Complex electronic equipment manufacturing process model simulation verification and optimization method

Abstract

The invention discloses a complex electronic equipment manufacturing process model simulation verification and optimization method. The method comprises the following steps: acquiring a process flow diagram in a manufacturing process model; performing chart conversion, resource library analysis and process flow diagram analysis on image-text data in the obtained process flow diagram, and taking data obtained by the chart conversion, the resource library analysis and the process flow diagram analysis as an input file of manufacturing process model simulation; carrying out operational analysis on the input file, and giving out warning and error information prompts; and modifying the manufacturing process model on the basis of a set rule and a production operation historical data optimization method until no error occurs in simulation. According to the method, the workload of repeatedly modifying the manufacturing process model can be reduced, the matching property of the manufacturing process model and the actual business is improved, and the predictability of the production process is improved, so that the optimization of the product process route, the controllable process quality and the achievable delivery plan are ensured.

Description

Technical Field

[0001] This application relates to the technical field of simulation verification and optimization, and particularly to a method for simulating, verifying and optimizing a manufacturing process model of complex electronic equipment. Background Art

[0002] The manufacturing process model is a type of graphic model developed for complex electronic equipment, similar to the industry's PBOM model, but with richer elements and connotations than PBOM. In addition to material information and hierarchical relationships, it also includes process flowcharts, personnel information, instrument and equipment information, workstation information, process methods, man-hour information, etc.

[0003] The manufacturing process model is the basis for realizing efficient automatic scheduling. Therefore, the accuracy of the manufacturing process model is very important. If the manufacturing process model is inaccurate or incorrect, the scheduling result will be inaccurate or unable to be scheduled, and the production process will not be able to run smoothly or even stagnate.

[0004] The existing technology basically simulates the manufacturing process model based on mature simulation software such as Plant Simulation, FlexSim, and AnyLogic, and the manufacturing process model is also a production line model established in the simulation software, mainly analyzing the impact of production line beat, bottleneck resources, logistics routes, etc. on production efficiency. However, there are many types of manufacturing process models established by each enterprise, not only limited to the manufacturing process models established using these industrial software, but also including self-compiled manufacturing process models. Currently, there is no simulation verification and optimization method applicable to this type of manufacturing process model. When the self-compiled manufacturing process model of an enterprise is used for scheduling, due to the lack of simulation verification and optimization, there are many problems such as a large number of missing elements and logical confusion in the model, resulting in the inability to perform planned scheduling, chaotic process routes, and uncontrollable process quality, causing problems such as chaos and even stagnation of materials, personnel, and instruments at the production site, and further leading to the inability to meet the delivery period and increased costs of the production schedule. Summary of the Invention

[0005] In order to avoid problems such as missing elements and logical confusion when the document-based manufacturing process model is applied to scheduling, this application provides a method for simulating, verifying and optimizing a manufacturing process model of complex electronic equipment, which is applicable to the simulation and optimization of the manufacturing process model of complex electronic equipment.

[0006] This application discloses a method for simulating, verifying and optimizing a manufacturing process model of complex electronic equipment, which includes: Step 1: Obtain the process flow chart in the manufacturing process model. The process flow chart consists of several block diagrams, and each block diagram contains process sequence number, process name, personnel requirement, man-hour requirement, material requirement, instrument requirement, and work station requirement. The block diagrams in the process flow chart are connected by arrows, and the arrows are used to represent the front-back position relationship between the block diagrams; Step 2: Perform chart conversion, resource library parsing, and process flow chart parsing on the text and graphic data in the obtained process flow chart, and use the data obtained from the chart conversion, resource library parsing, and process flow chart parsing as the input file for the manufacturing process model simulation; Step 3: Perform arithmetic analysis on the input file and give warning and error message prompts; Step 4: Modify the manufacturing process model based on established rules and production operation historical data optimization methods until no errors occur in the simulation.

[0007] Further, Step 2 includes: Chart conversion: Convert the text information read from the process flow chart into structured table data, and the structured table data includes process method table, formula table, work station table, instrument and equipment table, and personnel requirement table; Resource library parsing: Parse the data in the personnel library, work station library, and instrument and equipment library, and convert them into Json and Mat formats; Process flow chart parsing: It refers to parsing the relationships in the process flow chart, calculating the front-back sequence and the set of pre- and post-processes between processes, and forming a new data table, and then converting it into Json and Mat formats; Package the data obtained from the chart conversion, resource library parsing, and process flow chart parsing into a binary file as the input file for the simulation.

[0008] Further, Step 3 includes: Step 31: Initialize the basic information of the simulation object, and the basic information includes product number, planned start date, planned completion date, and scheduling quantity information; Step 32: Obtain the execution logic, execution cycle, resource requirements, and resource library of the simulation object from the database; the resource requirements include the requirements for instruments, work stations, and personnel; the resource library includes the instrument and equipment library, work station library, and personnel library; Step 33: According to the execution logic, execution cycle, resource requirements, and resource library of the simulation object, obtain the personnel, instruments, work stations, start and end times assigned to each process, and issue an exception alarm for the processes that do not match the resources in the resource library, and display warning and error messages.

[0009] Further, Step 33 includes: Step 331: Construct a constraint expression for the simulation operation according to the constraint conditions of the simulation operation; Step 332: Combine and code the simulation objects in the order of execution logic. According to the execution logic of the simulation objects, allocate resources for the resource requirements of each process, and record the total execution time of the tasks. Step 333: Decode and output the personnel, instruments, workstations, start and end times allocated for each process, raise an exception alarm for the processes that do not match resources from the resource library, display warning and error messages, and the simulation calculation ends. Further, the said Step 331 includes: When the constraint condition of the simulation operation is that one resource can execute at most one task at the same time, the constraint expression of the simulation operation is;

[0010] When the constraint condition of the simulation operation is that the next task can be executed only after the current task is completed, the constraint expression of the simulation operation is;

[0011] When the constraint condition of the simulation operation is that there is a precedence constraint relationship between processes, that is, the next process can be executed only after the previous process is completed, the constraint expression of the simulation operation is;

[0012] Among them, represents the execution time of task i, process j under the operation of personnel R; represents the start time of task i, process j under the operation of personnel R; t represents time; represents the set of time; is a decision variable equal to 0 or 1; b represents the total number of tasks; u represents the total number of processes; represents the start time of task v, process l under the operation of personnel R; represents the start time of task i, process j + 1 under the operation of personnel R.

[0013] Further, the said Step 4 includes: Step 41: Optimization based on established knowledge rules: After analyzing the manufacturing process model according to the established rules, for the problems existing in the model, some are modified manually and some are automatically modified by the element integrity recommendation algorithm; Step 42: Optimization based on historical data of production operation: According to historical MBOM data, instrument and equipment failure data, instrument and equipment resource data, workstation data, and process start and end data, the resource matching algorithm automatically recommends relevant data and generates a new manufacturing process model; the relevant data includes instrument types, workstation types, and man-hour data.

[0014] Furthermore, the operation process of the element integrity recommendation algorithm is as follows: Obtain the established rules for personnel, workstations, instruments, working hours, and materials; Input the element information of each process in the manufacturing process model into the corresponding rule categories respectively for analysis and calculation; Classify the calculation results into manual modification and automatic modification, and give a modification reminder. The modified results will overwrite the original results.

[0015] Furthermore, the operation process of the resource matching algorithm is as follows: Start resource matching calculation from the first process in the manufacturing process model, and traverse all processes included in the entire manufacturing process model in ascending order of process numbers; When calculating the resources for a certain process, according to specific mapping rules, calculate the historical actual operation data of this process of the project and the historical actual operation data of this process of similar projects; Compare the calculation results of the historical actual operation data with the data of this process in the current manufacturing process model to determine whether the value in the current manufacturing process model needs to be modified. If it needs to be modified, the new value will automatically overwrite the original value.

[0016] Furthermore, after step 4, it further includes: Data storage: Store the project name, project code, number of processes, number of error-type problems, number of warning-type problems, number of simulation times, and data of the modified model for each simulation.

[0017] Furthermore, the project name is the project name included in the manufacturing process model; the project code is the unique identification number of the project; the number of processes is the total number of processes in the manufacturing process model; the number of error-type problems is the sum of the number of error-type problems found in steps 3 and 4; the number of warning-type problems is the sum of the number of warning-type problems found in steps 3 and 4; the number of simulation times refers to the number of times of each simulation for the project; the modified model is the new version of the manufacturing process model that has been manually and automatically modified after simulation.

[0018] Due to the adoption of the above technical solutions, the present application has the following advantages: 1. This application provides a simulation verification and optimization method, which can realize the simulation verification and optimization of the document-based manufacturing process model. Through this method, problems such as missing elements and logical confusion in the manufacturing process model can be discovered, and improvement suggestions can be put forward. At the same time, based on the historical data of the enterprise's production operation, unreasonable elements in the manufacturing process model can be optimized, the accuracy of the manufacturing process model can be improved, and timely modification can be made at the development stage of the manufacturing process model, reducing the time cost of repeated communication between scheduling personnel and model developers after the model is finalized. This application can effectively improve the credibility of the manufacturing process model, support the smooth operation of production scheduling, reduce the situation of incorrect resource matching at the production site, improve product quality, ensure production progress, and save costs.

[0019] 2. This application can perform simulation analysis on the completed document-based manufacturing process model, analyze whether the elements in the model are complete and whether the logic is clear and reasonable, reduce the workload of repeated modification of the manufacturing process model, and at the same time, based on the historical data of the enterprise's production operation, optimize the unreasonable elements in the manufacturing process model, improve the matching degree between the manufacturing process model and the actual business, and improve the predictability of the production process, so as to ensure the optimization of the product process route, the controllability of the process quality, and the realization of the delivery plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of this application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments recorded in the embodiments of this application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a schematic diagram of the document-based manufacturing process model of the embodiment of this application; Figure 2 It is a schematic diagram of the process of the simulation operation of the embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The present application will be further described in conjunction with the drawings and embodiments. The described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art shall fall within the scope of protection of the embodiments of the present application.

[0023] See Figure 1 , this application provides an embodiment of a method for simulating, verifying and optimizing a manufacturing process model of complex electronic equipment, which includes: Step 1: Obtain the process flow chart in the manufacturing process model of the manufacturing process category. The process flow chart consists of several block diagrams, and each block diagram contains operation serial number, operation name, personnel requirement, man-hour requirement, material requirement, instrument requirement, and work station requirement. The block diagrams in the process flow chart are connected by arrows, and the arrows are used to represent the front and back position relationships of the block diagrams. Step 2: Perform chart conversion, resource library parsing, and process flow chart parsing on the graphic and text data in the obtained process flow chart, and use the data obtained from the chart conversion, resource library parsing, and process flow chart parsing as the input file for manufacturing process model simulation. Step 3: Perform arithmetic analysis on the input file and give warnings and error message prompts. Step 4: Modify the manufacturing process model based on established rules and production operation historical data optimization methods until no errors occur in the simulation.

[0024] The data obtained in Step 1 is to obtain the process flow chart in the manufacturing process model. The process flow chart consists of several block diagrams, and each block diagram contains information such as operation serial number, operation name, personnel requirement, man-hour requirement, material requirement, instrument requirement, and work station requirement. The block diagrams are connected by arrows, and the arrows are used to represent the front and back position relationships of the block diagrams.

[0025] The data parsing in Step 2 is to perform chart conversion, resource library parsing, and process flow chart parsing on the obtained data. The chart conversion refers to converting the text information read from the process flow chart into structured table data, including process methods, formula tables, work station tables, instrument and equipment tables, personnel requirement tables, etc. The resource library parsing is to parse the data in the personnel library, work station library, and instrument and equipment library and convert them into Json and Mat formats. The process flow chart parsing refers to parsing the relationships of the process flow chart, calculating the front and back sequences between operations and the set of pre-operation and post-operation, and forming a new data table, and then converting it into Json and Mat formats. Package the above three types of data into a binary file as the input file for simulation.

[0026] See Figure 2 , the simulation operation in Step 3 is to perform parallel arithmetic analysis on the obtained manufacturing process model data and give warnings and error message prompts.

[0027] 1) Initialize the basic information of the simulation object, including product number, planned start date, planned completion date, and scheduling quantity information; 2) Obtain the execution logic, execution cycle, resource requirements (including instruments, work stations, and personnel), and resource libraries (including instruments, work stations, and personnel) of the simulation object from the database; 3) Construct a constraint expression for the simulation operation according to the constraint conditions of the simulation operation; 4) Combine and encode the simulation objects in the order of execution logic. According to the execution logic of the simulation objects, allocate resources for the resource requirements of each process, and record the total execution time of the tasks. 5) Decode and output the personnel, instruments, workstations, start and end times allocated for each process. Raise an exception alarm for the processes that do not match resources from the resource library, display warning and error messages, and the simulation calculation ends. The constraint expression for constructing the simulation operation in step 3) is as follows: A resource can execute at most one task at the same time.

[0028] The next task can only be executed after the current task is completed.

[0029] There is a precedence constraint relationship between processes, that is, the next process can only be executed after the previous process is completed.

[0030] Among them, represents the execution time of task i, process j under the operation of personnel R; represents the start time of task i, process j under the operation of personnel R; represents the set of time; is a decision variable equal to 0 or 1.

[0031] The model optimization in step 4) is based on established rules and production operation historical data optimization methods to modify the manufacturing process model until no errors occur in the simulation.

[0032] 1) Optimization based on established knowledge rules: After analyzing the manufacturing process model according to the established rules, for the problems existing in the model, some are manually modified and some are automatically modified by the element integrity recommendation algorithm. The operation process of the element integrity recommendation algorithm in step 1) is as follows: a) Obtain the established rules for personnel, workstations, instruments, working hours, and materials. b) Input the element information of each process in the manufacturing process model into the corresponding rule class for analysis and calculation. c) Classify the calculation results into manual modification and automatic modification, give a modification reminder, and the modified results overwrite the original results.

[0033] 2) Optimization based on historical data of production operation: According to historical MBOM data, instrument and equipment failure data, instrument and equipment resource data, workstation data, and start and end data of processes, the resource matching algorithm automatically recommends instrument types, workstation types, man-hour data, etc. and generates a new manufacturing process model.

[0034] The operation process of the resource matching algorithm in step 2) is as follows: d) Start resource matching calculation from the first process of the manufacturing process model, and traverse all processes included in the entire manufacturing process model in ascending order of process numbers; e) When calculating the resources of a specific process, according to specific mapping rules, calculate the historical actual operation data of this process of this project and the historical actual operation data of this process of similar projects; f) Compare the calculation result of the historical actual operation data with the data of this process in the current model to determine whether the value in the current model needs to be modified. If it needs to be modified, the new value will automatically overwrite the original value.

[0035] Optionally, after step 4), it further includes: Data storage: Store the project name, project code, number of processes, number of error-type problems, number of warning-type problems, number of simulation times, and data of the modified model for each simulation. The project name is the project name included in this manufacturing process model, the project code is the unique identification number of the project, the number of processes is the total number of processes in this manufacturing process model, the number of error-type problems is the sum of the number of error-type problems found in steps 3 and 4, the number of warning-type problems is the sum of the number of warning-type problems found in steps 3 and 4, the number of simulation times is the number of times this simulation is for this project, and the modified model is the new version of the manufacturing process model that has been manually and automatically modified after simulation.

[0036] For easy understanding, this application gives a more specific embodiment: Assume that the manufacturing process model independently compiled by Project A is Figure 1 As shown, in order to discover problems such as missing elements and logical confusion in this model, the steps of using this method are as follows: S1: Data acquisition. Acquire the manufacturing process model of Project A, including all block diagrams, arrows, and text content in the block diagrams.

[0037] S2: Data parsing. Parse the content in each block diagram. The parsed process serial number serves as the first column of each subsequent table. Parse the "Process Name" row in the block diagram as the process method table, with attributes including name, process content description, process pre - relationship, process post - relationship, etc.; parse the "Personnel" row in the block diagram as the personnel requirement table, with attributes including personnel type, number of personnel, etc.; parse the "Cycle" row in the block diagram as the man - hour table, with attributes including cycle, operation man - hour, etc.; parse the other rows into other tables in sequence.

[0038] S3: Simulation operation.

[0039] 1) Initialize the basic information of Project A, as shown in Table 1. Assume the product numbers are NB12001, NB12002, NB12003, the batch number is K3839P01, the planned start date is September 1st, the planned completion date is September 15th, and scheduling is carried out according to the parallel work of 3 sets of products.

[0040] 2) Obtain the execution logic of Project A from the data parsed in S2. According to the process flow chart, determine that Process 3 and Process 4 are executed in parallel, and the rest of the processes are executed serially; obtain the execution cycle of Project A as 23.5 hours; obtain the personnel required for Project A as complete - set personnel, mechanical assembly personnel, common - property testing personnel, debugging personnel, testing personnel, common - property experiment personnel, inspection personnel, and warehouse keepers; obtain the specific instruments (signal source, spectrum analyzer, etc.) and workstation information (general integrated workstation) required for Project A; obtain various personnel models, instrument models, and workstation models required above from the database.

[0041] 3) Construct constraint expressions with the initialization information of Project A and the relevant information in the database. The complete - set personnel required for Process 1 preparation can execute at most one task at the same time, and Process 2 assembly can only be executed after Process 1 preparation is completed.

[0042] 4) Combine and code Project A in the order of the execution logic. According to the execution logic, allocate the resource requirements for each process, and record the total task execution time as 23.5 hours.

[0043] Table 1 Project basic information table

[0044] 5) Decode the personnel, instruments, tooling, start time, and end time allocated for each process to form the results shown in Table 2 - 1 and Table 2 - 2. Raise an exception alarm for the processes that do not match resources from the resource library, and display warning and error messages.

[0045] Table 2 - 1 Resource allocation information for each process

[0046] Table 2-2 Resource Allocation Information for Each Process (continued)

[0047] S4: Model Optimization

[0048] 1) Optimization Based on Established Knowledge Rules: Analyze the process method table parsed in S2 according to 39 established simulation rules. Compare the operation names with the standard operation names in the construction rules to determine whether the names are correct. If not, give an error prompt. Compare the precedence relationships with the block diagram arrows to determine whether the precedence relationships are correct. If not, give an error prompt.

[0049] Analyze the personnel requirement table parsed in S2 according to the established rules. Compare the personnel roles with the roles in the personnel model library to determine whether the roles are correct. If not, give an error prompt. Determine whether the characters filled in for the number of personnel are integers greater than 0 and cannot be any other characters. If not, give an error prompt. Compare the number of personnel with the number of personnel for similar products to determine whether it is reasonable. If not, give a warning prompt.

[0050] Analyze the man-hour table parsed in S2 according to the established rules. All filled cycle and operation man-hour data must be integers greater than 0 and cannot be any other characters. If not, give an error prompt. The value of the operation man-hour for the same process should be ≤ the value of the cycle. If not, give an error prompt. Compare the values of the cycle and operation man-hour with those of similar products to determine whether it is reasonable. If not, give a warning prompt.

[0051] For example, "Error: The name 'Performance Test' for Process 5 of Project A is incorrect. Please modify it according to the standard operation name", "Error: For Process 6 of Project A, the cycle is 4 hours and the operation man-hour is 6 hours. The operation man-hour is greater than the cycle man-hour. Please modify it.", "Error: The static test power supply module N10301 for Process 3 of Project A has duplicate materials. Please delete it." 2) Intelligent Optimization Based on Historical Data of Production Operations: According to the historical actual operation data of Project A and the historical actual operation data of similar products, traverse and calculate all the data tables parsed in S2 from Process 1 to Process 8. Compare the historical actual operation data with the data of this process in the current model to determine whether the value in the current model needs to be modified. If it needs to be modified, the new value will automatically overwrite the original value. For example, "Warning: The performance test cycle for Process 5 of Project A is 8 hours, and the historical actual operation data for 20 times is 4 hours. The error exceeds 20%. It has been automatically modified to 4 hours.", "Warning: The debugging station type for Process 4 of Project A is a large darkroom, and the dispatched station for the historical 14 operation data is a medium-sized darkroom. It has been automatically modified to a medium-sized darkroom." After S4, S5 is further included; S5: Data storage. Save the above simulation results as the first simulation results, and store the data of the project name, project code, number of processes, number of error-type problems, number of warning-type problems, number of simulation runs, and the modified model. Store the simulation results according to the format in Table 3, and the detailed information of the current simulation results is stored as shown in Table 4.

[0052] Table 3 Storage Format of Simulation Results

[0053] Table 4 Storage of Detailed Information of Simulation Results

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: It is still possible to modify the specific implementation manners of the present application or make equivalent replacements, and any modification or equivalent replacement that does not depart from the spirit and scope of the present application shall be covered by the protection scope of the claims of the present application.

Claims

1. A method for simulating, verifying and optimizing a complex electronic equipment manufacturing process model, characterized in that Including: Step 1: Obtain the process flow chart in the manufacturing process model. The process flow chart consists of several block diagrams, and each block diagram contains process sequence number, process name, personnel requirement, man-hour requirement, material requirement, instrument requirement, and work station requirement. The block diagrams in the process flow chart are connected by arrows, and the arrows are used to represent the front-back position relationship of the block diagrams; Step 2: Perform chart conversion, resource library parsing, and process flow chart parsing on the text and graphic data in the obtained process flow chart, and use the data obtained from the chart conversion, resource library parsing, and process flow chart parsing as the input file for manufacturing process model simulation; Step 3: Perform arithmetic analysis on the input file and give warnings and error message prompts; Step 4: Modify the manufacturing process model based on established rules and production operation historical data optimization methods until no errors occur in the simulation.

2. The method according to claim 1, characterized in that The said Step 2 includes: Chart conversion: Convert the text information read from the process flow chart into structured table data, and the structured table data includes process method table, formula table, work station table, instrument and equipment table, and personnel requirement table; Resource library parsing: Parse and perform format conversion on the data in the personnel library, work station library, and instrument and equipment library; Process flow chart parsing: It means parsing the relationship of the process flow chart, calculating the front-back sequence and the set of pre-processes and post-processes between processes and forming a new data table, and converting the format; Pack the data obtained from the chart conversion, resource library parsing, and process flow chart parsing into a binary file as the input file for simulation.

3. The method according to claim 1, characterized in that, The said Step 3 includes: Step 31: Initialize the basic information of the simulation object, and the basic information includes product number, planned start date, planned completion date, and scheduling quantity information; Step 32: Obtain the execution logic, execution cycle, resource requirements, and resource library of the simulation object from the database; The resource requirements include the requirements for instruments, work stations, and personnel; The resource library includes instrument and equipment library, work station library, and personnel library; Step 33: According to the execution logic, execution cycle, resource requirements, and resource library of the simulation object, obtain the personnel, instruments, work stations, start and end times assigned to each process, give an exception alarm for the processes that do not match resources from the resource library, and display the simulation error warning information.

4. The method according to claim 3, characterized in that The said Step 33 includes: Step 331: Construct a constraint expression for the simulation operation according to the constraint conditions of the simulation operation; Step 332: Combine and code the simulation objects in the order of execution logic, allocate resources for the resource requirements of each process according to the execution logic of the simulation object, and record the total execution time of the tasks; Step 333: Decode and output the personnel, instruments, work stations, start and end times assigned to each process, give an exception alarm for the processes that do not match resources from the resource library, display the simulation error warning information, and the simulation calculation ends.

5. The method according to claim 4, wherein The said Step 331 includes: When the constraint condition of the simulation operation is that one resource can execute at most one task at the same time, the constraint expression of the simulation operation is; When the constraint condition of the simulation operation is that the next task can only be executed after the current task is completed, the constraint expression of the simulation operation is; When the constraint condition of the simulation operation is that there is a tight - front - operation constraint relationship between processes, that is, the next process can only be executed after the previous process is completed, the constraint expression of the simulation operation is; Among them, represents the execution time of task i, process j under the operation of person R; represents the start time of task i, process j under the operation of person R; t represents time; represents the set of time; is a decision variable equal to 0 or 1; b represents the total number of tasks; u represents the total number of processes; represents the start time of task v, process l under the operation of person R; represents the start time of task i, process j + 1 under the operation of person R.

6. The method according to claim 1, characterized in that The said step 4 includes: Step 41: Optimization based on established knowledge rules: After analyzing the manufacturing process model according to the established rules, for the problems existing in the model, some are modified manually and some are automatically modified by the element integrity recommendation algorithm; Step 42: Optimization based on historical data of production operation: According to historical MBOM data, instrument equipment failure data, instrument equipment resource data, station data, and process start - and - end data, the resource matching algorithm automatically recommends relevant data and generates a new manufacturing process model; the relevant data includes instrument types, station types, and man - hour data.

7. The method according to claim 6, wherein The operation process of the said element integrity recommendation algorithm is: Obtain established rules for personnel, stations, instruments, man - hours, and materials; Input the element information of each process of the manufacturing process model into the corresponding rule categories respectively for analysis and calculation; Classify the calculation results into manual modification and automatic modification, and give a modification reminder. The modified results overwrite the original results.

8. The method according to claim 6, characterized in that The operation process of the said resource matching algorithm is: Start resource matching calculation from the first process of the manufacturing process model, and traverse all processes included in the entire manufacturing process model in ascending order of process numbers; When calculating the resources of a certain process, according to the preset mapping rules, calculate the historical actual operation data of this process of the project and the historical actual operation data of this process of similar projects; Compare the calculation result of the historical actual operation data with the data of this process in the current manufacturing process model to determine whether the value in the current manufacturing process model needs to be modified. If it needs to be modified, the new value will automatically overwrite the original value.

9. The method according to claim 1, characterized in that, After the said step 4, it also includes: Data storage: Store the project name, project code, number of processes, number of error - type problems, number of warning - type problems, number of simulation times, and data of the modified model for each simulation.

10. The method according to claim 9, wherein The said project name is the project name included in the manufacturing process model; the said project code is the unique identification number of the project; the said number of processes is the total number of processes in the manufacturing process model; the said number of error - type problems is the sum of the number of error - type problems found in step 3 and step 4; the said number of warning - type problems is the sum of the number of warning - type problems found in step 3 and step 4; the said number of simulation times refers to the number of times of each simulation for the project; the said modified model is the new version of the manufacturing process model that has been manually and automatically modified after simulation.

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