Production test lean production control method, system, platform and medium

Abstract

The invention discloses a production test lean production management and control method, system, platform and medium, relates to the technical field of electronic equipment manufacturing, and is used for solving the technical problem that in the prior art, lean management and control are difficult. The method is applied to a production test lean production management and control system comprising production test equipment, a data acquisition module, a business function module, a dynamic scheduling module, a statistical process control module, an electromagnetic simulation module and a digital twin module. A data acquisition module is adopted to carry out production real-time acquisition on the production test equipment to obtain production acquisition data; according to the production acquisition data, determining a target scheduling strategy from a plurality of scheduling strategies by adopting a dynamic scheduling module; generating a scheduling task according to the production plan and the target scheduling strategy; and according to the scheduling task, a service function module is adopted for product production and manufacturing. Therefore, the lean management and control difficulty is effectively reduced through systematic and digital quality management and control.

Description

Technical Field

[0001] This application relates to the field of electronic equipment manufacturing technology, and provides a method, system, platform and medium for lean production control of production testing. Background Technology

[0002] Due to the complexity of high-end electronic equipment functions, the high degree of integration of electromechanical and software disciplines, and the numerous layers of product composition, its manufacturing process covers multiple stages, including board level, module, complete machine, and system, making it extremely difficult to trace the source of anomalies in the production process. On the one hand, the types of equipment failures are diverse, their internal structures are complex, and the factors that induce failures are numerous and interdependent, making it extremely challenging to achieve rapid and accurate fault tracing based solely on test results. On the other hand, testing and manufacturing processes are heavily coupled, and fault adjustment involves all stages of the manufacturing process, including machining, micro-assembly, and assembly, making lean management of the entire manufacturing process extremely complex.

[0003] Furthermore, the industry has yet to develop systematic and efficient solutions to the problems of anomaly tracing in the manufacturing process of high-end electronic equipment and the challenges of lean management under the multiple coupling of testing and manufacturing processes. In particular, there is a lack of intelligent diagnostic and tracing systems that can span the entire process from board level to system level. Summary of the Invention

[0004] This application provides a method, system, platform, and medium for lean production control in production testing, which addresses the technical problems of difficulty in lean control in existing technologies.

[0005] On the one hand, a lean production management and control method for production testing is provided, applied to a lean production management and control system for production testing that includes production testing equipment, a data acquisition module, a business function module, a dynamic scheduling module, a statistical process control module, an electromagnetic simulation module, and a digital twin module. The method includes: Based on the received production plan, generate executable shift work orders; According to the shift work order, the data acquisition module is used to collect production data in real time from the production testing equipment to obtain production data; wherein, the production data includes equipment status, material consumption, order priority, process constraints and abnormal events; Based on the production data collected, the dynamic scheduling module determines the target scheduling strategy from multiple scheduling strategies; wherein, the multiple scheduling strategies include heuristic scheduling, genetic algorithm and real-time order insertion strategy; Based on the production plan and the target scheduling strategy, a scheduling task is generated; According to the scheduling task, the product manufacturing is carried out using the business function module.

[0006] Optionally, after manufacturing the product using the business function module according to the scheduled task, the method further includes: For the current process, the data acquisition module is used to collect data in real time on the production testing equipment to obtain test data; wherein, the test data includes process completion time, test start time, test end time and equipment status; Based on the test data and preset rules, determine whether the product being tested in the current process is qualified; If the product being tested in the current process is determined to be qualified, the product being tested will be transferred to the next process. If the product being tested in the current process is determined to be defective, the product exception handling process will be automatically triggered.

[0007] Optionally, after automatically triggering the product exception handling process, the method further includes: Receive exception tracing instructions generated by the operator clicking the exception tracing call button on the user interface; According to the anomaly tracing instruction, retrieve the product model of the product under test; Based on a pre-set fault mode knowledge base, the product model is subjected to parametric disturbance and simulation using an electromagnetic simulation module to obtain simulation results. The simulation results are compared with the test data collected by the product under test to obtain simulation comparison results; wherein, the simulation comparison results include combinations of parameter perturbations; The simulation comparison results are mapped to locate the abnormal source component information of the fault location of the tested product; Based on the information of the abnormal source component, an intelligent diagnostic report is generated; Based on the intelligent diagnostic report, the product manufacturing process of the tested product is improved.

[0008] Optionally, after mapping the simulation comparison results to locate the abnormal source component information of the fault location of the tested product, the method further includes: A statistical process control module is used to perform statistical analysis on the overall abnormality rate of the same batch of the tested product. A deep statistical process analysis was performed on the parameter perturbation combination of the tested product. Place the parameter disturbance combinations during abnormal periods in the historical control chart; Based on the process capability index of the parameter disturbance combination in the historical control chart, determine whether the current process is out of control.

[0009] Optionally, after automatically triggering the product exception handling process, the method further includes: Based on the status of the product under test on the production line, a digital twin module is used to synchronously update the digital twin model corresponding to the product under test.

[0010] Optionally, after the product to be tested is transferred to the next process, the method further includes: Based on the bill of materials and work report information, the material consumption is calculated using the statistical process control module. Based on the production plan and the aforementioned material consumption, calculate the inventory of various materials in the warehouse; If the inventory level of any material is determined to be less than the preset material threshold, an inventory warning will be triggered.

[0011] Optionally, the step of generating an executable shift work order based on the received production plan includes: Use an API gateway to receive production plans from Enterprise Resource Planning; Based on the received production plan, real-time equipment capacity constraints, material inventory constraints, and personnel allocation constraints, an executable shift work order is generated. The work orders for each shift are automatically sent to the production dashboard of the corresponding production line.

[0012] On the one hand, a lean production management and control system for production testing is provided, the system including production testing equipment, data acquisition module, business function module, dynamic scheduling module, statistical process control module, electromagnetic simulation module and digital twin module; The production and testing equipment is used to perform product production and testing. The data acquisition module is used to collect and report data from the production testing equipment; The aforementioned business function module is used for full-process management of the product manufacturing process; The dynamic scheduling module is used to schedule and optimize production tasks in real time. The statistical process control module is used for product quality control and continuous optimization. The electromagnetic simulation module is used to model the product under test and trace the source of product anomalies; The digital twin module is used to create a twin model of the product under test.

[0013] On the one hand, a lean production management and control platform for production testing is provided, the platform including a user interface layer, a business logic layer and a data access layer; The user interface layer is used for user interaction. The business logic layer is used to handle data requests between various services, as well as to persist or push data. The data access layer is used to collect and store various business information.

[0014] On the one hand, a storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement any of the methods described above.

[0015] Compared with the prior art, the beneficial effects of this application are as follows: In this application, when conducting lean production control for production testing, firstly, an executable shift work order can be generated based on the received production plan; then, based on the shift work order, the data acquisition module can be used to collect real-time production data from the production testing equipment to obtain production data; wherein, the production data includes equipment status, material consumption, order priority, process constraints, and abnormal events; next, based on the production data, the dynamic scheduling module can be used to determine a target scheduling strategy from multiple scheduling strategies; wherein, the multiple scheduling strategies include heuristic scheduling, genetic algorithms, and real-time order insertion strategies; then, a scheduling task can be generated based on the production plan and the target scheduling strategy; finally, based on the scheduling task, the business function module can be used to manufacture the product.

[0016] Therefore, in this application, since the entire product manufacturing process is directly automated using a lean production control system for testing, no manual intervention is required. Thus, compared to existing technologies, it provides a systematic and digital quality control approach for high-end electronic equipment manufacturing, reducing heavy reliance on human experience. Furthermore, because the target scheduling strategy is determined from multiple scheduling strategies, this application is applicable to various production scenarios, significantly expanding the scope of quality control. Attached Figure Description

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

[0018] Figure 1 An architecture diagram of a lean production control system for production testing provided in this application embodiment; Figure 2 A schematic diagram of a lean production control platform for production testing provided in this application embodiment; Figure 3 A schematic diagram of the production control process for skin antennas provided in this application embodiment; Figure 4This is a relationship mapping diagram of the antenna, TR component and chip provided in the embodiments of this application; Figure 5 A flowchart illustrating a lean production control method for production testing provided in this application embodiment; Figure 6 A schematic diagram of a product testing process provided in an embodiment of this application; Figure 7(a) is a top view of a digital twin 3D production line layout provided in an embodiment of this application; Figure 7(b) is a front view of a digital twin 3D production line layout provided in an embodiment of this application; Figure 8 This is a schematic diagram of a fault tracing process provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0020] Due to the complexity of high-end electronic equipment functions, the high degree of integration of electromechanical and software disciplines, and the numerous layers of product composition, its manufacturing process covers multiple stages, including board level, module, complete machine, and system, making it extremely difficult to trace the source of anomalies in the production process. On the one hand, the types of equipment failures are diverse, their internal structures are complex, and the factors that induce failures are numerous and interdependent, making it extremely challenging to achieve rapid and accurate fault tracing based solely on test results. On the other hand, testing and manufacturing processes are heavily coupled, and fault adjustment involves all stages of the manufacturing process, including machining, micro-assembly, and assembly, making lean management of the entire manufacturing process extremely complex.

[0021] Furthermore, the industry has yet to develop systematic and efficient solutions to the problems of anomaly tracing in the manufacturing process of high-end electronic equipment and the challenges of lean management under the multiple coupling of testing and manufacturing processes. In particular, there is a lack of intelligent diagnostic and tracing systems that can span the entire process from board level to system level.

[0022] Based on this, this application provides a lean production control method for production testing. In this method, firstly, an executable shift work order can be generated based on the received production plan. Then, based on the shift work order, the data acquisition module can be used to collect real-time production data from the production testing equipment to obtain production data. This production data includes equipment status, material consumption, order priority, process constraints, and abnormal events. Next, based on the production data, the dynamic scheduling module can determine a target scheduling strategy from multiple scheduling strategies. These multiple scheduling strategies include heuristic scheduling, genetic algorithms, and real-time order insertion strategies. Then, a scheduling task can be generated based on the production plan and the target scheduling strategy. Finally, based on the scheduling task, the business function module can be used to manufacture the product. Therefore, in this application, since the entire product manufacturing process is directly automated using a lean production control system for production testing, no manual intervention is required. Therefore, compared to existing technologies, it provides a systematic and digital quality control approach for high-end electronic equipment manufacturing, reducing the heavy reliance on human experience. Furthermore, since the target scheduling strategy is determined from multiple scheduling strategies, this application is applicable to various production scenarios, greatly improving the scope of quality control.

[0023] After introducing the design concept of the embodiments of this application, the following is a brief introduction to the application scenarios to which the technical solutions of the embodiments of this application can be applied. It should be noted that the application scenarios described below are only for illustrating the embodiments of this application and are not intended to limit the scope. In specific implementation, the technical solutions provided by the embodiments of this application can be flexibly applied according to actual needs.

[0024] like Figure 1 The diagram shown is an architecture diagram of a lean production control system for production testing provided in this application embodiment. Specifically, the system includes production testing equipment, a data acquisition module, a business function module, a dynamic scheduling module, a statistical process control module, an electromagnetic simulation module, and a digital twin module. The production testing equipment is used to perform product production and testing; in addition, the production testing equipment can also be used to report test results and manage the transfer of products between different devices.

[0025] The data acquisition module is used to collect and report data from the production and testing equipment; that is, it can be responsible for integrating production data, test data, and equipment report information, and encapsulating or converting them into structured data for reporting.

[0026] The aforementioned business function module is used for full-process management of product production; that is, it can be responsible for system-level functions such as user, role, menu management, access control and log management; as well as managing product production and testing information.

[0027] The dynamic scheduling module is used to efficiently schedule and optimize production tasks in real time.

[0028] The statistical process control module is used for product quality control and continuous optimization.

[0029] The electromagnetic simulation module is used to model the product under test and trace product anomalies. For example, it can be responsible for accurately modeling the antenna body, solving its intrinsic radiation characteristics through numerical calculation methods, obtaining the energy propagation law of electromagnetic waves in space, realizing the traceability of product anomalies, and dynamically feeding back the location of product anomalies.

[0030] The digital twin module is used to create a digital twin model of the product under test. That is, it can be responsible for creating a digital twin model of the actual product, achieving a virtual-real mapping, dynamically reflecting the production line status, and simulating production conditions.

[0031] In one possible implementation, this application uses Spring Boot as the underlying language to provide an agile development experience and dependency management.

[0032] In one possible implementation, this application uses MyBatis-Plus to work in conjunction with a MySQL database to ensure stable and persistent storage and efficient access to business data.

[0033] In one possible implementation, this application uses Apache Shiro as a security framework to finely manage the authentication and authorization of the system.

[0034] In one possible implementation, this application uses Freemarker templates in conjunction with the LayUI front-end framework, and performs page rendering management based on basic HTML, CSS and JavaScript, in order to quickly build a management and control interface with a unified style and rich functionality.

[0035] In one possible implementation, an integrated lean production control platform for production testing can also be built based on the lean production control system for production testing, such as... Figure 2The diagram illustrates a lean production management platform for production testing provided in this application. To achieve a highly cohesive and loosely coupled architecture, this lean production management platform adopts a classic three-layer design, constructing a user interface layer, a business logic layer, and a data access layer. It also incorporates microservices for modular encapsulation, ensuring the platform's scalability, maintainability, high security, and high performance. Furthermore, the core of this platform lies not only in its layered design but also in the collaborative business processes and data flows between each layer, particularly the production management closed-loop and intelligent fault tracing mechanism.

[0036] The user interface layer serves as the entry point for user interaction; specifically, it adopts a responsive web design and supports multi-terminal access. It achieves efficient and secure data interaction with the business logic layer through a RESTful API, realizing front-end and back-end separation.

[0037] like Figure 2 As shown, the user interface layer specifically includes a portal dashboard interface, a production execution interface, a test monitoring interface, a digital twin interface, and an anomaly tracing interface.

[0038] The portal dashboard provides management with real-time data-driven decision support. Through visualization libraries such as ECharts, it dynamically displays core KPIs such as production capacity, first-pass yield, overall equipment efficiency, and work-in-process distribution. It supports multi-dimensional analysis, such as comparisons by product line, work group, and time, to quickly pinpoint production bottlenecks.

[0039] The production execution interface provides operators with functions such as receiving work orders, viewing work instructions, scanning materials, reporting progress, reporting anomalies, and recording test results.

[0040] The test monitoring interface can display the test station status, test progress, real-time data curves, and pass / fail results in real time, and supports remote intervention and retest command issuance.

[0041] The digital twin interface displays the production line layout, real-time equipment status, equipment alarms, anomalies, material flow, and other information in 3D. Users can click on equipment to view detailed parameters and maintenance history.

[0042] The anomaly tracing interface can provide simulation results for product serial number queries, and link all related product production parameters and test results across the entire chain.

[0043] The business logic layer handles data requests between various services and persists or pushes data. Specifically, its main task is to process data requests with various services, performing necessary business logic, permission, and data status checks. Then, it persists or pushes the data to other business modules. These business modules cooperate with each other while remaining independent, enabling business interactions throughout the system, with the execution results displayed on the user interface.

[0044] like Figure 2 As shown, this business logic layer includes production management, test management, dynamic scheduling services, statistical process control services, a digital twin engine, and a simulation tracing engine. Based on this, the business logic layer can specifically implement closed-loop production control processes and fault tracing execution processes, such as... Figure 3 The diagram shown illustrates a production control process for a skin antenna according to an embodiment of this application. The production control process sequentially includes material loading, dispensing, patch mounting, AOI (Automated Optical Inspection), plasma cleaning, curing, bonding, AOI again, unloading, TR component testing, screw assembly, and skin antenna testing. Furthermore, as... Figure 4 The diagram shown is a relationship mapping diagram of antennas, TR components and chips provided in the embodiments of this application, wherein one skin antenna corresponds to multiple TR components, and one TR component corresponds to multiple multi-functional chips.

[0045] The data access layer is used to collect and store various business information. Specifically, this data access layer can use a MySQL relational database to store structured business information and adopts MyBatis-Plus to abstract a unified data access interface for easy expansion and maintenance. Furthermore, this data access layer can use an API gateway as a unified entry point, responsible for routing, authentication, rate limiting, and monitoring. For integration with other systems or modules, a unified RESTful API is used. It can be integrated with ERP systems to synchronize test production plans; and with PLM systems to automatically synchronize changes in product test specifications, etc.

[0046] Of course, the methods provided in the embodiments of this application are not limited to... Figure 1 The application scenarios shown can also be used in other possible scenarios, and this application embodiment does not impose any limitations. Figure 1 The functions that the various devices in the application scenarios shown can achieve will be described in subsequent method embodiments, and will not be elaborated on here. Below, the methods of the embodiments of this application will be described in conjunction with the accompanying drawings.

[0047] like Figure 5 The diagram shown is a flowchart of a lean production control method for production testing provided in this application. This method can... Figure 1The production testing is performed using a lean production control system. The specific process of this method is described below.

[0048] Step 501: Generate executable shift work orders based on the received production plan.

[0049] Specifically, firstly, an API gateway can be used to receive production plans from Enterprise Resource Planning (ERP) or plans manually created by users.

[0050] Then, based on the received production plan, real-time equipment capacity constraints, material inventory constraints, and personnel allocation constraints, executable shift work orders or daily plans can be generated.

[0051] Finally, shift work orders or daily plans can be automatically sent to the corresponding production line's production dashboard for staff to view.

[0052] Step 502: Based on the shift work order, use the data acquisition module to collect real-time production data from the production testing equipment to obtain production data.

[0053] In this application, the production data collected includes equipment status, material consumption, order priority, process constraints, and abnormal events.

[0054] Specifically, after each production step (e.g., dispensing, bonding, and curing) is completed by the operator or production testing equipment, the data acquisition module can collect production data in real time from the equipment PLC or testing instruments via the OPC UA protocol.

[0055] Step 503: Based on the production data collected, the target scheduling strategy is determined from multiple scheduling strategies using the dynamic scheduling module.

[0056] In this application, the multiple scheduling strategies include heuristic scheduling, genetic algorithms, and real-time order insertion strategies, which can be applied throughout the entire production process.

[0057] Specifically, when performing dynamic scheduling, the real-time production data can first be synchronized to the dynamic scheduling service as the input for dynamic scheduling.

[0058] Then, based on the production data collected, if it is determined that the current product production is a stable daily production (i.e., no anomalies or order interruptions), heuristic scheduling can be determined as the target scheduling strategy to achieve efficient and fast dynamic scheduling. Conversely, if it is determined that the current product production requires complex scheduling with multiple constraints (i.e., there are conflicts among multiple devices, multiple processes, and multiple work orders), genetic algorithm can be determined as the target scheduling strategy to pursue the optimal solution. Furthermore, if it is determined that the current product production is subject to unforeseen circumstances (e.g., emergency order interruptions, equipment failures, or material shortages), real-time order interruption strategy can be determined as the target scheduling strategy to achieve rapid scheduling response.

[0059] Step 504: Generate scheduling tasks based on the production plan and target scheduling strategy.

[0060] Step 505: According to the scheduling task, use the business function module to carry out product manufacturing.

[0061] Specifically, based on the scheduling instructions, the aforementioned business function modules can push scheduling tasks to the production dashboard, AG control system, and equipment terminals in real time to guide operators and automated equipment to execute the next task, thereby realizing the automatic continuous production and manufacturing of products.

[0062] In one possible implementation, during dynamic scheduling, when equipment malfunctions, processes are delayed, or materials are scarce, the order of work orders can be automatically adjusted to achieve real-time rescheduling.

[0063] In one possible implementation, the lean production control platform for production testing of this application also supports order insertion and priority scheduling, that is, it can support the insertion of urgent orders and automatically assess their impact on the original plan.

[0064] In one possible implementation, the lean production management platform for production testing of this application also supports resource balancing, that is, it can allocate the optimal production path according to equipment load, personnel skills and material availability.

[0065] In one possible implementation, after the product is manufactured using the aforementioned business function module for the current process, the manufactured product can also be tested, such as... Figure 6 The diagram shown is a schematic flowchart of a product testing process provided in an embodiment of this application.

[0066] Step 601: For the current process, use the data acquisition module to perform real-time testing and data acquisition on the production testing equipment to obtain the test data.

[0067] In this application, the test data collected includes process completion time, screw torque value, test start time, test end time, and equipment status.

[0068] Specifically, after each test step (e.g., scanning a code, starting a test) is completed by the operator or production test equipment, the data acquisition module can collect test data in real time from the equipment PLC or test instrument via the OPC UA protocol.

[0069] Step 602: Based on the test data and preset rules, determine whether the product being tested in the current process is qualified.

[0070] Specifically, after the test data is uploaded, "anomaly handling" can be performed. That is, based on the test data and preset rules (preset pass / fail rules), it can be automatically determined whether the tested product in the current process is qualified.

[0071] Step 603: If the product being tested in the current process is determined to be qualified, then the product being tested is transferred to the next process.

[0072] Step 604: If it is determined that the product being tested in the current process is unqualified, the product exception handling process will be automatically triggered.

[0073] In this application, while automatically triggering the product exception handling process, the impact of the exception on subsequent tasks can also be automatically assessed, and other executable work orders can be scheduled first.

[0074] In one possible implementation, "material consumption management" can also be performed after the product under test is transferred to the next process.

[0075] Specifically, firstly, based on the Bill of Materials (BOM) and work reporting information, the statistical process control module can be used to calculate material consumption.

[0076] Then, based on the production plan and the material consumption, the inventory of various materials in the warehouse can be calculated. If the inventory level of any material is determined to be less than a preset material threshold, an inventory warning is triggered. Conversely, if the inventory level of any material is determined to be not less than the preset material threshold, it indicates that the material of that type is sufficient for product manufacturing.

[0077] In one possible implementation, "equipment maintenance" can also be performed during production control. Specifically, the operating parameters of production testing equipment can be monitored; then, a predictive maintenance model can be used to predict the operating parameters; next, when a deviation in the predicted operating parameters is detected, an early warning message is generated to remind staff to perform equipment maintenance.

[0078] In one possible implementation, after automatically triggering the product anomaly handling process, the digital twin model can also be "state synchronized." Specifically, based on the status of the product under test on the production line, the digital twin module can be used to synchronize and update the digital twin model corresponding to the product under test in real time. As shown in Figure 7(a), it is a top view of the digital twin 3D production line layout provided in this application embodiment, and as shown in Figure 7(b), it is a front view of the digital twin 3D production line layout provided in this application embodiment. Furthermore, based on... Figures 7(a)-7(b) The 3D production line layout displayed on the digital twin visualization interface allows staff to not only understand the real-time status of equipment, equipment alarms, anomalies, material flow, and other information, but also to view detailed parameters and maintenance history data by clicking on the equipment.

[0079] In one possible implementation, to reduce the difficulty of lean management, improve production testing efficiency, and enhance equipment reliability and integrity, this application allows for "fault tracing" after automatically triggering the product anomaly handling process when the tested product fails to meet standards. Figure 8 The diagram shown is a flowchart illustrating a fault tracing method provided in an embodiment of this application.

[0080] Step 801: Receive the exception tracing instruction generated by the operator clicking the exception tracing call button on the user interface.

[0081] Step 802: Retrieve the product model of the product under test according to the anomaly tracing instruction.

[0082] That is, "model preparation and parameterization" can be performed. Specifically, based on the anomaly tracing instruction, the simulation tracing engine automatically retrieves the product model of the product under test, and determines the key electromagnetic performance indicators that need to be examined based on the failed test items.

[0083] Step 803: Based on the preset fault mode knowledge base, use the electromagnetic simulation module to perform parametric disturbance and simulation on the product model to obtain simulation results.

[0084] Specifically, firstly, "fault assumptions and parameter disturbances" can be performed. That is, based on a preset fault mode knowledge base (which is a common fault mode knowledge base), the electromagnetic simulation module can automatically introduce parameterized disturbances (such as increased local contact resistance, displacement of key components Δx, Δy, Δz, material property shifts, etc.) into the product model.

[0085] Then, "high-performance multiphysics coupling simulation" can be performed. That is, an electromagnetic simulation module can be used to execute large-scale parameter scanning simulations in parallel on a domestic supercomputing platform to accurately simulate the impact of the above-mentioned disturbances on the final electromagnetic performance, thereby obtaining simulation results.

[0086] Step 804: Compare the simulation results with the test data collected from the product under test to obtain the simulation comparison results.

[0087] In this application, the simulation comparison results include combinations of parameter perturbations.

[0088] In other words, "result comparison" can be performed. Specifically, the simulation results under various disturbance scenarios can be intelligently compared with the measured data of the faulty product (e.g., correlation analysis, feature extraction and matching) to obtain simulation comparison results, thereby locating one or more parameter disturbance combinations that cause the largest deviation between the measured and theoretical results.

[0089] Step 805: Map the simulation comparison results to locate the abnormal source component information of the fault location of the tested product.

[0090] Specifically, firstly, "root cause localization" can be performed, that is, the simulation comparison results can be mapped back to the specific assembly location or specific product in the physical world, thereby achieving precise localization from "performance abnormality" to "physical root cause".

[0091] Then, "simulation-driven root cause correlation" can be performed, that is, based on the physical root cause located by simulation, the simulation tracing engine can be used to automatically locate the source component information associated with the fault location of the tested product, so as to obtain its production process data, equipment parameters and production time, etc.

[0092] Step 806: Generate an intelligent diagnostic report based on the information of the component causing the anomaly.

[0093] In this application, the intelligent diagnostic report includes: ① test data of the fault phenomenon; ② the most likely root cause and confidence level located by simulation; ③ production traceability information of the affected components; ④ SPC control chart and capability analysis of relevant process parameters; and ⑤ improvement measures recommended based on the historical case library.

[0094] Step 807: Based on the intelligent diagnostic report, improve the product manufacturing process of the tested product.

[0095] In other words, "handling and closing the loop" can be implemented. Specifically, based on the corresponding intelligent diagnostic report, the product manufacturing process or technology of the tested product can be improved in a targeted manner, and operators can be guided to rework specific locations. After rework, the product can be retested, and the results are fed back to the system, forming a closed loop. Complete fault data, simulation analysis process, root cause location results, and verification effects are automatically structured and stored in a "fault-process" knowledge base. This knowledge base will be used to optimize future simulation diagnostic logic and improve process control strategies to achieve continuous self-learning of the system.

[0096] In one possible implementation, in order to achieve continuous self-learning of the system, after mapping the simulation comparison results and locating the abnormal source component information of the fault location of the tested product, "multi-dimensional statistical process control" can also be performed.

[0097] Specifically, firstly, a statistical process control module is used to statistically analyze the overall abnormality rate of the same batch of products being tested.

[0098] Then, in-depth statistical process analysis can be performed on the parameter perturbation combination of the tested product (i.e., the key process parameters located by simulation).

[0099] Next, the parameter disturbance combinations during abnormal periods can be placed in the historical control chart.

[0100] Finally, the process capability index of parameter disturbance combinations in historical control charts can be used to determine, based on statistical evidence, whether the current process is out of control.

[0101] In one possible implementation, during each product assembly, the relationships between products are mapped, and coordinate or position information is preserved. This mapping relationship should be progressive.

[0102] In one possible implementation, during product commissioning, commissioning result files can be recorded, with each test item and result file corresponding to the previous one.

[0103] In summary, this application has the following advantages: (1) Improved testing efficiency: The entire testing process is highly automated, covering all aspects such as test execution, data processing, result judgment, data recording, and anomaly tracing. No manual intervention is required from the start of the test to the output of the results. Among them, the anomaly tracing function traces the path of abnormal data in reverse, directly linking it to the initial production stage of the product, thereby accurately locating the root cause of the problem, accelerating the operation of the entire testing cycle, and enabling the product to complete the quality verification stage more quickly.

[0104] (2) Covering multi-level manufacturing processes: This application covers the entire process from board level, module, complete machine to system level, supports cross-stage anomaly tracing, and solves the problems of data fragmentation and lack of collaborative analysis tools in the past.

[0105] (3) Significantly shorten the time for anomaly investigation: Compared with traditional methods that rely on step-by-step physical detection, which is time-consuming and highly uncertain, this application can achieve rapid preliminary positioning through simulation and actual measurement comparison, thereby improving the efficiency of investigation.

[0106] (4) Supports anomaly propagation analysis in dynamic production environments: It can reconstruct the propagation path of anomalies in multi-stage manufacturing processes, helping to identify root causes and prevent similar problems from recurring.

[0107] (5) Improve the quality controllability of the whole process: provide a systematic and digital quality control approach for high-end electronic equipment manufacturing, and reduce the heavy reliance on human experience.

[0108] (6) Enhance equipment reliability and integrity: Rapid and accurate fault tracing helps improve the maintenance and support level of equipment and ensures the continued effectiveness of its strategic functions.

[0109] (7) Provide support for digital design and manufacturing: This application can be extended to electromagnetic compatibility prediction and process optimization in the product design stage, and promote the formation of an integrated digital link of "research and development-manufacturing-operation and maintenance".

[0110] In some possible implementations, various aspects of the methods provided in this application can also be implemented as a program product comprising program code that, when run on a computer device, causes the computer device to perform the steps of the methods according to the various exemplary embodiments of this application described above. For example, the computer device may perform actions such as... Figure 2 The method performed by the lean production control device in the production testing shown in the embodiment.

[0111] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. Alternatively, if the integrated units of this application are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of software products. These computer software products are stored in a storage medium and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0112] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0113] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A production test lean production management method characterized by, The method is applied to a production test lean production management and control system comprising a production test device, a data acquisition module, a business function module, a dynamic scheduling module, a statistical process control module, an electromagnetic simulation module, and a digital twin module, and comprises the following steps: According to the received production plan, an executable shift work order is generated; According to the shift work order, the production test device is subjected to real-time production acquisition by using the data acquisition module to obtain production acquisition data; wherein the production acquisition data comprises device state, material consumption, order priority, process constraint, and abnormal event; According to the production acquisition data, a target scheduling strategy is determined from a plurality of scheduling strategies by using the dynamic scheduling module; wherein the plurality of scheduling strategies comprises heuristic scheduling, genetic algorithm, and real-time order insertion strategy; According to the production plan and the target scheduling strategy, a scheduling task is generated; According to the scheduling task, product production and manufacturing are performed by using the business function module.

2. The method of claim 1, wherein, After product production and manufacturing are performed according to the scheduling task by using the business function module, the method further comprises the following steps: For the current process, the production test device is subjected to real-time test acquisition by using the data acquisition module to obtain test acquisition data; wherein the test acquisition data comprises process completion time, test start time, test end time, and device state; According to the test acquisition data and a preset rule, it is determined whether the measured product of the current process is qualified; If it is determined that the measured product of the current process is qualified, the measured product is transferred to the next process; If it is determined that the measured product of the current process is unqualified, a product abnormality processing procedure is automatically triggered.

3. The method of claim 2, wherein, After the product abnormality processing procedure is automatically triggered, the method further comprises the following steps: An abnormality tracing instruction generated by an operator clicking an abnormality tracing calling button on a user interface is received; According to the abnormality tracing instruction, a product model of the measured product is called; According to a preset fault mode knowledge base, parameterized disturbance and simulation of the product model are performed by using the electromagnetic simulation module to obtain a simulation result; The simulation result is compared with the test acquisition data of the measured product to obtain a simulation comparison result; wherein the simulation comparison result comprises a parameter disturbance combination; The simulation comparison result is mapped to locate abnormal source part information of a fault position of the measured product; According to the abnormal source part information, an intelligent diagnosis report is generated; According to the intelligent diagnosis report, the product production procedure of the measured product is improved.

4. The method of claim 3, wherein, After the simulation comparison result is mapped to locate the abnormal source part information of the fault position of the measured product, the method further comprises the following steps: By using the statistical process control module, overall abnormality proportion of the same batch of products of the measured product is statistically analyzed; The parameter disturbance combination of the measured product is subjected to in-depth statistical process analysis; The parameter disturbance combination in the abnormal period is placed in a historical control chart; According to the process capability index of the parameter disturbance combination in the historical control chart, it is determined whether the current process is out of control.

5. The method of claim 2, wherein, After the product abnormality processing procedure is automatically triggered, the method further comprises the following steps: According to the state of the measured product on the production line, the digital twin module is used to synchronously update the digital twin model corresponding to the measured product.

6. The method of claim 2, wherein, After the measured product is transferred to the next process, the method further comprises: According to the bill of materials and the work report information, the statistical process control module is used to calculate the material consumption; According to the production plan and the material consumption, the remaining amount of each type of material in the warehouse is calculated; For any type of material, if it is determined that the remaining amount of any type of material is less than the preset material threshold, an inventory warning is triggered.

7. The method of claim 1, wherein, The step of generating an executable shift work order according to the received production plan comprises: An API gateway is used to receive the production plan from the enterprise resource planning; According to the received production plan, real-time capability constraints of equipment, material inventory constraints and personnel configuration constraints, an executable shift work order is generated; The shift work order is automatically issued to the production board of the corresponding production line.

8. A production test lean production management system characterized by, The system comprises a production test device, a data acquisition module, a business function module, a dynamic scheduling module, a statistical process control module, an electromagnetic simulation module and a digital twin module; The production test device is used to perform production and testing of products. The data acquisition module is used to acquire and report data of the production test device. The business function module is used to manage the whole process of product production. The dynamic scheduling module is used to schedule and optimize production tasks in real time. The statistical process control module is used to control and continuously optimize product quality. The electromagnetic simulation module is used to model the measured product and trace the product abnormality. The digital twin module is used to model the measured product.

9. A production test lean production management and control platform, characterized by, The platform comprises a user interface layer, a business logic layer and a data access layer; The user interface layer is used for user interaction. The business logic layer is used to process data requests between services, and to persist or push data. The data access layer is used to collect and store various business information.

10. A storage medium, characterized by The storage medium stores computer executable instructions for causing a computer to execute the method of any one of claims 1-7.

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