calculated in advance every day, thereby automatically calculating the attendance results of the attendance personnel required for each wave on that day, and making reasonable allocations based on the calculation results, thereby improving production efficiency and reducing costs.

[0012] According to one embodiment of the present application, before determining the standard man-hours for assembling components of each order machine model in the to-be-produced order, the method further includes:

[0013] Based on the order to be produced, at least one order information and at least one order machine model are determined, and each order information is formatted and cleaned, and duplicate order information and abnormal order information in each order information are removed to obtain at least one order information to be assembled;

[0014] Obtaining a target order type, and building a complexity evaluation model of an order machine model based on the target order type, the assembly process of each order machine model, and each to-be-assembled order information;

[0015] Utilizing the complexity assessment model, the complexity coefficient of each order machine model is configured respectively based on each order machine model, and utilizing a preset optimization algorithm, order scheduling is performed based on the complexity coefficient of each order machine model and each order information to be assembled, thereby generating wave information of the order information to be assembled.

[0016] Through the above technical solution, the consistency of information can be ensured by formatting and cleaning the order information. The complexity of each model can be accurately calculated by building a complexity assessment model, which can better match production resources and avoid waste or shortage of resources. By using the preset optimization algorithm for order scheduling, the production sequence can be reasonably arranged, the existing resources can be maximized, the production cycle can be shortened, and the output rate can be improved.

[0017] According to one embodiment of the present application, determining the standard man-hours for assembling components of each order machine model in the to-be-produced order includes:

[0018] Determine the component assembly time of each ordered machine model based on the preset product standard and the assembly process of the ordered machine model;

[0019] The component assembly time and the preset debugging time are used to determine the standard component assembly time for each ordered machine model.

[0020] Through the above technical solution, by calculating the standard working hours for assembling product components, the accuracy of production planning can be improved and the effective use of resources can be ensured.

[0021] According to one embodiment of the present application, after determining the standard man-hours for assembling components of each order machine model, the method further includes:

[0022] The standard working hours for assembling components of each order machine model are stored in a preset working hours database, and are respectively associated with the order information corresponding to the standard working hours for assembling components of each order machine model;

[0023] When the standard working hours for component assembly of each order machine model changes, the working hours are updated based on a preset first working hour updating mechanism.

[0024] Through the above technical solution, through the update mechanism, the working hours are updated when the standard working hours for component assembly of each order machine model change, so as to continuously improve the accuracy and efficiency of production management, so as to avoid the situation of excessive or insufficient allocation of attendance personnel due to working hour errors.

[0025] According to one embodiment of the present application, before determining the test man-hours for each order machine model in the to-be-produced order, the method further includes:

[0026] Obtaining a first configuration parameter of at least one current order machine model and a second configuration parameter of at least one historical order machine model;

[0027] Identify the target configuration parameter that meets the similarity condition in each second configuration parameter, predict the aging completion time of each current order machine model based on the target configuration parameter and a preset correction coefficient of the current order machine model, and obtain the aging completion prediction time of each current order machine model;

[0028] Building a machine aging prediction model based on the target configuration parameters and the aging completion time of each current order machine model, and obtaining the aging completion time of a new order machine model based on the machine aging prediction model;

[0029] Generate aging test information of at least one order machine model based on a preset database management system, and build an aging database of the order machine model according to the aging test information, wherein the aging test information includes an order machine model configuration table, an aging test record table, and an aging completion schedule;

[0030] The aging test record table is scanned based on a preset scanning time, and according to the current scanning time and the predicted completion time of the aging, all order machine models that will complete the aging within the next attendance day are predicted and screened to obtain the completed aging records of all order machine models within the next attendance day, and a machine resource pool to be tested is constructed based on the information of all order machines and the completed aging records within the next attendance day, and all the order machine models to be tested in the machine resource pool to be tested are associated with the test working hours of the order machine models to be tested.

[0031] Through the above technical solution, the DIAG system performs aging tests on the ordered machine models. By accurately predicting the aging completion time, subsequent processes (such as inspection, packaging, etc.) can be arranged in advance to ensure the smooth operation of the production line.

[0032] According to one embodiment of the present application, determining the test man-hours for each order machine model in the to-be-produced order includes:

[0033] Get the product inspection operation consumption time and product packaging operation consumption time of each order machine model in the production order to be produced;

[0034] Calculating an average inspection operation time of the product based on the consumption time of each product inspection operation, and calculating an average packaging operation time of the product based on the consumption time of each product packaging operation;

[0035] The standard inspection working hours for each order machine model are determined based on the average inspection operation time of the product and the preset debugging time. The standard packaging working hours for each order machine model are determined based on the average packaging operation time of the product and the preset debugging time. The standard inspection working hours and the standard packaging working hours are stored in a preset working hour database and associated with each order machine model.

[0036] Through the above technical solution, by calculating the standard inspection working hours and the standard packaging working hours, it is possible to formulate production plans more accurately, arrange production cycles reasonably, and ensure the effective use of resources.

[0037] According to one embodiment of the present application, after the inspection standard working hours and the packaging standard working hours are stored in a preset working hour database and associated with each order machine model, the method further includes:

[0038] When it is detected that the inspection standard working hours of each order machine model or the packaging standard working hours of each order machine model changes, the working hours are updated based on a preset second working hour updating mechanism.

[0039] Through the above technical solution, through the update mechanism, when the inspection standard working hours or packaging standard working hours of each order machine model change, the working hours are updated, so as to continuously improve the accuracy and efficiency of production management, so as to avoid the over-allocation or under-allocation of attendance personnel due to working hour errors.

[0040] According to one embodiment of the present application, after obtaining the attendance accounting result of the assemblers based on the theoretical number of assemblers, the per capita skill level work efficiency coefficient and the total assembly working time requirement, and obtaining the attendance accounting result of the testers based on the theoretical number of testers and the total testing working time requirement, it also includes:

[0041] Obtaining historical attendance personnel data, historical component assembly standard working hours and historical testing working hours for each order machine model;

[0042] The attendance accounting results of the assemblers and the attendance accounting results of the testers are evaluated based on the historical attendance personnel data, the historical component assembly standard working hours and the historical testing working hours, and when the evaluation results do not meet the preset evaluation conditions, the attendance accounting results of the assemblers and the attendance accounting results of the testers are optimized and adjusted.

[0043] Through the above technical solution, by optimizing and adjusting the attendance accounting results of assembly personnel and test personnel, employees' work shifts and rest time can be arranged more accurately, ensuring that each position is covered by appropriate personnel, avoiding insufficient or excessive personnel, and achieving the purpose of accurate scheduling.

[0044] According to the automatic calculation method of attendance personnel in the embodiment of the present application, after determining the standard working hours for component assembly, test working hours and the number of product orders in each wave of information for each order machine model in the order to be produced, the standard working hours for the whole machine of each order machine model and the working hour requirements of the order machines in each wave of information are obtained respectively, and then the total assembly working hour requirements are obtained according to the working hour requirements of the order machines in all wave information; the number of order machines to be tested is determined, and the total testing working hour requirements of the order machines to be tested are obtained in combination with the test working hours, and then the attendance calculation results of the assemblers and the attendance calculation results of the testers are obtained based on the determined theoretical number of assemblers, theoretical number of testers and the work efficiency coefficient of the average skill level of the assemblers for each order machine model. As a result, the problem of chaotic attendance management caused by the lack of system logic for attendance personnel accounting, resulting in a mismatch between work indicators and attendance personnel, and thus affecting production efficiency, order delivery and production costs, was solved. Through the combination of the MES system and the DIAG system, the production schedule for the next attendance day is calculated in advance every day, thereby automatically calculating the attendance accounting results of the attendance personnel required for each wave on that day, and making reasonable allocations based on the accounting results, thereby improving production efficiency and reducing costs.

[0045] The second aspect of the present application provides an automatic accounting device for attendance personnel, including:

[0046] A determination module is used to determine the standard assembly time and test time of components for each order machine model in the production order and the product order quantity in each wave information;

[0047] A calculation module, used to obtain the standard working hours of the whole machine of each order machine model according to the standard working hours of the parts assembly of each order machine model, and calculate the working hours requirement of the order machine in each wave information based on the product order quantity in each wave information and the standard working hours of the whole machine of each order machine model, and obtain the total assembly working hours requirement of all order machines according to the working hours requirement of the order machines in all wave information;

[0048] A first acquisition module is used to determine the number of order machines to be tested in a resource pool of order machines to be tested, and obtain the total test man-hour requirement of the order machines to be tested based on the number of order machines to be tested and the test man-hour of each order machine model;

[0049] The second acquisition module is used to determine the theoretical number of assemblers for each order machine model, the work efficiency coefficient of the average skill level of assemblers, and the theoretical number of testers for each order machine model, and obtain the attendance calculation results of the assemblers based on the theoretical number of assemblers, the work efficiency coefficient of the average skill level, and the total assembly working time requirement, and at the same time obtain the attendance calculation results of the testers based on the theoretical number of testers and the total testing working time requirement.

[0050] According to one embodiment of the present application, before determining the standard man-hours for assembling components of each order machine model in the to-be-produced order, the determination module further includes:

[0051] A data processing unit, configured to determine at least one order information and at least one order machine model based on the order to be produced, and to perform format conversion and data cleaning on each order information, remove duplicate order information and abnormal order information from each order information, and obtain at least one order information to be assembled;

[0052] A first acquisition unit is used to acquire a target order type, and build a complexity evaluation model of the order machine model based on the target order type, the assembly process of each order machine model and each to-be-assembled order information;

[0053] A generation unit is used to use the complexity assessment model to configure the complexity coefficient of each order machine model based on each order machine model, and use a preset optimization algorithm to perform order scheduling based on the complexity coefficient of each order machine model and each order information to be assembled, so as to generate wave information of the order information to be assembled.

[0054] According to one embodiment of the present application, the determining module includes:

[0055] A first determining unit, configured to determine the component assembly time of each ordered machine model based on a preset product standard and an assembly process of the ordered machine model;

[0056] The second determining unit is used to determine the standard working hours for component assembly of each ordered machine model by using the component assembly time and the preset debugging time.

[0057] According to an embodiment of the present application, after determining the standard man-hours for assembling components of each order machine model, the second determining unit further includes:

[0058] A storage subunit, used to store the standard man-hours for assembling components of each order machine model into a preset man-hour database, and to associate the standard man-hours with the order information corresponding to the standard man-hours for assembling components of each order machine model;

[0059] The first updating subunit is used to update the working hours based on a preset first working hour updating mechanism when the standard working hours for component assembly of each order machine model changes.

[0060] According to one embodiment of the present application, before determining the test man-hours for each order machine model in the to-be-produced order, the determination module further includes:

[0061] A second acquisition unit, used to acquire a first configuration parameter of at least one current order machine model and a second configuration parameter of at least one historical order machine model;

[0062] A prediction unit, used to identify a target configuration parameter that meets a similarity condition in each second configuration parameter, and predict the aging completion time of each current order machine model based on the target configuration parameter and a preset correction coefficient of the current order machine model, to obtain the aging completion prediction time of each current order machine model;

[0063] A third acquisition unit is used to build a machine aging prediction model based on the target configuration parameters and the aging completion time of each current order machine model, and obtain the aging completion time of a new order machine model based on the machine aging prediction model;

[0064] A construction unit, configured to generate aging test information of at least one order machine model based on a preset database management system, and to construct an aging database of the order machine model according to the aging test information, wherein the aging test information includes an order machine model configuration table, an aging test record table, and an aging completion schedule;

[0065] A screening unit is used to scan the aging test record table based on a preset scanning time, predict all order machine models that will complete aging within the next attendance day according to the current scanning time and the predicted aging completion time, and screen them to obtain the completed aging records of all order machine models within the next attendance day, and build a machine resource pool to be tested based on all order machine information and completed aging records within the next attendance day, and associate all order machine models to be tested in the machine resource pool with the test working hours of the order machine models to be tested.

[0066] According to one embodiment of the present application, the determining module includes:

[0067] A fourth acquisition unit is used to acquire the consumed time of the product inspection operation and the consumed time of the product packaging operation for each order machine model in the to-be-produced order;

[0068] a calculation unit, configured to calculate an average inspection operation time of the product based on the consumption time of each product inspection operation, and to calculate an average packaging operation time of the product based on the consumption time of each product packaging operation;

[0069] A storage unit is used to determine the standard inspection working hours for each order machine model based on the average inspection operation time of the product and the preset debugging time, and to determine the standard packaging working hours for each order machine model based on the average packaging operation time of the product and the preset debugging time, and to store the standard inspection working hours and the standard packaging working hours in a preset working hour database and associate them with each order machine model.

[0070] According to one embodiment of the present application, after the inspection standard working hours and the packaging standard working hours are stored in a preset working hour database and associated with each order machine model, the storage unit further includes:

[0071] The second updating subunit is used to update the working hours based on a preset second working hour updating mechanism when it is detected that the inspection standard working hours of each order machine model or the packaging standard working hours of each order machine model changes.

[0072] According to one embodiment of the present application, after obtaining the attendance accounting result of the assemblers based on the theoretical number of assemblers, the per capita skill level work efficiency coefficient and the total assembly working time requirement, and obtaining the attendance accounting result of the testers based on the theoretical number of testers and the total testing working time requirement, the second acquisition module further includes:

[0073] A fifth acquisition unit, used to acquire historical attendance personnel data, historical component assembly standard working hours and historical testing working hours of each order machine model;

[0074] An optimization unit is used to evaluate the attendance accounting results of the assemblers and the attendance accounting results of the testers based on the historical attendance personnel data, the historical component assembly standard working hours and the historical testing working hours, and to optimize and adjust the attendance accounting results of the assemblers and the attendance accounting results of the testers when the evaluation results do not meet the preset evaluation conditions.

[0075] According to the automatic accounting device for attendance personnel of the embodiment of the present application, after determining the standard working hours for component assembly, test working hours and the number of product orders in each wave of information for each order machine model in the order to be produced, the standard working hours for the whole machine of each order machine model and the working hour requirements of the order machines in each wave of information are obtained respectively, and then the total assembly working hour requirements are obtained according to the working hour requirements of the order machines in all wave information; the number of order machines to be tested is determined, and the total testing working hour requirements of the order machines to be tested are obtained in combination with the test working hours, and then the attendance accounting results of the assemblers and the attendance accounting results of the testers are obtained based on the determined theoretical number of assemblers, theoretical number of testers and the work efficiency coefficient of the average skill level of the assemblers for each order machine model. As a result, the problem of chaotic attendance management caused by the lack of system logic for attendance personnel accounting, resulting in a mismatch between work indicators and attendance personnel, and thus affecting production efficiency, order delivery and production costs, was solved. Through the combination of the MES system and the DIAG system, the production schedule for the next attendance day is calculated in advance every day, thereby automatically calculating the attendance accounting results of the attendance personnel required for each wave on that day, and making reasonable allocations based on the accounting results, thereby improving production efficiency and reducing costs.

[0076] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the automatic accounting method for attendance personnel as described in the above embodiment.

[0077] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the automatic accounting method for attendance personnel as described in the above embodiment.

[0078] The fifth aspect of the present application provides a computer program product, including a computer program, which is executed to implement the automatic accounting method for attendance personnel described in the above embodiment.

[0079] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0081] Figure 1 A flowchart of an automatic accounting method for attendance personnel provided according to an embodiment of the present application;

[0082] Figure 2This is a schematic diagram of the architecture of an automatic accounting system for attendance personnel according to an embodiment of the present application;

[0083] Figure 3 A schematic diagram of calculation logic of an automatic accounting method for attendance personnel according to an embodiment of the present application;

[0084] Figure 4 A schematic diagram of a block diagram of an automatic accounting device for attendance personnel according to an embodiment of the present application;

[0085] Figure 5 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0086] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0087] The following describes the automatic accounting method, device, electronic device and storage medium of the attendance personnel of the embodiment of the present application with reference to the accompanying drawings. In view of the problem mentioned in the above background technology that the lack of system logic for attendance personnel accounting leads to chaotic attendance management, resulting in mismatch between operation indicators and attendance personnel, thereby affecting production efficiency, order delivery and production costs, the present application provides an automatic accounting method for attendance personnel, in which after determining the standard assembly working hours, test working hours and the number of product orders in each wave information of each order machine model in the order to be produced, the standard working hours of the whole machine of each order machine model and the working hours requirements of the order machine in each wave information are obtained respectively, and then the total assembly working hours requirements are obtained according to the working hours requirements of the order machines in all wave information; the number of order machines to be tested is determined, and the total test working hours requirements of the order machines to be tested are obtained in combination with the test working hours, and then the attendance accounting results of the assemblers and the attendance accounting results of the testers are obtained based on the theoretical number of assemblers, the theoretical number of testers and the work efficiency coefficient of the per capita skill level of each order machine model determined. As a result, the problem of chaotic attendance management caused by the lack of system logic for attendance personnel accounting, resulting in a mismatch between work indicators and attendance personnel, and thus affecting production efficiency, order delivery and production costs, was solved. Through the combination of the MES system and the DIAG system, the production schedule for the next attendance day is calculated in advance every day, thereby automatically calculating the attendance accounting results of the attendance personnel required for each wave on that day, and making reasonable allocations based on the accounting results, thereby improving production efficiency and reducing costs.

[0088] Specifically, Figure 1A flowchart of an automatic accounting method for attendance personnel provided in an embodiment of the present application.

[0089] like Figure 1 As shown, the automatic accounting method for attendance personnel includes the following steps:

[0090] In step S101, the standard assembly time and test time of components for each order machine model in the to-be-produced order and the product order quantity in each wave information are determined.

[0091] Among them, the order to be produced is at least one required production order, the order information to be assembled is the production order after data processing, the target order is the production order that currently needs to be assembled, and the preset optimization algorithm can be selected by technical personnel in this field according to the actual product assembly requirements, and no specific limitation is made here.

[0092] Specifically, in the server manufacturing industry, reasonable arrangement of employee attendance and flexible adjustment of staff allocation are key factors in controlling the manufacturing cost and product production efficiency of the entire production line. Therefore, in order to avoid the situation where the number of attendance people does not match the operating indicators, thereby affecting production efficiency and increasing manufacturing costs, such as Figure 2 As shown, the embodiment of the present application is based on a combination of the MES system and the DIAG system. The production schedule for the next attendance day can be calculated in advance every day, thereby automatically calculating the attendance accounting results of the attendance personnel required for each wave on that day according to the production schedule. Therefore, the attendance personnel can be reasonably allocated based on the attendance accounting results of the attendance personnel to improve production efficiency and reduce costs.

[0093] Specifically, the present application mainly relates to the attendance personnel accounting allocation in the product assembly stage and the attendance personnel accounting allocation in the product inspection and packaging stage. In the attendance personnel accounting allocation process in the product assembly stage, the MES system is used for development. The MES system receives in advance at least one order machine required to assemble each product, and based on the production scheduling conditions set in the MES system, automatically arranges the production waves according to the complexity coefficient of each order machine model.

[0094] Through the above technical solution, by combining the MES system with the DIAG system, the production schedule for the next attendance day can be calculated in advance every day, thereby automatically calculating the attendance results of the attendance personnel required for each wave on that day, and making reasonable allocations based on the calculation results, thereby improving production efficiency and reducing costs.

[0095] According to one embodiment of the present application, before determining the standard working hours for component assembly of each order machine model in the order to be produced, it also includes: based on the order to be produced, determining at least one order information and at least one order machine model, and performing format conversion and data cleaning on each order information, eliminating duplicate order information and abnormal order information in each order information, and obtaining at least one order information to be assembled; obtaining the target order type, and constructing a complexity evaluation model for the order machine model based on the target order type, the assembly process of each order machine model and each order information to be assembled; using the complexity evaluation model, configuring the complexity coefficient of each order machine model based on each order machine model, and using a preset optimization algorithm, scheduling orders based on the complexity coefficient of each order machine model and each order information to be assembled, and generating wave information of the order information to be assembled.

[0096] Specifically, Figure 3 As shown, before determining the standard working hours for assembling components of each order machine model in the order to be produced, a preparation process for associating order information with the order machine model is required. First, in the embodiment of the present application, the MES system is based on a pre-set interface and communication protocol, such as a CAN (Controller Area Network) communication interface protocol, and automatically connects to the ERP (Enterprise Resource Planning) system on a daily basis to obtain at least one required order information and at least one order machine model, wherein the order information includes but is not limited to key data such as product model, order quantity, and delivery time; secondly, after the MES system receives at least one order information sent by the ERP, since there may be a situation where the encoding format of the data in the ERP system is inconsistent with the encoding format of the data in the MES system, it is necessary to perform data processing on each order information, that is, the MES system will convert the format of each received order information according to the pre-set mapping rules into a format that it can recognize, and at the same time, perform data cleaning on each order information, eliminate duplicate order information and abnormal order information in each order information, and obtain at least one order information to be assembled after screening and correcting each order information.

[0097] Secondly, the target order type is obtained, such as the product BOM (Bill of Materials) structure and component types, and a complexity evaluation model of the order machine model is constructed based on the target order type, the assembly process of each order machine model and the information of each order to be assembled, so as to assign a complexity coefficient to each order machine model through the model. For example, for high-end order machine models with a wide variety of components, complex assembly processes and high precision requirements, the configuration complexity coefficient is relatively high, while for basic order machine models with relatively simple structures and strong component versatility, the configuration complexity coefficient is relatively low. Among them, the correspondence between the target order type and each order machine model can be obtained based on the BOM structure. That is to say, when a target product is obtained, the relevant information and order machine model corresponding to the target product can be directly obtained based on the complexity evaluation model of the order machine model, so as to assign the corresponding complexity coefficient according to the order machine model to ensure the accuracy and efficiency of the production process.

[0098] Finally, the present application performs order scheduling based on the configured complexity coefficient of each order machine model and each order information to be assembled. That is to say, based on the complexity coefficient of each order machine model, the urgency of the product order, the delivery time and other conditions, the MES system uses a preset optimization algorithm to schedule orders, thereby generating wave information of the order information to be assembled. Among them, the preset optimization algorithm adopted in the embodiment of the present application aims to make the configuration complexity coefficients of the order machine models in the same wave as close as possible under the premise of meeting the delivery time, so as to improve production efficiency and equipment utilization. For example, in order to better utilize the production capacity of each order machine, the embodiment of the present application can use a greedy algorithm to schedule orders, that is, first sort the orders according to the order delivery time, and then assign the order machine models with similar configuration complexity coefficients to the same wave in turn, until the production capacity of the order machine models in the wave reaches the upper limit, thereby improving production efficiency.

[0099] Through the above technical solution, the consistency of information can be ensured by formatting and cleaning the order information. The complexity of each model can be accurately calculated by building a complexity assessment model, which can better match production resources and avoid waste or shortage of resources. By using the preset optimization algorithm for order scheduling, the production sequence can be reasonably arranged, the existing resources can be maximized, the production cycle can be shortened, and the output rate can be improved.

[0100] According to one embodiment of the present application, the standard working hours for component assembly of each order machine model in the order to be produced are determined, including: determining the component assembly time of each order machine model based on preset product standards and the assembly process of the order machine model; and determining the standard working hours for component assembly of each order machine model by combining the component assembly time and the preset debugging time.

[0101] Among them, the preset product standard and the preset debugging time can be set according to the testing experience of technical personnel in this field, or based on the production process characteristics of the product, etc., and are not specifically limited here.

[0102] Specifically, in the embodiment of the present application, in order to accurately obtain the standard working hours for product production, the standard working hours for component assembly are limited according to the production standards of each order machine model, as well as the type and quantity of each product component, so as to obtain the standard working hours for the whole machine based on the standard working hours for assembly of each component, that is, the standard working hours for product production.

[0103] Specifically, during the product assembly process, the component assembly time of each order machine model is determined based on the preset product standards and the assembly process of the order machine model. To ensure the accuracy of the component assembly time, the embodiment of the present application can be carried out by a professional working time measurement team based on industry product standards and the production process characteristics of the order machine model itself, using time study methods, predetermined action time standard methods and other methods to measure the assembly time of each component under standard operating conditions (including standard operating methods, normal work rhythm, qualified material supply, etc.), thereby determining the standard component assembly working hours for each order machine model based on the component assembly time and the preset debugging time.

[0104] For example, for a single component operation of a product, such as the plug-in operation of an electronic component, a professional time measurement team can determine the operation time of the plug-in through multiple on-site observations and time records, and determine the standard component assembly time for each order machine model in combination with the preset debugging time. Due to factors such as employee fatigue and equipment adjustments that may occur during the product assembly process, a certain amount of slack time needs to be taken into account on the basis of the component assembly time, so as to obtain the standard component assembly time for each order machine model in order to improve the accuracy of the standard component assembly time.

[0105] Through the above technical solution, by calculating the standard working hours for assembling product components, the accuracy of production planning can be improved and the effective use of resources can be ensured.

[0106] According to one embodiment of the present application, after determining the standard working hours for component assembly of each order machine model, it also includes: storing the standard working hours for component assembly of each order machine model in a preset working hour database, and associating them with the order information corresponding to the standard working hours for component assembly of each order machine model; when the standard working hours for component assembly of each order machine model changes, updating the working hours based on a preset first working hour update mechanism.

[0107] Among them, the preset working hour database and the preset first working hour update mechanism can be set by technical personnel in this field based on product assembly requirements, and are not specifically limited here.

[0108] Specifically, after calculating the standard working hours for component assembly of each order machine model, the embodiment of the present application needs to further store the standard working hours for component assembly of each order machine model in the working hour database preset by the MES system, and associate it with the order information corresponding to the standard working hours for component assembly of each order machine model, so that the standard working hours for component assembly of the corresponding product can be obtained based on a certain target order through the preset working hour database.

[0109] Furthermore, as the order machines are used for a longer period of time, factors such as the assembly process and machine performance of each order machine model may change, which may cause the standard working hours for component assembly to change, making the original standard working hours for component assembly no longer accurate. At this time, in order to better coordinate the use of order machines on the production line and improve the accuracy and operability of product production plans, the embodiment of the present application needs to be based on a preset first working hour update mechanism. When the standard working hours for component assembly change due to changes in production processes, equipment upgrades, or improvements in employee operating skills, the standard working hours for component assembly can be updated and revised in a timely manner. For example, the standard working hours for component assembly can be updated through a data analysis platform, a professional working hour measurement team, or an automated working hour update tool, so that the standard working hours for component assembly can be reduced in a timely manner, making production scheduling more accurate.

[0110] Through the above technical solution, through the update mechanism, the working hours are updated when the standard working hours for component assembly of each order machine model change, so as to continuously improve the accuracy and efficiency of production management, so as to avoid the situation of excessive or insufficient allocation of attendance personnel due to working hour errors.

[0111] In step S102, the standard working hours for the whole machine of each order machine model is obtained based on the standard working hours for component assembly of each order machine model. At the same time, the working hour requirement of the order machines in each wave information is calculated based on the product order quantity in each wave information and the standard working hours for the whole machine of each order machine model. Finally, the total assembly working hour requirement of all order machines is obtained based on the working hour requirement of the order machines in all wave information.

[0112] Specifically, in the above embodiment, the standard assembly time of components for each order machine model has been stored in the preset work time database of the MES system. Therefore, after the MES system obtains the product model in a new order, it will automatically retrieve the order machine model corresponding to the product model and all the components it contains and the corresponding standard assembly time of each component from the preset work time database. Then, by accumulating the standard assembly time of each component, the standard assembly time of the whole machine for each order machine model is calculated.

[0113] For example, an electronic product consists of multiple components such as a motherboard, a display screen, and a casing. The MES system obtains the standard assembly time of these components based on the preset labor time database. For example, the standard assembly time of the motherboard is 15 minutes, the standard assembly time of the display screen is 10 minutes, the standard assembly time of the casing is 5 minutes, etc. Finally, the standard assembly time of each component is added up to obtain the standard assembly time of the whole machine of the order model, which is 30 minutes.

[0114] Furthermore, based on the standard working hours of the whole machine of each order machine model, the MES system will further calculate the total assembly working hours required for all order machines based on logical operations based on the working hours required for the order machines in all wave information scheduled to the production line on that day.

[0115] Specifically, first, for each wave of assembly information, the MES system obtains the product order quantity in each wave information and the corresponding standard machine working hours for each order machine model based on the wave information of the scheduled production line; secondly, based on the product order quantity in each wave information and the product of the standard machine working hours for each order machine model, the working hour requirement of the order machine in each wave information is calculated; finally, the working hour requirements of the order machines in all wave information are added up to obtain the total assembly working hour requirement of all order machines scheduled for the production line on that day.

[0116] For example, if production line A has two waves of production scheduled on the same day, namely wave 1 and wave 2, among which the product order quantity of wave 1 is 50 orders, and the standard working time of the whole machine of the order machine model is 30 minutes, then the working time requirement of the order machine of wave 1 is 50×30=1500 minutes; the product order quantity of wave 2 is 30 orders, and the standard working time of the whole machine of the order machine model is 35 minutes, then the working time requirement of the order machine of wave 2 is 30×35=1050 minutes. Therefore, it can be obtained that the total assembly working time requirement of production line A on that day is 1500+1050=2550 minutes.

[0117] Through the above technical solution, by accurately calculating the total assembly man-hour requirements for each product, the total time required to complete product assembly can be better predicted, so as to reasonably arrange the number of people on duty and ensure the effective use of resources.

[0118] In step S103, the number of order machines to be tested in the machine resource pool to be tested is determined, and the total test man-hour requirement of the order machines to be tested is obtained based on the number of order machines to be tested and the test man-hour of each order machine model.

[0119] The resource pool of machines to be tested is a centralized database or collection for managing and tracking all order machines that are about to undergo aging tests, and the order machines to be tested are order machines that perform product inspection and packaging after product assembly.

[0120] Specifically, after completing the product assembly process based on at least one order information and at least one order machine model, the product testing process is further completed based on at least one order information and at least one order machine model, mainly including the product inspection and packaging process, so as to obtain the total testing time requirement of the order machines to be tested based on the number of order machines to be tested and the testing time of each order machine model.

[0121] Through the above technical solution, by accurately calculating the total man-hours required for testing each product, the total time required to complete product inspection and packaging can be better predicted, so as to reasonably arrange the number of people on duty and ensure the effective use of resources.

[0122] According to one embodiment of the present application, before determining the test working hours for each order machine model in the order to be produced, it also includes: obtaining a first configuration parameter of at least one current order machine model and a second configuration parameter of at least one historical order machine model; identifying a target configuration parameter that meets a similarity condition in each second configuration parameter, and predicting the aging completion time of each current order machine model based on the target configuration parameter and a preset correction coefficient of the current order machine model, and obtaining the predicted aging completion time of each current order machine model; constructing a machine aging prediction model based on the second configuration parameter of at least one historical order machine model and the corresponding aging completion time, and obtaining the aging completion time of a new order machine model based on the machine aging prediction model; based on a preset database The management system generates aging test information of at least one order machine model, and builds an aging database of the order machine model based on the aging test information, wherein the aging test information includes an order machine model configuration table, an aging test record table and an aging completion schedule; the aging test record table is scanned based on a preset scanning time, and all order machine models that will complete aging within the next attendance day are predicted and screened based on the current scanning time and the aging completion prediction time, so as to obtain the completed aging records of all order machine models within the next attendance day, and a machine resource pool to be tested is built based on all order machine information and completed aging records within the next attendance day, and all order machine models to be tested in the machine resource pool to be tested are associated with the test hours of the order machine models to be tested.

[0123] Among them, the first configuration parameter is the configuration parameter of the current order machine model in use, the second configuration parameter is the configuration parameter of the historical order machine model that is known or historically used, the target configuration parameter is the configuration parameter of the historical order machine model that is identified to have a high degree of similarity with the configuration parameter of the current order machine model, and the predicted time for completion of aging is the time for completing aging within 24:00 the next day; the preset correction coefficient of the current order machine model, the preset database management system, and the preset scanning time can all be set by technical personnel in this field based on the inspection and packaging needs of the product, and are not specifically limited here.

[0124] Specifically, in the process of attendance personnel accounting and allocation in the product inspection and packaging stage, the MES system is developed in combination with the DIAG system. Before determining the test working hours for each order machine model in the production order, the DIAG system estimates the machine aging completion time according to the configuration of the order machine model (this estimated time is the time when the machine is aging and does not include errors). First, a detailed analysis model for different order machine model configurations is established in the DIAG system. The model collects and stores the second configuration parameters of at least one historical order machine model, including but not limited to hardware specifications (such as CPU (Central Processing Unit) model, memory capacity, hard disk type, etc.), software system version, functional module composition and other information, as well as the corresponding actual aging test data (such as the performance indicator change curve during the aging process, the final aging completion time, etc.). A machine learning algorithm (such as a regression analysis algorithm) is used to construct a prediction model based on the second configuration parameters of at least one historical order machine model as an input variable and the corresponding aging completion time as an output variable. When training the prediction model, the second configuration parameters of the historical order machine model are divided into a training set and a validation set, so that the prediction error of the prediction model on the validation set can be minimized by continuously adjusting the prediction model parameters.

[0125] Secondly, for the current order machine model (new model) or the historical order machine model that is not fully matched in the historical data, the DIAG system uses an algorithm based on the similarity principle for analysis. After obtaining the first configuration parameters of at least one current order machine model, the DIAG system inputs the first configuration parameters into the above-mentioned trained prediction model, and calculates the similarity between the current order machine model and the historical order machine model in key configuration parameters (for example, the Euclidean distance formula can be used to calculate the distance between the hardware specification parameter vectors), that is, based on the first configuration parameters of at least one current order machine model, identify the target configuration parameters in each second configuration parameter that have a high similarity with the first configuration parameters, use the target configuration parameters as a reference, and combine with the preset correction coefficient of the current order machine model (which can be adjusted according to the unique configuration characteristics of the current order machine model) to predict the aging completion time of each current order machine model, thereby obtaining the aging completion prediction time of each current order machine model, and then after obtaining the configuration parameters of the new order machine model, the aging completion time of the corresponding new order machine model can be calculated based on the trained prediction model.

[0126] For example, for a current order machine model, the DIAG system analyzes its CPU as a high-performance processor, large memory, equipped with an independent graphics card and other configuration features, combined with the aging completion time of historical order machine models, and predicts that the aging completion time of the current order machine model is about 10 hours (this time is the normal aging time of the machine excluding error reporting).

[0127] Finally, an aging database is established based on the order machines in the aging stage, which is limited by the aging completion time predicted by the DIAG system. The order machines that complete the aging within the next attendance day are captured as a resource pool of machines to be tested. The number of all order machine models to be tested in the resource pool of machines to be tested is pushed to the MES system by the DIAG system. The MES system develops a docking interface and records the data of the order machines to be tested the next day. That is to say, according to the established aging database, a preset database management system (such as MySQL or Oracle) is used to produce at least one data table to store the aging test information of the order machine model, including the order machine model configuration. The order machine model configuration table is used to record the detailed configuration parameters of the order machine model; the aging test record table is used to store the start time, end time, performance data during the process, whether an error is reported, and other information of the aging test of each order machine; the aging completion schedule is used to associate the order machine model configuration with the predicted aging completion time (calculated by the DIAG system), and establish a database index to improve data query and retrieval efficiency. For example, an index is established on the key configuration fields (such as machine model, main hardware model, etc.) of the order machine model configuration table to quickly obtain relevant aging information based on the order machine model.

[0128] Furthermore, the DIAG system will periodically scan the aging test record table in the aging database according to the preset scanning time, for example, scan at a scanning frequency of 1 hour, so as to predict and screen all the order machine models that will complete aging within the next attendance day according to the current scanning time and the predicted time for completion of aging, that is, screen out all the order machine model records that are expected to complete aging within the next attendance day (24:00 the next day), and based on all the order machine information (such as machine model, serial number, etc.) and the completed aging records within the next attendance day, these order machine information (such as machine model, serial number, etc.) are combined into a machine resource pool to be tested, and the DIAG system pushes the number of machines and related information in the machine resource pool to be tested to the MES system through a pre-developed interface. After receiving the data, the MES system establishes a corresponding data structure (such as a list or array) inside it to store all the order machine model related data to be tested in the machine resource pool to be tested, and associates it with the calculation module of the subsequent test working hours.

[0129] For example, if the DIAG system scans the aging test record table in the aging database at 8 o'clock every night and finds that 100 order machine models are expected to complete aging within 24 hours of the next day, the information of these 100 order machine models will be pushed to the MES system, and the MES system will record and prepare for the next step of test time calculation.

[0130] Through the above technical solution, the DIAG system performs aging tests on the ordered machine models. By accurately predicting the aging completion time, subsequent processes (such as inspection, packaging, etc.) can be arranged in advance to ensure the smooth operation of the production line.

[0131] According to one embodiment of the present application, the test working hours for each order machine model in the order to be produced are determined, including: obtaining the consumed time of the product inspection operation and the consumed time of the product packaging operation for each order machine model in the order to be produced; calculating the average inspection operation time of the product based on the consumed time of each product inspection operation, and calculating the average packaging operation time of the product based on the consumed time of each product packaging operation; determining the inspection standard working hours for each order machine model based on the average inspection operation time and the preset debugging time of the product, and at the same time determining the packaging standard working hours for each order machine model based on the average packaging operation time and the preset debugging time of the product; storing the inspection standard working hours and the packaging standard working hours in a preset working hour database, and associating them with each order machine model.

[0132] Specifically, in an embodiment of the present application, based on the complexity of the order machine model, a set of standard working hour logic for inspecting and packaging order machines is set. When the order machines in the above-mentioned machine resource pool to be tested are automatically transferred to the MES system, the MES system will calculate the total working hours in the entire machine resource pool to be tested based on the test working hours of a single order machine.

[0133] Specifically, the embodiment of the present application can organize a professional team of industrial engineers to conduct detailed job analysis for different order machine models. First, time study methods (such as stopwatch timing method, work sampling method, etc.) are used to observe and record the time consumed by inspectors in performing inspection operations (including appearance inspection, function testing, performance testing and other inspection links) on different order machine models, as well as the time consumed by packaging personnel in performing packaging operations (such as packaging material preparation, product boxing, label pasting, etc.); secondly, based on multiple observation data, after removing outliers, the average inspection operation time of the product is calculated based on the time consumed for each product inspection operation, and based on the time consumed for each product packaging, the average inspection operation time of the product is calculated based on the time consumed for each product inspection operation. The average packaging operation time of the product is calculated based on the time consumed in the operation. Since factors such as employee fatigue and equipment adjustment may occur during the product inspection and packaging process, a certain amount of allowance time, that is, the preset debugging time, needs to be taken into account on the basis of the component inspection and packaging time. In this way, the average packaging operation time of the product is calculated based on the time consumed in each product packaging operation. Furthermore, the standard inspection hours for each order machine model are determined based on the average inspection operation time and the preset debugging time of the product, and the standard packaging hours for each order machine model are determined based on the average packaging operation time and the preset debugging time of the product, so as to improve the accuracy of the product inspection standard hours and packaging standard hours.

[0134] For example, for a certain order machine model, after multiple observations and analyses, it is determined that the standard working hours for appearance inspection is 5 minutes, the standard working hours for function inspection is 10 minutes, and the standard working hours for packaging is 3 minutes. Therefore, it can be obtained that the total standard working hours for inspection and packaging of a single order machine is 18 minutes.

[0135] Furthermore, the calculated inspection standard working hours and packaging standard working hours are stored in a preset working hour database and associated with each order machine model, so that a unified standard operating time can be established for each order machine model to ensure that products in different batches and different production lines can be produced and inspected according to the same standards, thereby improving the accuracy and efficiency of production management.

[0136] Through the above technical solution, by calculating the standard inspection working hours and the standard packaging working hours, it is possible to formulate production plans more accurately, arrange production cycles reasonably, and ensure the effective use of resources.

[0137] According to one embodiment of the present application, after the inspection standard working hours and the packaging standard working hours are stored in a preset working hour database and associated with each order machine model, it also includes: when it is detected that the inspection standard working hours of each order machine model, or the packaging standard working hours of each order machine model have changed, the working hours are updated based on a preset second working hour update mechanism.

[0138] Among them, the preset second working hour update mechanism can be set by technical personnel in this field based on product inspection and packaging requirements, and is not specifically limited here.

[0139] Specifically, as the order machine is used for a longer period of time, factors such as the assembly process and machine performance of each order machine model may change, resulting in changes in the standard inspection hours and packaging hours for the product, making the original standard inspection hours and packaging hours no longer accurate. At this time, in order to better coordinate the use of order machines on the production line and improve the accuracy and operability of product inspection and packaging, the embodiment of the present application needs to be based on a preset second working hour update mechanism. When the production process changes, equipment upgrades, or employee operating skills are improved, resulting in changes in the standard inspection hours and packaging hours, the standard inspection hours and packaging hours can be updated and revised in a timely manner. For example, the standard inspection hours and packaging hours can be updated through a data analysis platform, a professional working hour measurement team, or an automated working hour update tool, so that the errors of the standard inspection hours and packaging hours can be reduced in a timely manner, making production scheduling more accurate.

[0140] Furthermore, after the MES system receives the resource pool data of the machines to be tested from the DIAG system, for each machine to be tested in the resource pool of the machines to be tested, the MES system retrieves the corresponding standard inspection hours and standard packaging hours from the preset working hour database according to the model of each machine to be tested. Then, the system multiplies the standard inspection hours and standard packaging hours of each machine model by the number of machines to be tested in the resource pool of the machines to be tested to obtain the total testing hours required for the machines to be tested.

[0141] For example, if there are 100 machines to be tested in the machine resource pool, and the standard inspection and packaging time for each machine is 20 minutes, the total testing time requirement for the machines to be tested in the machine resource pool is 100×20=2000 minutes.

[0142] Through the above technical solution, through the update mechanism, when the inspection standard working hours or packaging standard working hours of each order machine model change, the working hours are updated, so as to continuously improve the accuracy and efficiency of production management, so as to avoid the over-allocation or under-allocation of attendance personnel due to working hour errors.

[0143] In step S104, the theoretical number of assemblers for each order machine model, the work efficiency coefficient of the average skill level of assemblers, and the theoretical number of testers for each order machine model are determined, and the attendance accounting results of the assemblers are obtained based on the theoretical number of assemblers, the work efficiency coefficient of the average skill level, and the total assembly working hours requirement. At the same time, the attendance accounting results of the testers are obtained based on the theoretical number of testers and the total testing working hours requirement.

[0144] Specifically, based on the total assembly time requirements of all order machines calculated above, the MES system continues to perform in-depth calculations and calculates the total required attendance time for the order machine models that have been scheduled for production on this production line according to the theoretical number of assembly personnel in the database of each production line. Based on the total required attendance time given by the MES system, personnel attendance can be reasonably arranged during the assembly stage to avoid blindly arranging employees to work every day but with insufficient output.

[0145] Specifically, in the MES system, a standard manpower database is established for each production line. The database records the standard manpower allocation of the production line under different production loads, that is, the theoretical number of assembly personnel, including the number of personnel in each position, skill level and other information. At the same time, based on historical production data and industry experience, the work efficiency coefficients corresponding to different personnel skill levels are set, that is, the work efficiency coefficient of the average skill level of the assembly. After the total assembly working time requirement is calculated, the MES system calculates the attendance time required to complete the production scheduling task of the day based on the number of assembly personnel on the production line and the work efficiency coefficient of the average skill level of the assembly personnel, that is, the attendance accounting result of the assembly personnel is obtained, among which the calculation method of the attendance accounting result of the assembly personnel is: total assembly working time requirement ÷ (theoretical number of assembly personnel × work efficiency coefficient of average skill level).

[0146] For example, the theoretical number of assembly personnel on production line A is 10, and the average skill level and work efficiency coefficient is 0.8. At this time, the total assembly time requirement is 2550 minutes. The attendance calculation result of the assembly personnel required for production line A to complete the production scheduling task for the day is 2550÷(10×0.8)=318.75 minutes (about 5.31 hours).

[0147] Furthermore, based on the total test time requirements of all order machines calculated above, the MES system will automatically calculate the attendance results of the testers required for the next day according to the developed calculation function through the captured total test time requirements. Then, the management personnel will reasonably arrange the inspection and packaging attendance manpower according to the attendance results of the testers given by MES every day.

[0148] Specifically, in the MES system, first, a human resource allocation model for inspection and packaging is established. This model can set the standard number of manpower corresponding to unit working hours under different production loads according to the company's production organization mode and work efficiency requirements, that is, the theoretical number of testers. For example, under normal production load, one inspection and packaging personnel is required for every 100 minutes of standard inspection working hours and standard packaging working hours; secondly, after calculating the total testing working hours required, the MES system can calculate the attendance accounting results of the testers required for the next day based on the inspection and packaging human resource allocation model by dividing the total testing working hours required by the theoretical number of testers corresponding to the unit working hours.

[0149] For example, the total testing time requirement is 2000 minutes. Based on the standard of allocating one person every 100 minutes, the attendance calculation result of the testers required on the second day is 2000÷100=20 people.

[0150] Furthermore, in order to facilitate relevant managers to formulate attendance plans more accurately, the MES system presents the attendance accounting results of the assembly personnel and the test personnel obtained based on the above calculations to the relevant managers in the form of reports or visual interfaces. The relevant managers make appropriate adjustments based on the actual situation (such as employee leave, temporary task allocation, employee skill differences, etc.) based on the attendance accounting results of the assembly personnel and the test personnel, and reasonably arrange the attendance manpower for assembly and the attendance manpower for inspection and packaging to improve production efficiency and product quality.

[0151] Through the above technical solution, by calculating the attendance accounting results of assembly personnel and test personnel, a production plan can be formulated more accurately to ensure the effective use of resources.

[0152] According to one embodiment of the present application, after obtaining the attendance accounting results of the assemblers based on the theoretical number of assemblers, the work efficiency coefficient of the average skill level and the total assembly working time requirement, and obtaining the attendance accounting results of the testers based on the theoretical number of testers and the total testing working time requirement, it also includes: obtaining historical attendance personnel data, historical component assembly standard working hours and historical testing working hours for each order machine model; evaluating the attendance accounting results of the assemblers and the attendance accounting results of the testers based on the historical attendance personnel data, historical component assembly standard working hours and historical testing working hours, and optimizing and adjusting the attendance accounting results of the assemblers and the attendance accounting results of the testers when the evaluation results do not meet the preset evaluation conditions.

[0153] Specifically, in order to improve the level of refinement and response speed of enterprise management, the embodiments of the present application can also be based on historical attendance data comparison, so that historical attendance data can be tracked over a long period of time to help identify the impact of changes in product demand, improvements in machine model performance, or other factors on production, and provide a basis for future decision-making.

[0154] Specifically, first, obtain the historical attendance data, historical component assembly standard working hours and historical testing working hours provided by the MES system, compare the historical attendance data, historical component assembly standard working hours and historical testing working hours of each order machine model, and then analyze the actual production efficiency. This can help managers evaluate whether the current attendance manpower arrangement has achieved the expected goals, such as production speed, quality standards, etc. If the attendance on the day seriously exceeds the standards given by the MES system, the MES will automatically push an early warning to the relevant managers for investigation and improvement, so as to continuously optimize the attendance manpower allocation decisions.

[0155] For example, relevant managers can view the actual attendance number and production completion status of the same-scale machine resource pool to be tested in the past week, and compare the current attendance accounting results of assemblers and testers to determine whether it is necessary to increase or decrease the number of attendance personnel, or adjust the personnel skill combination. This can help relevant managers quickly adjust the number of attendance personnel based on real-time data feedback to enhance the reliability of management decisions, thereby ensuring that the production efficiency of the production line is always in the best state.

[0156] Through the above technical solution, by optimizing and adjusting the attendance accounting results of assembly personnel and test personnel, employees' work shifts and rest time can be arranged more accurately, ensuring that each position is covered by appropriate personnel, avoiding insufficient or excessive personnel, and achieving the purpose of accurate scheduling.

[0157] In summary, based on the specific discussion of the above embodiments, the following beneficial effects can be achieved:

[0158] (1) Through the data collection, maintenance and automatic decision-making of the MES system, and the combination of the MES system and the DIAG system, the MES system and the DIAG system can reasonably calculate the attendance accounting results and make attendance arrangements, thereby realizing the intelligent management and control of daily attendance, facilitating the accuracy and safety of labor cost management, achieving manpower simplification, maximizing efficiency, accurate information, saving resource waste, and greatly improving the accuracy of attendance arrangements;

[0159] (2) This application replaces manual decision-making with intelligent system judgment, thereby improving the maximum utilization rate of attendance personnel and greatly reducing labor costs. At the same time, it replaces manual control with an intelligent system, reducing the occurrence of omissions in manual attendance arrangements, and can also assist managers and employees to complete their work more efficiently.

[0160] According to the automatic calculation method of attendance personnel in the embodiment of the present application, after determining the standard working hours for component assembly, test working hours and the number of product orders in each wave of information for each order machine model in the order to be produced, the standard working hours for the whole machine of each order machine model and the working hour requirements of the order machines in each wave of information are obtained respectively, and then the total assembly working hour requirements are obtained according to the working hour requirements of the order machines in all wave information; the number of order machines to be tested is determined, and the total testing working hour requirements of the order machines to be tested are obtained in combination with the test working hours, and then the attendance calculation results of the assemblers and the attendance calculation results of the testers are obtained based on the determined theoretical number of assemblers, theoretical number of testers and the work efficiency coefficient of the average skill level of the assemblers for each order machine model. As a result, the problem of chaotic attendance management caused by the lack of system logic for attendance personnel accounting, resulting in a mismatch between work indicators and attendance personnel, and thus affecting production efficiency, order delivery and production costs, was solved. Through the combination of the MES system and the DIAG system, the production schedule for the next attendance day is calculated in advance every day, thereby automatically calculating the attendance accounting results of the attendance personnel required for each wave on that day, and making reasonable allocations based on the accounting results, thereby improving production efficiency and reducing costs.

[0161] Next, the automatic accounting device for attendance personnel proposed in accordance with the embodiment of the present application will be described with reference to the accompanying drawings.

[0162] Figure 4 It is a block diagram of an automatic accounting device for attendance personnel according to an embodiment of the present application.

[0163] like Figure 4 As shown, the automatic accounting device 10 for attendance personnel includes: a determination module 100 , a calculation module 200 , a first acquisition module 300 and a second acquisition module 400 .

[0164] The determination module 100 is used to determine the standard assembly time and test time of each machine model in the order to be produced and the product order quantity in each wave information;

[0165] The calculation module 200 is used to obtain the standard working time of the whole machine of each order machine model according to the standard working time of the parts assembly of each order machine model, and calculate the working time requirement of the order machine in each wave information based on the product order quantity in each wave information and the standard working time of the whole machine of each order machine model, and obtain the total assembly working time requirement of all order machines according to the working time requirements of the order machines in all wave information;

[0166] The first acquisition module 300 is used to determine the number of order machines to be tested in the machine resource pool to be tested, and obtain the total test time requirement of the order machines to be tested based on the number of order machines to be tested and the test time of each order machine model;

[0167] The second acquisition module 400 is used to determine the theoretical number of assemblers for each order machine model, the work efficiency coefficient of the average skill level of assemblers, and the theoretical number of testers for each order machine model, and obtain the attendance accounting results of the assemblers based on the theoretical number of assemblers, the work efficiency coefficient of the average skill level, and the total assembly working hours requirement, and at the same time obtain the attendance accounting results of the testers based on the theoretical number of testers and the total testing working hours requirement.

[0168] According to one embodiment of the present application, before determining the standard man-hours for assembling components of each order machine model in the to-be-produced order, the determination module further includes:

[0169] A data processing unit, used to determine at least one order information and at least one order machine model based on the order to be produced, and perform format conversion and data cleaning on each order information, remove duplicate order information and abnormal order information in each order information, and obtain at least one order information to be assembled;

[0170] A first acquisition unit is used to acquire a target order type, and build a complexity evaluation model of the order machine model based on the target order type, the assembly process of each order machine model and each to-be-assembled order information;

[0171] The generation unit is used to use the complexity evaluation model to configure the complexity coefficient of each order machine model based on each order machine model, and use a preset optimization algorithm to schedule orders based on the complexity coefficient of each order machine model and each order information to be assembled, and generate wave information of the order information to be assembled.

[0172] According to one embodiment of the present application, the determination module includes:

[0173] A first determining unit, configured to determine the component assembly time of each ordered machine model based on a preset product standard and an assembly process of the ordered machine model;

[0174] The second determining unit is used to determine the standard working hours for component assembly of each order machine model by using the component assembly time and the preset debugging time.

[0175] According to one embodiment of the present application, after determining the standard man-hours for assembling components of each order machine model, the second determining unit further includes:

[0176] A storage subunit, used to store the standard labor time for assembling components of each order machine model into a preset labor time database, and to associate the standard labor time with the order information corresponding to the standard labor time for assembling components of each order machine model;

[0177] The first updating subunit is used to update the working hours based on a preset first working hour updating mechanism when the standard working hours for component assembly of each order machine model changes.

[0178] According to one embodiment of the present application, before determining the test man-hours for each order machine model in the to-be-produced order, the determination module further includes:

[0179] A second acquisition unit, used to acquire a first configuration parameter of at least one current order machine model and a second configuration parameter of at least one historical order machine model;

[0180] A prediction unit, used to identify a target configuration parameter that meets a similarity condition in each second configuration parameter, and predict the aging completion time of each current order machine model based on the target configuration parameter and a preset correction coefficient of the current order machine model, so as to obtain the aging completion prediction time of each current order machine model;

[0181] A third acquisition unit is used to build a machine aging prediction model based on the target configuration parameters and the aging completion time of each current order machine model, and obtain the aging completion time of a new order machine model based on the machine aging prediction model;

[0182] A construction unit, configured to generate aging test information of at least one order machine model based on a preset database management system, and to construct an aging database of the order machine model according to the aging test information, wherein the aging test information includes an order machine model configuration table, an aging test record table, and an aging completion schedule;

[0183] A screening unit is used to scan the aging test record table based on a preset scanning time, predict all order machine models that will complete aging within the next attendance day according to the current scanning time and the predicted aging completion time, and screen them to obtain the completed aging records of all order machine models within the next attendance day, and build a machine resource pool to be tested based on all order machine information and completed aging records within the next attendance day, and associate all order machine models to be tested in the machine resource pool to be tested with the test working hours of the order machine models to be tested.

[0184] According to one embodiment of the present application, the determination module includes:

[0185] A fourth acquisition unit is used to acquire the consumed time of the product inspection operation and the consumed time of the product packaging operation for each order machine model in the to-be-produced order;

[0186] A calculation unit, used to calculate an average inspection operation time of the product based on the consumption time of each product inspection operation, and to calculate an average packaging operation time of the product based on the consumption time of each product packaging operation;

[0187] A storage unit is used to determine the standard inspection working hours for each order machine model based on the average inspection operation time of the product and the preset debugging time, and to determine the standard packaging working hours for each order machine model based on the average packaging operation time of the product and the preset debugging time, and to store the standard inspection working hours and the standard packaging working hours in a preset working hour database and associate them with each order machine model.

[0188] According to one embodiment of the present application, after the inspection standard working hours and the packaging standard working hours are stored in a preset working hour database and associated with each order machine model, the storage unit further includes:

[0189] The second updating subunit is used to update the working hours based on a preset second working hour updating mechanism when it is detected that the inspection standard working hours of each order machine model or the packaging standard working hours of each order machine model changes.

[0190] According to one embodiment of the present application, after obtaining the attendance accounting result of the assemblers based on the theoretical number of assemblers, the work efficiency coefficient of the average skill level and the total assembly time requirement, and obtaining the attendance accounting result of the testers based on the theoretical number of testers and the total test time requirement, the second acquisition module further includes:

[0191] A fifth acquisition unit, used to acquire historical attendance personnel data, historical component assembly standard working hours and historical testing working hours for each order machine model;

[0192] The optimization unit is used to evaluate the attendance accounting results of assemblers and testers based on historical attendance data, historical component assembly standard working hours and historical testing working hours, and to optimize and adjust the attendance accounting results of assemblers and testers when the evaluation results do not meet the preset evaluation conditions.

[0193] According to the automatic accounting device for attendance personnel of the embodiment of the present application, after determining the standard working hours for component assembly, test working hours and the number of product orders in each wave of information for each order machine model in the order to be produced, the standard working hours for the whole machine of each order machine model and the working hour requirements of the order machines in each wave of information are obtained respectively, and then the total assembly working hour requirements are obtained according to the working hour requirements of the order machines in all wave information; the number of order machines to be tested is determined, and the total testing working hour requirements of the order machines to be tested are obtained in combination with the test working hours, and then the attendance accounting results of the assemblers and the attendance accounting results of the testers are obtained based on the determined theoretical number of assemblers, theoretical number of testers and the work efficiency coefficient of the average skill level of the assemblers for each order machine model. As a result, the problem of chaotic attendance management caused by the lack of system logic for attendance personnel accounting, resulting in a mismatch between work indicators and attendance personnel, and thus affecting production efficiency, order delivery and production costs, was solved. Through the combination of the MES system and the DIAG system, the production schedule for the next attendance day is calculated in advance every day, thereby automatically calculating the attendance accounting results of the attendance personnel required for each wave on that day, and making reasonable allocations based on the accounting results, thereby improving production efficiency and reducing costs.

[0194] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0195] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0196] When the processor 502 executes the program, the automatic accounting method for attendance personnel provided in the above embodiment is implemented.

[0197] Furthermore, the electronic device further comprises:

[0198] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0199] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0200] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0201] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0202] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0203] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0204] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned method for automatically calculating attendance personnel is implemented.

[0205] This embodiment also provides a computer program product, including a computer program, which is executed to implement the automatic accounting method for attendance personnel of the above embodiment.

[0206] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0207] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0208] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0209] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0210] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0211] A person skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0212] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0213] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for automatically calculating attendance of personnel, characterized in that: The following steps are involved: Determine the standard assembly time, test time and product order quantity in each wave information for each order machine model in the production order to be produced; The standard working hours for the whole machine of each order machine model are obtained according to the standard working hours for assembling the parts of each order machine model, and the working hours requirement for the order machines in each wave information is calculated based on the product order quantity in each wave information and the standard working hours for the whole machine of each order machine model, and the total assembly working hours requirement for all order machines is obtained according to the working hours requirement for the order machines in all wave information; Determine the number of order machines to be tested in the machine resource pool to be tested, and obtain the total test man-hour requirement of the order machines to be tested based on the number of order machines to be tested and the test man-hour of each order machine model; Determine the theoretical number of assemblers for each order machine model, the work efficiency coefficient of the average skill level of assemblers, and the theoretical number of testers for each order machine model, and obtain the attendance calculation results of the assemblers based on the theoretical number of assemblers, the work efficiency coefficient of the average skill level, and the total assembly working time requirement, and at the same time, obtain the attendance calculation results of the testers based on the theoretical number of testers and the total testing working time requirement.

2. The method according to claim 1, characterized in that Before determining the standard labor time for component assembly of each order machine model in the to-be-produced order, it also includes: Based on the order to be produced, at least one order information and at least one order machine model are determined, and each order information is formatted and cleaned, and duplicate order information and abnormal order information in each order information are removed to obtain at least one order information to be assembled; Obtaining a target order type, and building a complexity evaluation model of an order machine model based on the target order type, the assembly process of each order machine model, and each to-be-assembled order information; Utilizing the complexity assessment model, the complexity coefficient of each order machine model is configured respectively based on each order machine model, and utilizing a preset optimization algorithm, order scheduling is performed based on the complexity coefficient of each order machine model and each order information to be assembled, thereby generating wave information of the order information to be assembled.

3. The method according to claim 1, characterized in that The step of determining the standard man-hours for assembling components of each order machine model in the order to be produced includes: Determine the component assembly time of each ordered machine model based on the preset product standard and the assembly process of the ordered machine model; The component assembly time and the preset debugging time are used to determine the standard component assembly time for each ordered machine model.

4. The method according to claim 3, characterized in that After determining the standard labor time for component assembly for each order machine model, it also includes: The standard working hours for assembling components of each order machine model are stored in a preset working hours database, and are respectively associated with the order information corresponding to the standard working hours for assembling components of each order machine model; When the standard working hours for component assembly of each order machine model changes, the working hours are updated based on a preset first working hour updating mechanism.

5. The method according to claim 1, characterized in that Before determining the test hours for each order machine model in the production order, it also includes: Obtaining a first configuration parameter of at least one current order machine model and a second configuration parameter of at least one historical order machine model; Identify the target configuration parameter that meets the similarity condition in each second configuration parameter, predict the aging completion time of each current order machine model based on the target configuration parameter and a preset correction coefficient of the current order machine model, and obtain the aging completion prediction time of each current order machine model; Building a machine aging prediction model based on the target configuration parameters and the aging completion time of each current order machine model, and obtaining the aging completion time of a new order machine model based on the machine aging prediction model; Generate aging test information of at least one order machine model based on a preset database management system, and build an aging database of the order machine model according to the aging test information, wherein the aging test information includes an order machine model configuration table, an aging test record table, and an aging completion schedule; The aging test record table is scanned based on a preset scanning time, and according to the current scanning time and the predicted completion time of the aging, all order machine models that will complete the aging within the next attendance day are predicted and screened to obtain the completed aging records of all order machine models within the next attendance day, and a machine resource pool to be tested is constructed based on the information of all order machines and the completed aging records within the next attendance day, and all the order machine models to be tested in the machine resource pool to be tested are associated with the test working hours of the order machine models to be tested.

6. The method according to claim 5, characterized in that Determining the test hours for each machine model in the order to be produced includes: Get the product inspection operation consumption time and product packaging operation consumption time of each order machine model in the production order to be produced; Calculating an average inspection operation time of the product based on the consumption time of each product inspection operation, and calculating an average packaging operation time of the product based on the consumption time of each product packaging operation; The standard inspection working hours for each order machine model are determined based on the average inspection operation time of the product and the preset debugging time. The standard packaging working hours for each order machine model are determined based on the average packaging operation time of the product and the preset debugging time. The standard inspection working hours and the standard packaging working hours are stored in a preset working hour database and associated with each order machine model.

7. The method according to claim 6, characterized in that After storing the inspection standard working hours and the packaging standard working hours in a preset working hours database and associating them with each order machine model, the method further includes: When it is detected that the inspection standard working hours of each order machine model or the packaging standard working hours of each order machine model changes, the working hours are updated based on a preset second working hour updating mechanism.

8. The method according to claim 1, characterized in that After obtaining the attendance accounting result of the assemblers based on the theoretical number of assemblers, the per capita skill level work efficiency coefficient and the total assembly time requirement, and obtaining the attendance accounting result of the testers based on the theoretical number of testers and the total test time requirement, the method further includes: Obtaining historical attendance personnel data, historical component assembly standard working hours and historical testing working hours for each order machine model; The attendance accounting results of the assemblers and the attendance accounting results of the testers are evaluated based on the historical attendance personnel data, the historical component assembly standard working hours and the historical testing working hours, and when the evaluation results do not meet the preset evaluation conditions, the attendance accounting results of the assemblers and the attendance accounting results of the testers are optimized and adjusted.

9. An automatic accounting device for attendance personnel, characterized in that: include: A determination module is used to determine the standard assembly time and test time of components for each order machine model in the production order and the product order quantity in each wave information; A calculation module, used to obtain the standard working hours of the whole machine of each order machine model according to the standard working hours of the parts assembly of each order machine model, and calculate the working hours requirement of the order machine in each wave information based on the product order quantity in each wave information and the standard working hours of the whole machine of each order machine model, and obtain the total assembly working hours requirement of all order machines according to the working hours requirement of the order machines in all wave information; A first acquisition module is used to determine the number of order machines to be tested in a resource pool of order machines to be tested, and obtain the total test man-hour requirement of the order machines to be tested based on the number of order machines to be tested and the test man-hour of each order machine model; The second acquisition module is used to determine the theoretical number of assemblers for each order machine model, the work efficiency coefficient of the average skill level of assemblers, and the theoretical number of testers for each order machine model, and obtain the attendance calculation results of the assemblers based on the theoretical number of assemblers, the work efficiency coefficient of the average skill level, and the total assembly working time requirement, and at the same time obtain the attendance calculation results of the testers based on the theoretical number of testers and the total testing working time requirement.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for automatically calculating attendance personnel as described in any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the automatic accounting method for attendance personnel as described in any one of claims 1 to 8.

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