Multi-level simulation optimization and quality control method and system for production process

Abstract

The invention relates to the technical field of intelligent manufacturing and data processing, in particular to a multi-level simulation optimization and quality control method and system for a production process, and the method comprises the steps: carrying out the intelligent analysis of a production task through an adaptive neural network, and generating task function data; carrying out multi-level simulation optimization on the geometric dimension, the material performance and the production process based on the task function data; the simulation optimization result data and the task function data are stored in a block chain, and data verification and processing are performed through an intelligent contract; a quality control instruction is generated based on the simulation optimization result and the task function data, the state of the production line is monitored in real time, and dynamic adjustment is carried out; through multi-level simulation optimization and intelligent quality control, the efficiency of the production process and the product quality are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing and data processing technology, and in particular to a multi-level simulation optimization and quality control method and system for a production process. Background Art

[0002] Efficient simulation and optimization can significantly improve production efficiency, reduce resource waste, improve product quality, and thus enhance corporate competitiveness. Driven by digitalization and intelligent manufacturing, the traditional production model is shifting towards intelligent and data-driven. The key to this transformation is to achieve comprehensive monitoring and optimization of the production process through simulation optimization and real-time quality control.

[0003] There are many problems in the current production process, mainly including the following aspects:

[0004] There are many uncertain factors in the production process, resulting in low production precision; traditional production methods rely on manual monitoring and management, with a low level of intelligence, making it difficult to respond and adjust in real time; quality inspection is highly dependent on manual labor, with low efficiency, poor precision, and high cost, making it impossible to achieve quality control throughout the entire process; data from the production site cannot be collected and analyzed in real time, resulting in delayed production decisions.

[0005] These problems are mainly caused by the limitations of traditional production methods and the lack of advanced digital tools.

[0006] Although the existing technology (Chinese invention patent, publication number: CN117391552B, title: Building component quality control system and method based on digital twin) has introduced a virtual production line and a twin database, it still has the following deficiencies or defects:

[0007] There are delays in data collection and transmission in existing technologies, and true real-time monitoring cannot be achieved; in the simulation optimization process, existing technologies lack comprehensive optimization of multiple levels and dimensions, and cannot comprehensively improve production efficiency and quality; although some intelligent algorithms have been introduced, the intelligence level of existing technologies is still insufficient, and it is difficult to cope with complex and changing production environments; existing technologies lack the ability to process and analyze data, and cannot make full use of big data for in-depth analysis and optimization. Summary of the invention

[0008] In view of the many problems existing in the above-mentioned prior art, the present invention provides a multi-level simulation optimization and quality control method and system for the production process. The present invention uses an adaptive neural network to perform intelligent analysis on production tasks and generate task function data; based on the task function data, multi-level simulation optimization is performed, including geometric size optimization, material performance optimization and production process optimization; the simulation optimization result data and the task function data are stored in the blockchain, and data verification and processing are performed through smart contracts; quality control instructions are generated based on the simulation optimization results and the task function data, the production line status is monitored in real time, and dynamic adjustments are made. Through these steps, the present invention realizes comprehensive optimization and quality control of the production process, and improves production efficiency and product quality.

[0009] A multi-level simulation optimization and quality control method for a production process comprises the following steps:

[0010] Acquiring target production task data, wherein the target production task data includes production targets, time requirements and quality standards;

[0011] Analyzing the target production task data through an adaptive neural network to generate task function data;

[0012] Performing multi-level simulation optimization based on the task function data includes the following steps: inputting the task function data into a simulation optimization module to generate a preliminary simulation model; performing multi-level optimization on the preliminary simulation model, including geometric size optimization, material performance optimization, and production process optimization; generating optimization suggestions based on the optimization results, and feeding back to the production control system;

[0013] The simulation optimization result data and task function data are stored in the blockchain, and the data is verified and processed through smart contracts;

[0014] Based on the simulation optimization results and the task function data, quality control instructions are generated, the production line status is monitored in real time, and dynamic adjustments are made to generate adjusted production task data.

[0015] Preferably, the obtaining of target production task data includes: obtaining detailed information of production targets, time requirements and quality standards from a management system, and converting these detailed information into target production task data, wherein the target production task data includes production target parameters, time parameters and quality standard parameters.

[0016] Preferably, analyzing the target production task data by an adaptive neural network includes:

[0017] Assess the complexity of the task and calculate the resources and time required to complete the task;

[0018] Analyze resource requirements and determine the materials, equipment and human resources required in the production process;

[0019] Assess potential quality risks and identify factors that may affect product quality;

[0020] Generate task function data, including task complexity parameters, resource requirement parameters and quality risk assessment results.

[0021] Preferably, inputting the task function data into the simulation optimization module to generate a preliminary simulation model comprises:

[0022] Build geometric models based on mission function data, including key dimensions and shape parameters;

[0023] Generate a finite element model based on the geometric model and set material properties and boundary conditions;

[0024] The finite element model is input into the simulation optimization module for preliminary simulation calculations.

[0025] Preferably, the multi-level optimization of the preliminary simulation model includes:

[0026] Geometry optimization, where the geometry is optimized according to the following calculation expression:

[0027]

[0028] Among them, O geo represents the objective function of geometric size optimization; d i Indicates the current size; represents the target size; n is the number of optimization dimensions;

[0029] Material performance optimization, where material performance is optimized according to the following calculation expressions:

[0030]

[0031] Among them, O mat represents the material performance optimization objective function; k j Indicates the weight of material performance parameters; p j Represents material performance parameters; m is the number of performance parameters;

[0032] Production process optimization, where the production process parameters are optimized by the following calculation expressions:

[0033]

[0034] Among them, O proc represents the production process optimization objective function; t k Indicates the current production process time; represents the target production process time; l is the number of production process steps.

[0035] Preferably, the geometric dimension optimization includes ensuring that all key dimensions meet a predetermined tolerance range and minimizing the dimension error using an iterative algorithm, wherein the iterative algorithm includes a genetic algorithm or a particle swarm optimization algorithm.

[0036] Preferably, the material performance optimization includes: selecting the best material combination to improve product strength and durability, material performance parameters include tensile strength, hardness and wear resistance, and calibrating the material performance model through experimental data to ensure the accuracy of the optimization results, including establishing a material performance database and performing model verification.

[0037] Preferably, the production process optimization includes: optimizing the sequence of production steps to improve production efficiency, the steps include process scheduling and equipment scheduling, and verifying the optimization plan through simulation and experimental data to ensure its feasibility, including simulating the operation of the production line and verifying production parameters.

[0038] Preferably, the generating of quality control instructions comprises:

[0039] Generate specific quality control measures based on simulation optimization results, including real-time monitoring parameters and adjustment measures;

[0040] Real-time monitoring of key parameters in the production process, and automatic adjustment of production parameters to maintain product quality, including temperature, pressure and speed.

[0041] A system for implementing the multi-level simulation optimization and quality control method for a production process, comprising:

[0042] A management system module is used to obtain target production task data, wherein the target production task data includes production targets, time requirements and quality standards;

[0043] An adaptive neural network module, used for analyzing the target production task data and generating task function data;

[0044] The simulation optimization module includes: a preliminary simulation submodule, which is used to generate a preliminary simulation model according to the task function data; a multi-level optimization submodule, which is used to optimize the geometric dimensions, material properties and production process of the preliminary simulation model; an optimization suggestion generation submodule, which is used to generate optimization suggestions according to the optimization results and feed them back to the production control system;

[0045] Blockchain data management module, which is used to store simulation optimization result data and task function data, and to verify and process data through smart contracts;

[0046] The quality control module is used to generate quality control instructions based on simulation optimization results and task function data, monitor the status of the production line in real time, make dynamic adjustments, and generate adjusted production task data;

[0047] Augmented reality interface module, used to display simulation results and quality inspection information in real time, and managers can monitor and adjust the production line through the AR interface;

[0048] Intelligent fault warning system, which uses adaptive neural networks to analyze production equipment and environmental data, predict potential faults and risks, provide early warnings and fault handling suggestions, and ensure the continuity and stability of the production process;

[0049] The feedback and adjustment module is used to dynamically adjust production tasks based on quality inspection results and early warning information, optimize production processes and quality control, and generate adjusted production task data.

[0050] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0051] The present invention realizes intelligent analysis and accurate prediction of production task data by means of adaptive neural network technology, effectively solving the problem of insufficient real-time data in the prior art;

[0052] The present invention realizes comprehensive optimization of the production process through multi-level simulation optimization technology, including geometric size optimization, material performance optimization and production process optimization, solving the defect of incomplete optimization in the prior art.

[0053] The present invention realizes the safe and reliable storage and verification of data through blockchain data storage and smart contract technology, ensures the data is tamper-proof and transparent, and enhances the data processing capability;

[0054] The present invention realizes real-time monitoring and adjustment of the production line status through real-time monitoring and dynamic adjustment technical means, improves the intelligent level of the production process, and can quickly respond to and adapt to complex and changeable production environments;

[0055] The present invention also provides a comprehensive, automated quality control system, which reduces dependence on manual labor and improves production efficiency and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the process of the present invention;

[0057] Figure 2 The data flow chart of the adaptive neural network analysis in the present invention;

[0058] Figure 3 It is a multi-level simulation optimization flow chart in the present invention;

[0059] Figure 4 This is a flow chart of data storage and verification in the present invention;

[0060] Figure 5 This is a flow chart of real-time monitoring and dynamic adjustment in the present invention;

[0061] Figure 6 It is a structural block diagram of the system of the present invention. DETAILED DESCRIPTION

[0062] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0063] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0064] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0065] like Figure 1 As shown, a multi-level simulation optimization and quality control method for a production process includes the following steps:

[0066] Acquiring target production task data, wherein the target production task data includes production targets, time requirements and quality standards;

[0067] Preferably, the obtaining of target production task data includes: obtaining detailed information of production targets, time requirements and quality standards from a management system, and converting these detailed information into target production task data, wherein the target production task data includes production target parameters, time parameters and quality standard parameters.

[0068] The principle of obtaining target production task data is to collect and organize the key information required in the production process through the management system. This information includes production goals (such as the specific quantity and type of production), time requirements (such as the specific time and schedule for production completion) and quality standards (such as the specific quality requirements and qualification standards of the product). By collecting these detailed information, the system can conduct a comprehensive analysis of the production task and provide accurate input data for subsequent simulation optimization.

[0069] In the present invention, the process of obtaining target production task data is not limited to simple data collection, but also includes converting these detailed information into target production task data suitable for further processing. These data include production target parameters, time parameters and quality standard parameters. Through this conversion, the system can process and analyze data more efficiently and ensure the consistency and accuracy of the data.

[0070] The specific steps include:

[0071] First, obtain detailed information on production goals, time requirements, and quality standards from the management system. The management system usually integrates the production management, resource planning, and quality management modules of the enterprise, and can provide comprehensive production task information.

[0072] Convert detailed information obtained from the management system into target production task data. This process involves converting descriptive information into quantifiable and calculable parameters. For example, production targets can be converted into production target parameters (such as product quantity and type), time requirements can be converted into time parameters (such as start time, end time, and time nodes for each stage), and quality standards can be converted into quality standard parameters (such as dimensional tolerances and material performance indicators).

[0073] These converted target production task data will be used as input and passed to the adaptive neural network for intelligent analysis. The adaptive neural network generates task function data, including task complexity parameters, resource requirement parameters and quality risk assessment results, by analyzing these data. These data provide the basis for subsequent simulation optimization and quality control.

[0074] like Figure 2 As shown, the target production task data is analyzed by an adaptive neural network to generate task function data;

[0075] Preferably, analyzing the target production task data by an adaptive neural network includes:

[0076] Assess the complexity of the task and calculate the resources and time required to complete the task;

[0077] Analyze resource requirements and determine the materials, equipment and human resources required in the production process;

[0078] Assess potential quality risks and identify factors that may affect product quality;

[0079] Generate task function data, including task complexity parameters, resource requirement parameters and quality risk assessment results.

[0080] Analyzing the target production task data through an adaptive neural network and generating task function data is a key step in achieving multi-level simulation optimization and quality control. Adaptive neural network is an advanced technology that improves prediction and analysis accuracy through training, learning and self-adjustment. In the present invention, the adaptive neural network is used to deeply analyze the target production task data and generate task function data including task complexity parameters, resource requirement parameters and quality risk assessment results. The core of this process is to ensure accurate and reliable data support for subsequent simulation optimization by comprehensively evaluating the complexity of production tasks, calculating the resources and time required to complete the tasks, analyzing resource requirements in detail, and evaluating potential quality risks.

[0081] Specifically, the adaptive neural network first evaluates the complexity of the task, a process that predicts its manufacturing difficulty and technical challenges by analyzing various aspects of the production task. For example, for a new product production task, the adaptive neural network can predict its manufacturing difficulty and possible technical challenges through historical data and current task data. The result of evaluating the complexity of the task generates task complexity parameters to help production managers prepare in advance and allocate appropriate resources and time. Next, the adaptive neural network calculates the resources and time required to complete the task, which includes the demand for materials, equipment and human resources, as well as the time required for each production stage. This calculation process not only takes into account the current production capacity, but also integrates historical data and potential production bottlenecks to generate resource demand parameters and time parameters.

[0082] In terms of resource demand analysis, the adaptive neural network determines in detail the various resources required in the production process, including the type and quantity of materials, the usage and maintenance requirements of equipment, and the required human resources and skill levels. For example, in a complex production task, the system can identify which key equipment needs to be maintained first to avoid downtime in the production process. By analyzing this data, detailed resource demand parameters are generated. Quality risk assessment is a key link in ensuring product quality. The adaptive neural network analyzes production task data and identifies factors that may affect product quality. These factors may include material stability, process complexity, and changes in the production environment. By identifying these risks in advance, the system can formulate corresponding preventive measures to ensure the stability and consistency of product quality. The evaluation results generate quality risk assessment results and provide specific preventive measures.

[0083] Through this systematic analysis process, the adaptive neural network can generate detailed task function data. These data not only improve the accuracy and efficiency of production task analysis, but also provide important basic data for subsequent multi-level simulation optimization and quality control. In general, the application of adaptive neural networks enables the system to comprehensively evaluate task complexity, accurately calculate resource and time requirements, analyze resource requirements in detail, and evaluate potential quality risks, thereby achieving efficient production management and optimization control. The generated task function data ensures the accuracy of the input data for simulation optimization, ultimately improving the overall efficiency of the production process and product quality.

[0084] like Figure 3 As shown, multi-level simulation optimization is performed based on the task function data, including the following steps: inputting the task function data into a simulation optimization module to generate a preliminary simulation model; performing multi-level optimization on the preliminary simulation model, including geometric size optimization, material performance optimization and production process optimization; generating optimization suggestions based on the optimization results, and feeding back to the production control system;

[0085] Multi-level simulation optimization based on the task function data is the core link of the present invention to achieve efficient production process management and quality control. The principle of multi-level simulation optimization is to ensure that all aspects of the production process are optimally controlled by gradually refining and optimizing the simulation model. This process includes the following main steps: inputting the task function data into the simulation optimization module to generate a preliminary simulation model; performing multi-level optimization on the preliminary simulation model, including geometric dimension optimization, material property optimization and production process optimization; generating optimization suggestions based on the optimization results and feeding them back to the production control system.

[0086] First, the task function data is input into the simulation optimization module to generate a preliminary simulation model. The core of this step is to convert complex production task parameters into a computable simulation model. Task function data includes task complexity parameters, resource requirement parameters and quality risk assessment results, which provide the necessary input for simulation optimization. Through the simulation optimization module, the system can generate a preliminary simulation model to simulate the basic behaviors and results of the production process. This preliminary model provides the basis for subsequent multi-level optimization.

[0087] Next, the preliminary simulation model is optimized at multiple levels. The multiple levels of optimization include geometry optimization, material properties optimization, and production process optimization. Geometry optimization aims to ensure that all critical dimensions in the production process meet the predetermined tolerance range and minimize dimensional errors through iterative algorithms. For example, when producing high-precision mechanical parts, geometry optimization can ensure that the dimensions of each part are within the allowable error range. Using genetic algorithms or particle swarm optimization algorithms can effectively reduce errors and improve product consistency and accuracy.

[0088] The goal of material performance optimization is to select the best material combination to improve the strength and durability of the product. By analyzing material performance parameters such as tensile strength, hardness and wear resistance, and calibrating the material performance model based on experimental data, the system can select the material combination that best suits production needs. For example, when producing high-strength alloy parts, material performance optimization can ensure that the selected alloy material has both sufficient strength and good wear resistance, thereby extending the service life of the parts.

[0089] Production process optimization improves production efficiency by optimizing the sequence of production steps and equipment scheduling. This process verifies the feasibility of the optimization plan through simulation and experimental data to ensure smooth and efficient production processes. For example, in a complex assembly task, production process optimization can reduce unnecessary waiting time in the production process and improve overall production efficiency by rearranging the sequence of processes and equipment scheduling.

[0090] Based on the results of multi-level optimization, the system generates optimization suggestions and feeds them back to the production control system. The optimization suggestions include specific adjustment measures and optimization schemes to guide the production line to make corresponding adjustments and optimizations. These suggestions include not only specific optimization schemes for geometric dimensions, material properties and production processes, but also real-time monitoring and dynamic adjustment measures to ensure continuous optimization and quality control of the production process.

[0091] Through the above multi-level simulation optimization, the system can achieve comprehensive and accurate production process control. Geometric optimization ensures high precision and consistency of the product, material performance optimization improves the strength and durability of the product, and production process optimization significantly improves production efficiency. In summary, multi-level simulation optimization based on task function data not only improves the overall efficiency of the production process and product quality, but also provides detailed optimization suggestions and implementation plans for the production control system, achieving efficient production management and quality control.

[0092] Preferably, inputting the task function data into the simulation optimization module to generate a preliminary simulation model comprises:

[0093] Build geometric models based on mission function data, including key dimensions and shape parameters;

[0094] Generate a finite element model based on the geometric model and set material properties and boundary conditions;

[0095] The finite element model is input into the simulation optimization module for preliminary simulation calculations.

[0096] First, a geometric model is constructed based on the mission function data. The principle of this step is to convert the mission function data into a visual geometric model, which includes key dimensions and shape parameters, which are the basis for accurate simulation. Through the geometric model, the system can intuitively represent the structural characteristics and dimensional requirements of the product, thereby providing the necessary geometric information for subsequent finite element analysis. For example, in the production process of mechanical parts, the geometric model can accurately represent the key dimensions of the part, such as shape, aperture, thickness, etc., to ensure that all important geometric features are taken into account during the simulation process.

[0097] Next, a finite element model is generated based on the geometric model. The principle of the finite element model is to discretize the complex geometric model into a finite number of units, and simulate the physical behavior of the overall model by analyzing these units. When generating a finite element model, it is necessary to set material properties and boundary conditions. Material properties include the elastic modulus, density, tensile strength, etc. of the material, while boundary conditions include external loading, fixed constraints, etc. Through these settings, the finite element model can accurately simulate the stress, deformation, heat conduction and other behaviors of the product in the actual production process. For example, in the production of aviation components, the finite element model can simulate the stress conditions of the components under high temperature and high pressure conditions, and predict possible stress concentration and deformation areas.

[0098] Finally, the finite element model is input into the simulation optimization module for preliminary simulation calculation. The principle of preliminary simulation calculation is to use the finite element analysis method to perform numerical calculation on the model to simulate the physical behavior and performance of the product under various working conditions. Through preliminary simulation calculation, the system can obtain data such as stress distribution, deformation, temperature field, etc. of the product under different working conditions. These data provide preliminary analysis results for subsequent multi-level optimization. For example, in the production of automotive parts, preliminary simulation calculation can simulate the stress conditions of parts under different load conditions, identify possible weak points and failure risks, and thus provide a reference for subsequent optimization.

[0099] Through the above steps, the system can generate an accurate preliminary simulation model, providing a reliable data basis for subsequent multi-level simulation optimization. The construction of geometric models and finite element models ensures the accuracy and comprehensiveness of the simulation process, while the preliminary simulation calculation provides a performance prediction of the product under actual production conditions. In general, by inputting task function data into the simulation optimization module and generating a preliminary simulation model, the system can fully simulate various physical behaviors in the production process, provide necessary data support for multi-level optimization and quality control, and improve the overall efficiency of the production process and product quality.

[0100] Multi-level optimization of the preliminary simulation model is a core step to achieve efficient production process management and quality control. Multi-level optimization includes geometry optimization, material performance optimization and production process optimization. The goal of each optimization step is to ensure that all aspects of the production process are in the best state.

[0101] Preferably, the multi-level optimization of the preliminary simulation model includes:

[0102] The principle of geometric dimension optimization is to optimize the geometric dimension parameters to achieve the best dimensional accuracy of the product; geometric dimension optimization, in which the geometric dimension is optimized according to the following calculation expression:

[0103]

[0104] Among them, O geo represents the objective function of geometric size optimization; d i Indicates the current size; represents the target size; n is the number of optimized dimensions; the goal of this expression is to minimize the deviation between the actual size and the target size, thereby ensuring the dimensional accuracy of the product. For example, in precision machining, through geometric optimization, the system can adjust the machining parameters to ensure that the key dimensions of each part are within the allowable tolerance range, thereby improving the consistency and accuracy of the product.

[0105] The principle of material performance optimization is to ensure the best performance of the product by optimizing material parameters; material performance optimization, in which material performance is optimized according to the following calculation expression:

[0106]

[0107] Among them, O mat represents the material performance optimization objective function; k j Indicates the weight of material performance parameters; p j represents the material performance parameter; m is the number of performance parameters; the goal of this expression is to maximize the overall performance of the product by optimizing the combination of various material performance parameters. For example, in the production of aviation parts, material performance optimization can select the best material combination to improve the strength and durability of the parts. By analyzing parameters such as tensile strength, hardness, and wear resistance of the material, the system can determine the optimal material combination, thereby improving the quality and service life of the product.

[0108] The principle of production process optimization is to optimize production efficiency by adjusting production process parameters; production process optimization, in which the production process parameters are optimized through the following calculation expression:

[0109]

[0110] Among them, O proc represents the production process optimization objective function; t k Indicates the current production process time; represents the target production process time; l is the number of production process steps. The goal of this expression is to minimize the overall production process time by optimizing the timing of each production step, thereby improving production efficiency. For example, in the automobile manufacturing process, through production process optimization, the system can adjust the sequence and timing of each process, reduce waiting time and resource waste in production, and thus improve overall production efficiency.

[0111] Through geometry optimization, material performance optimization and production process optimization, the system can comprehensively improve all aspects of the production process. Geometry optimization ensures high precision and consistency of the product, material performance optimization improves the strength and durability of the product, and production process optimization significantly improves production efficiency. These optimization steps work together to form a complete multi-level optimization system, which provides a solid foundation for efficient management and quality control of the production process. In general, through multi-level optimization of the preliminary simulation model, the system can achieve comprehensive optimization of the production process and improve overall production efficiency and product quality.

[0112] Preferably, the geometric dimension optimization includes ensuring that all key dimensions meet a predetermined tolerance range and minimizing the dimension error using an iterative algorithm, wherein the iterative algorithm includes a genetic algorithm or a particle swarm optimization algorithm.

[0113] The principle of geometric optimization is to adjust and optimize geometric parameters so that the produced products can meet the predetermined dimensional tolerance range. First, the system will identify the key dimensions in the product design, which are the key factors for product performance and quality. For example, in the manufacture of mechanical parts, the diameter of the shaft, the diameter of the hole, and the surface flatness are all key dimensions, which directly affect the assembly accuracy and performance of the parts.

[0114] After determining the critical dimensions, the system uses an iterative algorithm to minimize the dimensional error. An iterative algorithm is a method that approaches the optimal solution by repeatedly adjusting parameters. In the present invention, commonly used iterative algorithms include genetic algorithms and particle swarm optimization algorithms. Genetic algorithms imitate the process of natural selection and genetic variation, and continuously optimize dimensional parameters through selection, crossover and mutation operations. Particle swarm optimization algorithms simulate the foraging behavior of bird flocks and find the optimal dimensional parameters by adjusting the position and speed of particles. The application of these algorithms can effectively reduce dimensional errors and make the actual product size as close to the design target as possible.

[0115] In the specific implementation process, the geometry optimization first evaluates the key dimensions in the preliminary simulation model and determines the current error of each dimension. The system then uses genetic algorithms or particle swarm optimization algorithms to iteratively calculate and adjust the dimensional parameters to minimize the error. For example, for a complex mechanical part, through geometry optimization, the system can identify and adjust the processing parameters that cause dimensional deviations, and ultimately make each key dimension of the part within the allowable tolerance range.

[0116] Through this optimization, not only can the high precision and consistency of the product be ensured, but also the rework and scrap rate can be reduced, and production efficiency and product quality can be improved. The optimized dimensional parameters will be applied to the actual production control system to guide the processing equipment to perform precise processing, thereby ensuring that each produced part meets the design requirements.

[0117] Example: Taking the manufacture of automobile engine cylinder blocks as an example, the key dimensions of the cylinder block include the diameter of the cylinder bore, the hole spacing and the surface flatness. Through geometric dimension optimization, the system first evaluates the preliminary simulation model and identifies the current dimensional error of the cylinder bore. Next, a genetic algorithm is used for iterative calculations to adjust the processing parameters (such as tool feed speed, cutting depth, etc.) to gradually reduce the dimensional error so that the diameter and hole spacing of the cylinder bore meet the design requirements. After multiple rounds of iterative optimization, a set of optimal processing parameters is finally generated. These parameters are applied to CNC machine tools to guide them for precise processing, ensuring that the size of each cylinder bore is within the predetermined tolerance range, thereby improving the overall performance and service life of the engine.

[0118] Through geometric optimization, the system can effectively control the key dimensions of the product to ensure that it meets the predetermined tolerance requirements. The application of genetic algorithms and particle swarm optimization algorithms makes the dimension optimization process more efficient and accurate. The optimized dimensional parameters not only improve the quality and consistency of the product, but also reduce rework and scrap in production, significantly improving production efficiency and economic benefits. Overall, geometric optimization is an important part of achieving efficient production process management and quality control, and provides strong technical support for the production system.

[0119] Preferably, the material performance optimization includes: selecting the best material combination to improve product strength and durability, material performance parameters include tensile strength, hardness and wear resistance, and calibrating the material performance model through experimental data to ensure the accuracy of the optimization results, including establishing a material performance database and performing model verification.

[0120] The first task of material performance optimization is to select the best material combination to improve the strength and durability of the product. Strength and durability are important indicators for evaluating material performance, among which tensile strength, hardness and wear resistance are the most critical parameters. Tensile strength reflects the ability of a material to resist deformation and fracture under tension; hardness indicates the ability of a material surface to resist local deformation; and wear resistance refers to the ability of a material to resist wear under friction.

[0121] During the optimization process, the system first analyzes and compares the performance parameters of different materials based on the design requirements and the use environment. Through this analysis, the system can determine which material combinations can provide the best performance under specific use conditions. For example, when manufacturing high-strength mechanical parts, the system may choose alloy materials with high tensile strength and hardness to ensure the service life of the parts under high load conditions.

[0122] In order to ensure the accuracy of the optimization results, material performance optimization also requires calibrating the material performance model through experimental data. Specifically, the system will conduct experimental tests on the selected materials to obtain their performance data under different conditions. These data will be used to calibrate and verify the material performance model to ensure that the model can accurately reflect the actual performance of the material. For example, by performing tensile tests and hardness tests on different batches of materials, the system can establish an accurate material performance database, so that these data can be used for calculations and predictions during the simulation and optimization process.

[0123] In the process of material performance optimization, establishing a material performance database is a key link. The material performance database contains detailed performance parameters and experimental data of various materials, providing rich data support for the system. Through the database, the system can quickly query and compare the performance of different materials and make the best material selection. In addition, the material performance database also supports model verification. By comparing the actual experimental results and the model prediction results, the system can continuously optimize and calibrate the material performance model to ensure the reliability of the optimization results.

[0124] Example: Taking the manufacture of aircraft engine blades as an example, the selection of blade materials is crucial to engine performance. In order to improve the strength and durability of the blades, the system first performs a performance analysis on a variety of high-temperature alloy materials to compare their tensile strength, hardness, and wear resistance. After analysis, the system selects several candidate materials. Next, the system conducts experimental tests on these materials to obtain their performance data under high temperature and high pressure conditions. These data will be used to calibrate the material performance model and establish a detailed material performance database. Through model verification, the system confirms that the performance of the selected material under simulated working conditions is as expected. Finally, the system selects a high-temperature alloy material as the best material combination for blade manufacturing, thereby ensuring that the engine blades have excellent strength and durability.

[0125] Through material performance optimization, the system can accurately select and combine materials to significantly improve the strength and durability of the product. Experimental data calibration and model verification during the optimization process ensure the accuracy and reliability of the material performance model. The establishment of a material performance database provides the system with rich performance data support, making the optimization process more efficient and accurate. Overall, material performance optimization is an important part of achieving efficient production process management and quality control, and provides a solid technical guarantee for the high performance and long life of the product.

[0126] Preferably, the production process optimization includes: optimizing the sequence of production steps to improve production efficiency, the steps include process scheduling and equipment scheduling, and verifying the optimization plan through simulation and experimental data to ensure its feasibility, including simulating the operation of the production line and verifying production parameters.

[0127] The first task of production process optimization is to optimize the sequence of production steps. Through detailed analysis of production tasks, the system can identify the dependencies and time consumption between the various steps in the production process. Optimizing the sequence of steps can reduce waiting time and idle resources in production and improve the overall efficiency of the production line. For example, in the automobile manufacturing process, the system can analyze the various processes of the assembly line, rearrange the assembly sequence, reduce the waiting time between processes, and thus speed up the overall production progress.

[0128] Process scheduling and equipment scheduling are the core links of production process optimization. Process scheduling involves allocating production tasks to different processes and workstations to ensure balanced workloads in each process. Equipment scheduling refers to the reasonable arrangement and allocation of the use time and sequence of production equipment to ensure efficient use of equipment and minimize downtime. By optimizing process scheduling and equipment scheduling, the system can achieve the optimal configuration of production resources and avoid waste of resources and bottlenecks. For example, in electronic product manufacturing, by optimizing equipment scheduling, the system can ensure that each device runs at the most appropriate time, improving equipment utilization and production efficiency.

[0129] In order to verify the feasibility of the optimization plan, the production process optimization needs to be verified through simulation and experimental data. Simulating the operation of the production line is an important means to verify the optimization plan. By establishing a simulation model of the production line, the system can simulate various situations in actual production and evaluate the performance of the optimization plan under different conditions. For example, the system can simulate the operation of the production line through a simulation model, observe the performance of the optimized process arrangement and equipment scheduling under different production loads, and ensure that the plan is feasible and reliable in actual production.

[0130] On the basis of simulating the operation of the production line, the system also needs to verify the accuracy of the production parameters through experimental data. By conducting experiments in actual production, the system can obtain real production data and calibrate and verify the simulation model and optimization plan. For example, in the production process of a new product, the system can verify whether the optimized process arrangement and equipment scheduling can effectively improve production efficiency and product quality through small-scale trial production. By comparing experimental data and simulation results, the system can further optimize production parameters to ensure the effectiveness and feasibility of the final plan.

[0131] Example: Taking the manufacturing of smartphones as an example, the production process optimization first identifies possible bottlenecks and inefficient links in the assembly process by analyzing each assembly step in detail. Next, the system optimizes the order of the assembly steps, arranges some processes that can be processed in parallel together, and reduces the waiting time between processes. Then, by reasonably arranging the use time of the equipment, it is ensured that each device is running at the most appropriate time point to improve equipment utilization. In order to verify the optimization plan, the system establishes a simulation model of the smartphone assembly line, simulates the optimized production process, and evaluates its performance under different production loads. Finally, through small-scale trial production, actual production data is obtained, and the simulation model and optimization plan are calibrated and verified to ensure the feasibility and reliability of the plan.

[0132] Through production process optimization, the system can significantly improve production efficiency and resource utilization. The optimized production step sequence, process arrangement and equipment scheduling ensure the smooth and efficient operation of the production process. Through simulation and experimental data verification, the system can ensure the feasibility and effectiveness of the optimization scheme in actual production. Overall, production process optimization is an important link in achieving efficient production management and quality control, providing strong technical support for the production system and improving overall production efficiency and product quality.

[0133] like Figure 4 As shown, the simulation optimization result data and task function data are stored in the blockchain, and the data is verified and processed through smart contracts;

[0134] The simulation optimization result data and task function data are stored in the blockchain. Blockchain is a distributed ledger technology that ensures the security and immutability of data through decentralization and encryption technology. In the present invention, the simulation optimization result data includes production parameters and quality control strategies after multi-level optimization, while the task function data includes the complexity of the production task, resource requirements and quality risk assessment results. Storing these data in the blockchain can ensure the transparency and security of all key data in the production process.

[0135] The principle of the storage process is to package data into blocks and verify and store them through nodes in the blockchain network. Each block contains a timestamp and the hash value of the previous block, forming an immutable chain. In this way, any modification of the data will be detected by the entire network, ensuring the integrity and authenticity of the data. For example, in a production task, the system can store the task's complexity parameters, resource requirement parameters, and quality risk assessment results on the blockchain to ensure that these key data will not be tampered with or lost.

[0136] Data verification and processing through smart contracts is another major advantage of blockchain technology. Smart contracts are self-executing codes that can automatically perform predetermined operations when specific conditions are met. In the present invention, smart contracts can be used to verify the correctness of simulation optimization result data and task function data, and perform corresponding processing based on the verification results. The principle of smart contracts is to automatically perform data verification and processing operations through predefined rules and conditions. For example, the system can define a series of verification rules, such as data format check, value range check, etc., through smart contracts. When new data is stored in the blockchain, the smart contract will automatically execute these verification rules to ensure the accuracy and integrity of the data.

[0137] The application of smart contracts not only improves the efficiency of data processing, but also reduces the possibility of human intervention, and reduces errors and risks in data processing. Through smart contracts, the system can automatically complete data verification and processing while storing data, ensuring that all data meets the predetermined standards and requirements. For example, in a complex production task, the system can automatically verify the accuracy of each production parameter through smart contracts, and automatically trigger early warnings or adjust production plans when abnormalities are found to ensure the smooth progress of the production process.

[0138] Example: Taking the manufacturing of precision mechanical parts as an example, the simulation optimization result data includes optimized processing parameters such as cutting speed, feed rate and tool selection. Task function data includes the complexity of the production task, the required materials and equipment resources, and the quality risk assessment results. The system first packages this data into blocks and stores them through the blockchain network. Next, the smart contract automatically performs data validation to check the format, value range and consistency of the data. If the data passes the verification, the smart contract will store it on the blockchain and update the corresponding production plan and quality control strategy. If the data fails to pass the verification, the smart contract will automatically trigger an early warning to notify the production management personnel to check and adjust.

[0139] like Figure 5 As shown, quality control instructions are generated based on the simulation optimization results and task function data, the production line status is monitored in real time, and dynamic adjustments are made to generate adjusted production task data.

[0140] Preferably, the generating of quality control instructions comprises:

[0141] Generate specific quality control measures based on simulation optimization results, including real-time monitoring parameters and adjustment measures;

[0142] Real-time monitoring of key parameters in the production process, and automatic adjustment of production parameters to maintain product quality, including temperature, pressure and speed.

[0143] Generate specific quality control measures based on simulation optimization results. The simulation optimization results provide multi-level optimized production parameters and quality control strategies, which provide the basis for formulating specific quality control measures. Quality control measures include real-time monitoring parameters and adjustment measures to ensure that any deviation from the predetermined standards can be discovered and corrected in time during the production process. For example, when producing high-precision mechanical parts, the simulation optimization results may include the best cutting speed and feed rate, and the quality control measures will include real-time monitoring and adjustment plans for these parameters.

[0144] In the process of generating quality control instructions, the system will combine the simulation optimization results and the task function data to develop a detailed quality control plan. This plan includes real-time monitoring and adjustment measures for key production parameters such as temperature, pressure and speed. These key parameters directly affect the quality and performance of the product. By monitoring these parameters in real time, the system can detect any abnormalities in time and take corresponding adjustment measures. For example, in the metal processing process, changes in temperature and pressure will directly affect the processing accuracy and material properties. By monitoring and adjusting these parameters in real time, the stability of the processing process and the reliability of product quality can be ensured.

[0145] Real-time monitoring of key parameters in the production process is the core of quality control. The system obtains key parameter data on the production line in real time through sensor networks and data acquisition equipment, and transmits this data to the quality control system for analysis and processing. The principle of real-time monitoring is to detect any deviation from the predetermined standard in a timely manner through continuous tracking of key parameters, and automatically trigger adjustment measures. For example, during the injection molding process, the system can monitor the mold temperature and injection pressure in real time. When it detects that the temperature is too high or the pressure is insufficient, the system will automatically adjust the corresponding production parameters to maintain product quality.

[0146] Automatic adjustment of production parameters is a key step in quality control. By analyzing real-time monitoring data, the system can automatically adjust production parameters to ensure the stability of product quality. The principle of this process is to automatically adjust the operating status of production equipment according to real-time monitoring data through a preset control algorithm. For example, in the production of chemical preparations, the system can automatically adjust the heating power and stirring speed according to the real-time monitored temperature and reaction speed to ensure the stability of the reaction process and the consistency of product quality.

[0147] Example: Taking semiconductor manufacturing as an example, the simulation optimization results may provide the best lithography process parameters, including exposure time and development time. Based on these simulation optimization results, the system generates detailed quality control measures to monitor the temperature of the exposure machine and the liquid concentration of the developer tank in real time during the lithography process. Through the sensor network, the system can obtain these key parameters in real time and automatically adjust the exposure time and development time when any abnormality is detected to ensure the quality and performance of the semiconductor chip. Throughout the production process, the system continuously generates adjusted production task data to ensure that each production step runs under the optimal parameters, improving overall production efficiency and product quality.

[0148] By generating quality control instructions based on simulation optimization results and task function data, and performing real-time monitoring and dynamic adjustments, the system can significantly improve the stability of the production process and product quality. Specific quality control measures and real-time monitoring ensure that any abnormalities in the production process can be discovered and corrected in a timely manner, and automatic adjustment of production parameters ensures the consistency and reliability of product quality. Overall, this process not only enhances the flexibility and responsiveness of the production management system, but also provides solid technical support for achieving efficient production process management and quality control.

[0149] like Figure 6 As shown, a system for implementing the multi-level simulation optimization and quality control method for a production process comprises:

[0150] The management system module is used to obtain target production task data, including production targets, time requirements and quality standards. The management system module is used to obtain target production task data, including production targets, time requirements and quality standards. Through the management system module, the system can fully understand the specific requirements and constraints of the production tasks, and provide basic data for subsequent analysis and optimization.

[0151] The adaptive neural network module is used to analyze the target production task data and generate task function data. The adaptive neural network module is used to analyze the target production task data and generate task function data. Adaptive neural network is an advanced technology that can improve prediction and analysis accuracy through training, learning and self-adjustment. In the present invention, the adaptive neural network module can evaluate the complexity of the task, computing resources and time requirements, analyze resource requirements, and evaluate potential quality risks. These task function data provide necessary inputs for subsequent simulation optimization.

[0152] The simulation optimization module includes: a preliminary simulation submodule, which is used to generate a preliminary simulation model according to the task function data; a multi-level optimization submodule, which is used to optimize the geometric dimensions, material properties and production process of the preliminary simulation model; an optimization suggestion generation submodule, which is used to generate optimization suggestions according to the optimization results and feed back to the production control system; the simulation optimization module includes a preliminary simulation submodule, a multi-level optimization submodule and an optimization suggestion generation submodule. The preliminary simulation submodule is used to generate a preliminary simulation model according to the task function data, and simulate the basic behaviors and results in the production process through simulation. The multi-level optimization submodule optimizes the geometric dimensions, material properties and production process of the preliminary simulation model to ensure that all aspects of the production process are in the best state. The optimization suggestion generation submodule generates optimization suggestions according to the optimization results and feeds back to the production control system. For example, in the production process of mechanical parts, the system can ensure that the key dimensions of the parts meet the predetermined tolerance range through geometric dimension optimization, select the best material combination through material property optimization to improve the strength and durability of the parts, and improve production efficiency and reduce resource waste through production process optimization.

[0153] The blockchain data management module is used to store simulation optimization result data and task function data, and to verify and process the data through smart contracts; The blockchain data management module is used to store simulation optimization result data and task function data, and to verify and process the data through smart contracts. The decentralization and encryption characteristics of blockchain technology ensure the security and immutability of data. A smart contract is a self-executing code that can automatically perform predetermined operations when specific conditions are met. In the present invention, the blockchain data management module verifies and processes the stored data through smart contracts to ensure the integrity and authenticity of the data. For example, when storing the simulation optimization result data of mechanical parts, the smart contract can automatically verify the format and numerical range of the data to ensure the accuracy of the data.

[0154] The quality control module is used to generate quality control instructions based on the simulation optimization results and task function data, monitor the production line status in real time, and make dynamic adjustments to generate adjusted production task data; The quality control module is used to generate quality control instructions based on the simulation optimization results and task function data, monitor the production line status in real time, and make dynamic adjustments to generate adjusted production task data. Through the quality control module, the system can monitor key parameters (such as temperature, pressure, and speed) in the production process in real time and automatically adjust production parameters to maintain product quality. For example, during the injection molding process, the quality control module can monitor the mold temperature and injection pressure in real time. When it detects that the temperature is too high or the pressure is insufficient, the system will automatically adjust the corresponding production parameters to ensure product quality.

[0155] The augmented reality interface module is used to display simulation results and quality inspection information in real time, and managers can monitor and adjust the production line through the AR interface. The augmented reality interface module is used to display simulation results and quality inspection information in real time, and managers can monitor and adjust the production line through the AR interface. Augmented reality technology can superimpose virtual information on the real world, providing managers with an intuitive production line monitoring and adjustment interface. For example, managers can use AR glasses to view the operating status and key parameters of each device on the production line in real time, and make corresponding adjustments to improve production efficiency and product quality.

[0156] The intelligent fault warning system is used to analyze production equipment and environmental data using adaptive neural networks, predict potential faults and risks, issue early warnings and provide fault handling suggestions to ensure the continuity and stability of the production process; the intelligent fault warning system is used to analyze production equipment and environmental data using adaptive neural networks, predict potential faults and risks, issue early warnings and provide fault handling suggestions to ensure the continuity and stability of the production process. Through the intelligent fault warning system, the system can promptly identify and handle potential problems in the production process to avoid production interruptions caused by equipment failures. For example, in the chemical production process, the intelligent fault warning system can predict the risk of equipment failure by analyzing the vibration and temperature data of the equipment, and issue early warnings to notify maintenance personnel to conduct inspections and repairs.

[0157] The feedback and adjustment module is used to dynamically adjust production tasks based on quality inspection results and early warning information, optimize production processes and quality control, and generate adjusted production task data. The feedback and adjustment module is used to dynamically adjust production tasks based on quality inspection results and early warning information, optimize production processes and quality control, and generate adjusted production task data. Through the feedback and adjustment module, the system can adjust production plans and control strategies in real time according to actual production conditions and quality inspection results to ensure efficient operation of the production process and continuous improvement of product quality. For example, in the semiconductor manufacturing process, the system can adjust lithography and development process parameters based on real-time detected product defect data, optimize production processes, and improve product qualification rates.

[0158] The system of the present invention realizes multi-level simulation optimization and quality control of the production process through the collaborative work of management system module, adaptive neural network module, simulation optimization module, blockchain data management module, quality control module, augmented reality interface module, intelligent fault warning system and feedback and adjustment module. Through adaptive neural network technology, blockchain data storage and smart contracts, real-time monitoring and dynamic adjustment and other technical means, the present invention significantly improves production efficiency, product quality and the intelligent level of the production process.

[0159] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0160] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A multi-level simulation optimization and quality control method for a production process, characterized in that: The following steps are involved: Acquiring target production task data, wherein the target production task data includes production targets, time requirements and quality standards; Analyzing the target production task data through an adaptive neural network to generate task function data; Performing multi-level simulation optimization based on the task function data includes the following steps: inputting the task function data into a simulation optimization module to generate a preliminary simulation model; performing multi-level optimization on the preliminary simulation model, including geometric size optimization, material performance optimization, and production process optimization; generating optimization suggestions based on the optimization results, and feeding back to the production control system; The simulation optimization result data and task function data are stored in the blockchain, and the data is verified and processed through smart contracts; Based on the simulation optimization results and the task function data, quality control instructions are generated, the production line status is monitored in real time, and dynamic adjustments are made to generate adjusted production task data.

2. The method according to claim 1, characterized in that The obtaining of target production task data includes: obtaining detailed information of production targets, time requirements and quality standards from a management system, and converting these detailed information into target production task data, wherein the target production task data includes production target parameters, time parameters and quality standard parameters.

3. The method according to claim 1, characterized in that Analyzing the target production task data by an adaptive neural network includes: Assess the complexity of the task and calculate the resources and time required to complete the task; Analyze resource requirements and determine the materials, equipment and human resources required in the production process; Assess potential quality risks and identify factors that may affect product quality; Generate task function data, including task complexity parameters, resource requirement parameters and quality risk assessment results.

4. The method according to claim 1, characterized in that: The step of inputting the task function data into the simulation optimization module to generate a preliminary simulation model comprises: Build geometric models based on mission function data, including key dimensions and shape parameters; Generate a finite element model based on the geometric model and set material properties and boundary conditions; The finite element model is input into the simulation optimization module for preliminary simulation calculations.

5. The method according to claim 1, characterized in that The multi-level optimization of the preliminary simulation model includes: Geometry optimization, where the geometry is optimized according to the following calculation expression: Among them, O geo represents the objective function of geometric size optimization; d i Indicates the current size; represents the target size; n is the number of optimization dimensions; Material performance optimization, where material performance is optimized according to the following calculation expressions: Among them, O mat represents the material performance optimization objective function; k j Represents the weight of material performance parameters; p j Represents material performance parameters; m is the number of performance parameters; Production process optimization, where the production process parameters are optimized by the following calculation expressions: Among them, O proc represents the production process optimization objective function; t k Indicates the current production process time; represents the target production process time; l is the number of production process steps.

6. The method according to claim 5, characterized in that The geometric dimension optimization includes ensuring that all key dimensions meet a predetermined tolerance range and minimizing the dimension error using an iterative algorithm, wherein the iterative algorithm includes a genetic algorithm or a particle swarm optimization algorithm.

7. The method according to claim 5, characterized in that The material performance optimization includes: selecting the best material combination to improve product strength and durability, material performance parameters include tensile strength, hardness and wear resistance, and calibrating the material performance model through experimental data to ensure the accuracy of the optimization results, including establishing a material performance database and performing model verification.

8. The method according to claim 5, characterized in that The production process optimization includes: optimizing the sequence of production steps to improve production efficiency, including process scheduling and equipment scheduling, and verifying the optimization plan through simulation and experimental data to ensure its feasibility, including simulating the operation of the production line and verifying production parameters.

9. The method according to claim 1, characterized in that: The generating of quality control instructions comprises: Generate specific quality control measures based on simulation optimization results, including real-time monitoring parameters and adjustment measures; Real-time monitoring of key parameters in the production process, and automatic adjustment of production parameters to maintain product quality, including temperature, pressure and speed.

10. A system for implementing the multi-level simulation optimization and quality control method for a production process as claimed in any one of claims 1 to 9, characterized in that: include: A management system module is used to obtain target production task data, wherein the target production task data includes production targets, time requirements and quality standards; An adaptive neural network module, used for analyzing the target production task data and generating task function data; The simulation optimization module includes: a preliminary simulation submodule, which is used to generate a preliminary simulation model according to the task function data; a multi-level optimization submodule, which is used to optimize the geometric dimensions, material properties and production process of the preliminary simulation model; an optimization suggestion generation submodule, which is used to generate optimization suggestions according to the optimization results and feed them back to the production control system; Blockchain data management module, which is used to store simulation optimization result data and task function data, and to verify and process data through smart contracts; The quality control module is used to generate quality control instructions based on simulation optimization results and task function data, monitor the status of the production line in real time, make dynamic adjustments, and generate adjusted production task data; Augmented reality interface module, used to display simulation results and quality inspection information in real time, and managers can monitor and adjust the production line through the AR interface; Intelligent fault warning system, which uses adaptive neural networks to analyze production equipment and environmental data, predict potential faults and risks, provide early warnings and fault handling suggestions, and ensure the continuity and stability of the production process; The feedback and adjustment module is used to dynamically adjust production tasks based on quality inspection results and early warning information, optimize production processes and quality control, and generate adjusted production task data.

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