WO2021227325A1 - Digital twin-based production process simulation and optimization method - Google Patents

Digital twin-based production process simulation and optimization method Download PDF

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WO2021227325A1
WO2021227325A1 PCT/CN2020/115792 CN2020115792W WO2021227325A1 WO 2021227325 A1 WO2021227325 A1 WO 2021227325A1 CN 2020115792 W CN2020115792 W CN 2020115792W WO 2021227325 A1 WO2021227325 A1 WO 2021227325A1
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digital
assembly
data
equipment
workshop
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胡长明
贲可存
张柳
谢协国
吕龙泉
冯展鹰
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中国电子科技集团公司第十四研究所
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0633—Workflow analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management

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  • the invention belongs to the field of simulation optimization technology, and specifically relates to a digital twin technology.
  • the general assembly of existing large and complex products usually adopts fixed stations and manual vertical assembly based on rigid frames.
  • the assembly quality is unstable, the assembly efficiency is low, the labor intensity of workers is high, and the assembly operation management is difficult.
  • Pulse Assembly Lines were originally derived from Ford's mobile car production line, which is a transitional stage of the continuous mobile assembly line. The difference is that the pulsating assembly production line can set the buffer time, which does not require high production beats. When a problem occurs in a certain production link, the entire production line can not move or leave it to the next station to solve it. When the assembly work is completely completed When the time, the production line pulses once.
  • Digital twin is to create virtual models of physical entities in the information space through digital methods, and use data to simulate the behavior of physical entities in the real environment. Through the information fusion and data interaction between the virtual model and the physical entity, more optimization decisions are provided for the physical entity.
  • the present invention proposes a production process simulation optimization method based on digital twins.
  • the present invention adopts the following technical solutions.
  • Adjust the final assembly process flow according to the work step deviation further optimize the process flow, balance the station beat, and improve the station assembly capacity to improve the final assembly efficiency.
  • Product cluster analysis according to the structure composition and process characteristics of the product, design different types of product lines in different fields, establish tasks, process flow, people, devices, materials, logistics distribution, process guidance models or documents, and measurement devices.
  • Time series models such as the utilization rate and the occurrence of the problem, analyze the utilization rate of various resources over time, and use the cluster analysis method to determine the occurrence mode of bottleneck resources.
  • Work station layout optimization analyze the product assembly process, and integrate and optimize the operation content of each station.
  • the method proposes a real-time mapping operation process in the digital space, uses real-time data to drive the highly realistic operation of the digital twin model, realizes the synchronous operation of the digital space and the logical space, and realizes the products, equipment, personnel, systems and environment in the digital space and the physical space. Mapping and interaction.
  • DT pers is a human digital twin model, and DT env is an environmental twin model.
  • Collect real-time quality data associate the collected data with production orders, batches and other information, discover potential quality risks and problems in time, realize rework and repair, quality visualization, and traceability management throughout the quality process, using accumulated historical data for Statistical analysis and data-driven equipment status modeling, non-linear predictive models, parameter optimization techniques and alarm strategies for different types of equipment objects, accurately grasp the deterioration trend of equipment status, prevent equipment abnormalities, and ensure smooth production.
  • the input raw data is fused and the basic information is extracted from the lower layer, and the basic information is fused into the higher representation information and decision-making of the middle layer, and these decisions and information are further fused in the higher layer to form the final classification result.
  • Discover complex data structures learn useful features layer by layer from the original data, and adaptively optimize the combination of different fusion levels to meet the data requirements of intelligent decision-making in production management.
  • Construct a digital twin model of the product's full life cycle perform real-time simulation of the operating status of the workshop, calculate the progress deviation of each process step in the actual assembly, adjust the final assembly process flow according to the process step deviation, further optimize the process flow, balance the station beat, and enhance the process. Position assembly capabilities to enhance the efficiency of the final assembly.
  • the process flow simulation is performed in the digital twin model again, the progress deviation of each work step in the actual assembly is calculated, and a new round of process flow optimization is completed.
  • the process flow is often not in the best state, the station settings are unreasonable, and there is a large difference in the operating time of each station.
  • the assembly tools and equipment in the station are relatively traditional, the assembly efficiency is low, and the process data cannot be automated. Collection is not conducive to quality monitoring and process traceability, and the final assembly efficiency is improved by further optimizing the process flow, balancing the station beat, and improving the station assembly capacity.
  • the antenna array, the platform and the radar car assembly's machine installation and the electric equipment content did not carry out parallel operations, resulting in low operating efficiency and long operating cycles.
  • the telecommunications test was carried out on the test rack to increase the front and the test.
  • the time for assembling and separating the frame, the panel assembly station, the high-frequency box assembly, and the high-frequency box assembly station are unreasonably divided, and the time of each station is not balanced.
  • the content of the high-frequency box assembly station is split into the panel assembly station and the high-frequency box assembly station.
  • the entire life cycle from parts delivery to finished product arrival is driven by actual data.
  • Product evolution, dynamic storage of product process data, quality data, etc., and virtual product labeling, accompany the full life cycle of the product.
  • Real-time mapping of the action, spatial position, and operating status of various equipment such as robots, AGVs, and processing equipment in the production line, completing the processing of each station, real-time mapping of personnel identity, location and other information, completing the visual management of personnel, real-time mapping Information such as production plan progress, operation plan progress, and process progress, among which information such as inventory status, logistics status, processing station flow, and work-in-process data can be visually analyzed and managed by the digital twin space.
  • association modeling For historical data, it is necessary to carry out association modeling.
  • the focus is on the operation association modeling that comprehensively considers the task-driven man-machine material method and environmental testing and other comprehensive factors, and takes the time axis as the direction to form a comprehensive model.
  • the current status monitoring, operation and shutdown of the core equipment are displayed in the system in real time.
  • Maintenance, failure, etc. when the mouse is moved to the device, it will display the operating status of the device, operating parameters, standby time, torque press displacement, etc., which are associated with OEE, and the statistical analysis report can be seen in the software in real time .
  • the present invention establishes a three-dimensional visualization display platform for the assembly intelligent workshop, which has the functions of the overall workshop operation status, assembly process monitoring, key station operation status, production process simulation and optimization, etc., and realizes the effective management of the workshop operation process.
  • Figure 1 is the system design architecture
  • Figure 2 is the traditional process flow
  • Figure 3 is the optimized process flow
  • Figure 4 is the cluster analysis process.
  • Adjust the final assembly process flow according to the work step deviation further optimize the process flow, balance the station beat, and improve the station assembly capacity to improve the final assembly efficiency.
  • the construction is carried out from the operation and control layers.
  • the production management of the assembly workshop is unified and deployed.
  • the material cache library and the line-side warehouse are set up in the assembly workshop, and the steps are carried out according to the system planning and hierarchical implementation.
  • Build an intelligent workshop with high efficiency, transparency, high flexibility, punctual delivery, and outstanding human-machine collaboration.
  • Product cluster analysis as shown in Figure 4, according to the product structure and process characteristics, design different types of product lines in different fields, establish tasks, process flow, people, equipment, materials, logistics distribution, process guidance Models, files, measuring devices, etc., and the time series model of the occurrence of problems, analyze the utilization of various resources over time, and use cluster analysis to determine the occurrence mode of bottleneck resources.
  • Work station layout optimization analyze the product assembly process, and integrate and optimize the operation content of each station.
  • DT pers is a human digital twin model, and DT env is an environmental twin model.
  • Collect real-time quality data associate the collected data with production orders, batches and other information, discover potential quality risks and problems in time, realize rework and repair, quality visualization, and traceability management throughout the quality process, using accumulated historical data for Statistical analysis and data-driven equipment status modeling, non-linear predictive models, parameter optimization techniques and alarm strategies for different types of equipment objects, accurately grasp the deterioration trend of equipment status, prevent equipment abnormalities, and ensure smooth production.
  • the input raw data is fused and the basic information is extracted from the lower layer, and the basic information is fused into the higher representation information and decision-making of the middle layer, and these decisions and information are further fused in the higher layer to form the final classification result.
  • Discover complex data structures learn useful features layer by layer from the original data, and adaptively optimize the combination of different fusion levels to meet the data requirements of intelligent decision-making in production management.
  • Construct a digital twin model of the product's full life cycle perform real-time simulation of the operating status of the workshop, calculate the progress deviation of each process step in the actual assembly, adjust the final assembly process flow according to the process step deviation, further optimize the process flow, balance the station beat, and enhance the process. Position assembly capabilities to enhance the efficiency of the final assembly.
  • the process flow simulation is performed in the digital twin model again, the progress deviation of each work step in the actual assembly is calculated, and a new round of process flow optimization is completed.
  • the process flow is often not in the best state, the station settings are unreasonable, and there is a large difference in the operating time of each station.
  • the assembly tools and equipment in the station are relatively traditional, the assembly efficiency is low, and the process data cannot be automated. Collection is not conducive to quality monitoring and process traceability, and the final assembly efficiency is improved by further optimizing the process flow, balancing the station beat, and improving the station assembly capacity.
  • the optimized process flow is shown in Figure 3.
  • the content of the high-frequency box assembly station is split into the panel assembly station and the high-frequency box assembly station.
  • Real-time mapping of the action, spatial position, and operating status of various equipment such as robots, AGVs, and processing equipment in the production line, completing the processing of each station, real-time mapping of personnel identity, location and other information, completing the visual management of personnel, real-time mapping Information such as production plan progress, operation plan progress, and process progress, among which information such as inventory status, logistics status, processing station flow, and work-in-process data can be visually analyzed and managed by the digital twin space.
  • association modeling For historical data, it is necessary to carry out association modeling.
  • the focus is on the operation association modeling that comprehensively considers the task-driven man-machine material method and environmental testing and other comprehensive factors, and takes the time axis as the direction to form a comprehensive model.
  • the current status monitoring, operation and shutdown of the core equipment are displayed in the system in real time.
  • Maintenance, failure, etc. when the mouse is moved to the device, it will display the operating status of the device, operating parameters, standby time, torque press displacement, etc., which are associated with OEE, and the statistical analysis report can be seen in the software in real time .
  • the final assembly process has been refined and sorted out, and the total adjustment efficiency of the final assembly has been increased by 28.7%, and the cycle time has been shortened by 29 days from 101 days to 72 days through measures such as parallel operation of multiple work types, optimization of station integration and optimization of test processes.

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Abstract

On the basis of flexible production requirements on multiple varieties, small batches and variable paces, a manufacturing operation management system and a data acquisition and control system based on big data are constructed to improve the intelligent level of production logistics and the professional level of assembly process equipment and form an assembly intelligent workshop using a batch product pulsation production line and a developed product intelligent assembly unit as main bodies, thereby providing more optimization decisions for physical entities by means of information fusion and data interaction between virtual models and the physical entities.

Description

一种基于数字孪生的生产过程仿真优化方法A simulation and optimization method of production process based on digital twin 技术领域Technical field
本发明属于仿真优化技术领域，具体涉及一种数字孪生技术。The invention belongs to the field of simulation optimization technology, and specifically relates to a digital twin technology.
背景技术Background technique
现有大型复杂产品的总装通常采用固定式站位，基于刚性型架进行手工垂直装配，装配质量不稳定，装配效率低，工人劳动强度大，装配作业管理困难。The general assembly of existing large and complex products usually adopts fixed stations and manual vertical assembly based on rigid frames. The assembly quality is unstable, the assembly efficiency is low, the labor intensity of workers is high, and the assembly operation management is difficult.
脉动装配生产线(Pulse Assembly Lines)最初从Ford公司的移动式汽车生产线衍生而来，是连续移动装配生产线的过渡阶段。不同的是，脉动装配生产线可以设定缓冲时间，对生产节拍要求不高，当某个生产环节出现问题时，整个生产线可以不移动，或留给下个站位去解决，当装配工作全部完成时，生产线就脉动一次。Pulse Assembly Lines were originally derived from Ford's mobile car production line, which is a transitional stage of the continuous mobile assembly line. The difference is that the pulsating assembly production line can set the buffer time, which does not require high production beats. When a problem occurs in a certain production link, the entire production line can not move or leave it to the next station to solve it. When the assembly work is completely completed When the time, the production line pulses once.
现阶段生产过程数据采集率低，数据分析应用能力弱，过程控制及追溯能力不强，导致质量管控成效不高。车间管理中，资源和计划调度依赖人工经验，信息化手段不足，生产计划排程精细化程度不够，动态响应能力不强，导致车间作业管理效能不高。At this stage, the data collection rate of the production process is low, the data analysis and application ability is weak, and the process control and traceability ability are not strong, which leads to the low quality control effect. In workshop management, resources and planning and scheduling rely on manual experience, insufficient information technology, insufficient production planning and scheduling, and insufficient dynamic response capabilities, resulting in low efficiency of workshop operation management.
随着信息时代的发展，数字孪生技术已成为实现智能制造的方法之一。数字孪生就是通过数字化的方式，在信息空间创建物理实体的虚拟模型，利用数据模拟物理实体在现实环境中的行为。通过虚拟模型和物理实体之间的信息融合和数据交互，为物理实体提供更多的优化决策。With the development of the information age, digital twin technology has become one of the methods to realize intelligent manufacturing. Digital twin is to create virtual models of physical entities in the information space through digital methods, and use data to simulate the behavior of physical entities in the real environment. Through the information fusion and data interaction between the virtual model and the physical entity, more optimization decisions are provided for the physical entity.
发明内容Summary of the invention
本发明为了解决现有技术存在的问题，提出了一种基于数字孪生的生产过程仿真优化方法，为了实现上述目的，本发明采用了以下技术方案。In order to solve the problems existing in the prior art, the present invention proposes a production process simulation optimization method based on digital twins. In order to achieve the above objective, the present invention adopts the following technical solutions.
运行状态、关键工位运行状态、装配过程监控、生产过程仿真及优化等功能，实现车间运营过程的有效管理，全面提升智能车间的管控能力，通过对车间运行状态进行实时仿真，计算实际装配中各个工步的进度偏差，根据工步偏差调整总装工艺流程，进一步优化工艺流程、均衡工位节拍、工位装配能力提升等方面提升总装效能。Functions such as operating status, operating status of key stations, assembly process monitoring, production process simulation and optimization, etc., realize effective management of the workshop operation process, and comprehensively improve the management and control ability of the intelligent workshop. Through real-time simulation of the workshop operation status, the actual assembly is calculated For the progress deviation of each work step, adjust the final assembly process flow according to the process step deviation, further optimize the process flow, balance the station beat, and improve the assembly capacity of the station to improve the final assembly efficiency.
根据工步偏差调整总装工艺流程，进一步优化工艺流程、均衡工位节拍、工位装配能力提升等方面提升总装效能。Adjust the final assembly process flow according to the work step deviation, further optimize the process flow, balance the station beat, and improve the station assembly capacity to improve the final assembly efficiency.
分析产品装配过程，对每个工位的操作内容进行整合及优化。Analyze the product assembly process, integrate and optimize the operation content of each station.
依据产品的结构组成及工艺特点，分别设计不同领域不同类型的产品产线。According to the structural composition and process characteristics of the products, different types of product production lines in different fields are designed respectively.
完成智能工厂数字孪生模型建造，工厂内实现设备数据采集分析。Complete the construction of the digital twin model of the smart factory, and realize the equipment data collection and analysis in the factory.
建立任务、工艺流程、人、装置、物料齐套、物流配送、工艺指导模型或文件、测量装置等利用率以及问题发生的时间序列模型，分析各种资源随时间变化的利用率，利用聚类分析方法，判断瓶颈资源的发生模式。Establish the utilization rate of tasks, process flow, people, equipment, materials, logistics distribution, process guidance models or documents, measurement devices, etc., and time series models of problems, analyze the utilization rate of various resources over time, and use clustering Analysis method to determine the occurrence mode of bottleneck resources.
实现关键资源设备的互联互通，实现风险件关键参数可控，实现装配过程中简单质量问题的快速决策处理，同时也为智能车间集成应用建设提供基础硬件条件。Realize the interconnection and intercommunication of key resource equipment, realize the controllable key parameters of risk parts, realize the rapid decision-making and processing of simple quality problems in the assembly process, and provide basic hardware conditions for the integrated application construction of intelligent workshops.
实现数字空间和逻辑空间的同步运行，研究实体空间数据和信号的逻辑处理方法，提出数字空间实时映射运行流程，利用实时数据驱动数字孪生模型的高度拟真化运行，实现产品、设备、人员、***和环境在数字空间和实体空间的映射与交互。Realize the synchronous operation of digital space and logical space, study the logical processing methods of physical space data and signals, propose the real-time mapping operation process of digital space, and use real-time data to drive the highly realistic operation of the digital twin model to realize products, equipment, personnel, The mapping and interaction of system and environment in digital space and physical space.
实现返工返修、质量可视化，以及质量全过程追溯管理。利用积累的历史数据进行统计分析和数据驱动的设备状态建模，开发针对不同类别设备对象的非线性预测模型、参数优化技术和报警策略等，准确把握设备状态的劣化趋势，防止设备出现异常，确保生产顺利进行。Realize rework and repair, quality visualization, and traceability management throughout the quality process. Use accumulated historical data for statistical analysis and data-driven equipment state modeling, develop nonlinear prediction models, parameter optimization techniques and alarm strategies for different types of equipment objects, accurately grasp the deterioration trend of equipment status, and prevent equipment abnormalities. Ensure smooth production.
从运营层、控制层开展建设，统一实施部署总装车间生产管理，按照分级存放的原则，在总装车间设置物料缓存库及线边库，按***规划、分级实施的步骤开展建设，建成高效透明、高度柔性、准时配送、人机协同特征显著的智能车间。Carry out construction from the operation level and control level, implement unified deployment of the production management of the final assembly workshop, set up the material buffer warehouse and the line side warehouse in the final assembly workshop according to the principle of grading storage, and carry out the construction according to the steps of system planning and grading implementation. Intelligent workshop with high flexibility, punctual delivery, and human-machine collaboration.
增加产线柔性，提高产线效率，对各型号产品进行详细工艺流程梳理、分析归类、优化调整。Increase the flexibility of the production line, improve the efficiency of the production line, and carry out detailed process analysis, analysis and classification, and optimization and adjustment of various types of products.
产品聚类分析，依据产品的结构组成及工艺特点，分别设计不同领域不同类型的产品产线，建立任务、工艺流程、人、装置、物料齐套、物流配送、工艺指导模型或文件、测量装置等利用率以及问题发生的时间序列模型，分析各种资源随时间变化的利用率，利用聚类分析方法，判断瓶颈资源的发生模式。Product cluster analysis, according to the structure composition and process characteristics of the product, design different types of product lines in different fields, establish tasks, process flow, people, devices, materials, logistics distribution, process guidance models or documents, and measurement devices. Time series models such as the utilization rate and the occurrence of the problem, analyze the utilization rate of various resources over time, and use the cluster analysis method to determine the occurrence mode of bottleneck resources.
工艺流程再造，突破传统设计思路，全寿命周期分析产品生产过程，再造产品工艺流程，以适应同类产品生产。Reengineering the technological process, breaking through the traditional design ideas, analyzing the production process of the product in the whole life cycle, and reengineering the technological process of the product to adapt to the production of similar products.
工位布局优化，分析产品装配过程，对每个工位的操作内容进行整合及优化。Work station layout optimization, analyze the product assembly process, and integrate and optimize the operation content of each station.
基于工艺流程分析结果，开展装配设备的智能化升级研究，通过各种装备的数字化、网络化、智能化升级，实现关键资源设备的互联互通，实现风险件关键参数可控，实现装配过程中简单质量问题的快速决策处理，同时也为智能车间集成应用建设提供基础硬件条件。Based on the analysis results of the process flow, research on the intelligent upgrade of assembly equipment is carried out. Through the digital, networked, and intelligent upgrade of various equipment, the interconnection of key resource equipment is realized, the key parameters of risk parts are controllable, and the assembly process is simple The rapid decision-making and processing of quality issues also provide basic hardware conditions for the construction of integrated applications in intelligent workshops.
面对不同类型的产品、零部件、物料、、设备、人员和环境等物理实体类型和多样化功能，以及实体产生的数据，构建数字空间中的孪生模型，研究实体空间数据和信号的逻辑处理方法，提出数字空间实时映射运行流程，利用实时数据驱动数字孪生模型的高度拟真化运行，实现数字空间和逻辑空间的同步运行，实现产品、设备、人员、***和环境在数字空间和实体空间的映射与交互。Facing different types of products, parts, materials, equipment, personnel, environment and other physical entity types and diversified functions, as well as the data generated by the entities, construct a twin model in the digital space, and study the logical processing of physical space data and signals The method proposes a real-time mapping operation process in the digital space, uses real-time data to drive the highly realistic operation of the digital twin model, realizes the synchronous operation of the digital space and the logical space, and realizes the products, equipment, personnel, systems and environment in the digital space and the physical space. Mapping and interaction.
生产过程数字孪生模型采用统一表达式DT ws＝DT equip∪DT prod∪DT pers∪DT env，其中DT ws为车间生产过程数字模型，DT equip为设备数字孪生模型，DT prod为产品数字孪生模型，DT pers为人员数字孪生模型，DT env为环境孪生模型。 The digital twin model of the production process adopts the unified expression DT ws = DT equip ∪DT prod ∪DT pers ∪DT env , where DT ws is the digital model of the workshop production process, DT equip is the digital twin model of the equipment, and DT prod is the digital twin model of the product. DT pers is a human digital twin model, and DT env is an environmental twin model.
基于数字孪生信息与统计学习、深度学习技术，完成对总装车间运行状态的专业诊断，实现对异常的通知、判定、处理、跟踪、分析追偿和关闭，形成车间异常管理的闭环，提高异常处理的及时性。Based on digital twin information, statistical learning, and deep learning technology, complete professional diagnosis of the operating status of the assembly shop, realize the notification, determination, processing, tracking, analysis, recovery and closure of abnormalities, forming a closed loop of abnormal management of the workshop, and improving the abnormal handling Timeliness.
对质量数据实时采集，将采集的数据与生产订单、批次等信息进行关联，及时发现潜在的质量风险和问题，实现返工返修、质量可视化，以及质量全过程追溯管理，利用积累的历史数据进行统计分析和数据驱动的设备状态建模，针对不同类别设备对象的非线性预测模型、参数优化技术和报警策略，准确把握设备状态的劣化趋势，防止设备出现异常，确保生产顺利进行。Collect real-time quality data, associate the collected data with production orders, batches and other information, discover potential quality risks and problems in time, realize rework and repair, quality visualization, and traceability management throughout the quality process, using accumulated historical data for Statistical analysis and data-driven equipment status modeling, non-linear predictive models, parameter optimization techniques and alarm strategies for different types of equipment objects, accurately grasp the deterioration trend of equipment status, prevent equipment abnormalities, and ensure smooth production.
输入原始数据融合并从其较低层提取基本信息，将基本信息融合到其中间层的更高表示信息和决策中，进一步将这些决策和信息融合在其较高层中，形成最终分类结果，自动发现复杂的数据结构，并从原始数据逐层学习有用的特征，自适应优化不同融合级别的组合，以满足生产管理中智能决策对数据的要求。The input raw data is fused and the basic information is extracted from the lower layer, and the basic information is fused into the higher representation information and decision-making of the middle layer, and these decisions and information are further fused in the higher layer to form the final classification result. Discover complex data structures, learn useful features layer by layer from the original data, and adaptively optimize the combination of different fusion levels to meet the data requirements of intelligent decision-making in production management.
构建产品全生命周期的数字孪生模型，对车间运行状态进行实时仿真，计算实际装配中各个工步的进度偏差，根据工步偏差调整总装工艺流程，进一步优化工艺流程、均衡工位节拍、增强工位装配能力，提升总装效能。Construct a digital twin model of the product's full life cycle, perform real-time simulation of the operating status of the workshop, calculate the progress deviation of each process step in the actual assembly, adjust the final assembly process flow according to the process step deviation, further optimize the process flow, balance the station beat, and enhance the process. Position assembly capabilities to enhance the efficiency of the final assembly.
再次在数字孪生模型中进行工艺流程模拟，计算实际装配中各个工步的进度偏差，完成新一轮的工艺流程优化。The process flow simulation is performed in the digital twin model again, the progress deviation of each work step in the actual assembly is calculated, and a new round of process flow optimization is completed.
在实际装配作业中，往往工艺流程不是最佳状态，工位设置不合理，存在各工位作业时间相差较大的问题，工位中装配工具及设备较为传统，装配效率低下，过程数据无法自动采集，不利于质量监控和过程追溯，通过进一步优化工艺流程、均衡工位节拍、工位装配能力提升等方面提升总装效能。In actual assembly operations, the process flow is often not in the best state, the station settings are unreasonable, and there is a large difference in the operating time of each station. The assembly tools and equipment in the station are relatively traditional, the assembly efficiency is low, and the process data cannot be automated. Collection is not conducive to quality monitoring and process traceability, and the final assembly efficiency is improved by further optimizing the process flow, balancing the station beat, and improving the station assembly capacity.
传统工艺流程，天线阵面、平台和雷达车总成的机装与电装内容未开展并行作业，导致作业效率低下，作业周期长的问题，电讯测试在测试架上进行，增加阵面与测试架的拼装和分离的时间，面板装配工位、高频箱装配、高频箱拼装工位划分不合理，各工位时间不均衡。In the traditional process, the antenna array, the platform and the radar car assembly's machine installation and the electric equipment content did not carry out parallel operations, resulting in low operating efficiency and long operating cycles. The telecommunications test was carried out on the test rack to increase the front and the test. The time for assembling and separating the frame, the panel assembly station, the high-frequency box assembly, and the high-frequency box assembly station are unreasonably divided, and the time of each station is not balanced.
优化后的工艺流程，高频箱装配工位的内容拆分至面板装配工位与高频箱拼装工位，从零部件出库到成品入库的整个生命周期都由实际产生数据驱动，完成产品的演化，产品的工艺数据、质量数据等动态存储与虚拟产品的标签中，伴随产品的全生命周期。After the optimized process, the content of the high-frequency box assembly station is split into the panel assembly station and the high-frequency box assembly station. The entire life cycle from parts delivery to finished product arrival is driven by actual data. Product evolution, dynamic storage of product process data, quality data, etc., and virtual product labeling, accompany the full life cycle of the product.
对生产线中机器人、AGV、加工设备等各种设备的动作、空间位置、运行状态进行实时映射，完成每个工位的加工，实时映射人员身份、位置等信息，完成人员的可视化管理，实时映射生产计划进度、作业计划进度、工序进度等信息，其中库存状态、物流情况、加工工位的流程、在制品数据量等信息均可由数字孪生空间进行可视化分析与管理。Real-time mapping of the action, spatial position, and operating status of various equipment such as robots, AGVs, and processing equipment in the production line, completing the processing of each station, real-time mapping of personnel identity, location and other information, completing the visual management of personnel, real-time mapping Information such as production plan progress, operation plan progress, and process progress, among which information such as inventory status, logistics status, processing station flow, and work-in-process data can be visually analyzed and managed by the digital twin space.
对于历史数据必须进行关联建模，重点是综合考虑任务驱动下的人机料法环测等综合因素的运行关联建模，并且以时间轴为导向形成综合模型。For historical data, it is necessary to carry out association modeling. The focus is on the operation association modeling that comprehensively considers the task-driven man-machine material method and environmental testing and other comprehensive factors, and takes the time axis as the direction to form a comprehensive model.
建立任务、工艺流程、人、装置、物料齐套、物流配送、工艺指导模型或文件、测量装置等利用率以及问题发生的时间序列模型，分析各种资源随时间变化的利用率，利用聚类分析方法，判断瓶颈资源的发生模式，为后续的实际执行提供指导。Establish the utilization rate of tasks, process flow, people, equipment, materials, logistics distribution, process guidance models or documents, measurement devices, etc., and time series models of problems, analyze the utilization rate of various resources over time, and use clustering Analyze methods, determine the occurrence mode of bottleneck resources, and provide guidance for subsequent actual implementation.
在***中建立核心装备数据库，实时展示核心装备的使用情况、核心装备的使用率、不合格产品计数、统计该设备的产品合格率，在***中实时展示核心装备当前的状态监控，运行、停机、维护、故障等，在鼠标移动到该设备时停留，会显示该设备的运行状况，运行参数，待机时间，力矩压装位移等，与OEE相关联，可以在软件中实时看到统计分析报告。Establish a core equipment database in the system to display the use of core equipment, the utilization rate of core equipment, the count of unqualified products, and the product qualification rate of the equipment in real time. The current status monitoring, operation and shutdown of the core equipment are displayed in the system in real time. , Maintenance, failure, etc., when the mouse is moved to the device, it will display the operating status of the device, operating parameters, standby time, torque press displacement, etc., which are associated with OEE, and the statistical analysis report can be seen in the software in real time .
本发明基于数字孪生模型，为总装智能车间建立了三维可视化展示平台，具备车间整体运行状态、装配过程监控、关键工位运行状态、生产过程仿真及优化等功能，实现车间运营过程的有效管理，全面提升智能车间的管控能力；通过对车间运行状态进行实时仿真，计算实际装配中各个工步的进度偏差，根据工步偏差调整总装工艺流程，进一步优化工艺流程、均衡工位节拍、增强工位装配能力，从而提升总装效能；基于多品种、小批量、变节拍的柔性生产需求，建设基于大数据的制造运营管理***、数据采集及控制***，提升生产物流智能化水平和总装工艺装备专业化水准，形成批产产品脉动生产线和研制产品智能总装单元为主体的总装智能化车间。Based on the digital twin model, the present invention establishes a three-dimensional visualization display platform for the assembly intelligent workshop, which has the functions of the overall workshop operation status, assembly process monitoring, key station operation status, production process simulation and optimization, etc., and realizes the effective management of the workshop operation process. Comprehensively improve the management and control ability of the intelligent workshop; through real-time simulation of the operation status of the workshop, the progress deviation of each work step in the actual assembly is calculated, and the final assembly process flow is adjusted according to the work step deviation, and the process flow is further optimized, the work station beats are balanced, and the work station is enhanced Assembling capabilities, thereby improving the efficiency of final assembly; based on the flexible production requirements of multiple varieties, small batches, and variable tempo, build a big data-based manufacturing operation management system, data collection and control system, and improve the level of intelligent production logistics and specialization of final assembly process equipment Level, forming a pulsating production line for batch products and an intelligent assembly workshop with the development of intelligent assembly units as the main body.
附图说明Description of the drawings
图1是***设计架构，图2是传统工艺流程，图3是优化后的工艺流程，图4是聚类分析过程。Figure 1 is the system design architecture, Figure 2 is the traditional process flow, Figure 3 is the optimized process flow, and Figure 4 is the cluster analysis process.
具体实施方式Detailed ways
以下结合附图对本发明的技术方案做具体的说明。The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.
运行状态、关键工位运行状态、装配过程监控、生产过程仿真及优化等功能，实现车间运营过程的有效管理，全面提升智能车间的管控能力，通过对车间运行状态进行实时仿真，计算实际装配中各个工步的进度偏差，根据工步偏差调整总装工艺流程，进一步优化工艺流程、均衡工位节拍、工位装配能力提升等方面提升总装效能。Functions such as operating status, operating status of key stations, assembly process monitoring, production process simulation and optimization, etc., realize effective management of the workshop operation process, and comprehensively improve the management and control ability of the intelligent workshop. Through real-time simulation of the workshop operation status, the actual assembly is calculated For the progress deviation of each work step, adjust the final assembly process flow according to the process step deviation, further optimize the process flow, balance the station beat, and improve the assembly capacity of the station to improve the final assembly efficiency.
根据工步偏差调整总装工艺流程，进一步优化工艺流程、均衡工位节拍、工位装配能力提升等方面提升总装效能。Adjust the final assembly process flow according to the work step deviation, further optimize the process flow, balance the station beat, and improve the station assembly capacity to improve the final assembly efficiency.
分析产品装配过程，对每个工位的操作内容进行整合及优化。Analyze the product assembly process, integrate and optimize the operation content of each station.
依据产品的结构组成及工艺特点，分别设计不同领域不同类型的产品产线。According to the structural composition and process characteristics of the products, different types of product production lines in different fields are designed respectively.
完成智能工厂数字孪生模型建造，工厂内实现设备数据采集分析。Complete the construction of the digital twin model of the smart factory, and realize the equipment data collection and analysis in the factory.
建立任务、工艺流程、人、装置、物料齐套、物流配送、工艺指导模型或文件、测量装置等利用率以及问题发生的时间序列模型，分析各种资源随时间变化的利用率，利用聚类分析方法，判断瓶颈资源的发生模式。Establish the utilization rate of tasks, process flow, people, equipment, materials, logistics distribution, process guidance models or documents, measurement devices, etc., and time series models of problems, analyze the utilization rate of various resources over time, and use clustering Analysis method to determine the occurrence mode of bottleneck resources.
实现关键资源设备的互联互通，实现风险件关键参数可控，实现装配过程中简单质量问题的快速决策处理，同时也为智能车间集成应用建设提供基础硬件条件。Realize the interconnection and intercommunication of key resource equipment, realize the controllable key parameters of risk parts, realize the rapid decision-making and processing of simple quality problems in the assembly process, and provide basic hardware conditions for the integrated application construction of intelligent workshops.
实现数字空间和逻辑空间的同步运行，研究实体空间数据和信号的逻辑处理方法，提出数字空间实时映射运行流程，利用实时数据驱动数字孪生模型的高度拟真化运行，实现产品、 设备、人员、***和环境在数字空间和实体空间的映射与交互。Realize the synchronous operation of digital space and logical space, study the logical processing methods of physical space data and signals, propose the real-time mapping operation process of digital space, and use real-time data to drive the highly realistic operation of the digital twin model to realize products, equipment, personnel, The mapping and interaction of system and environment in digital space and physical space.
实现返工返修、质量可视化，以及质量全过程追溯管理。利用积累的历史数据进行统计分析和数据驱动的设备状态建模，开发针对不同类别设备对象的非线性预测模型、参数优化技术和报警策略等，准确把握设备状态的劣化趋势，防止设备出现异常，确保生产顺利进行。Realize rework and repair, quality visualization, and traceability management throughout the quality process. Use accumulated historical data for statistical analysis and data-driven equipment state modeling, develop nonlinear prediction models, parameter optimization techniques and alarm strategies for different types of equipment objects, accurately grasp the deterioration trend of equipment status, and prevent equipment abnormalities. Ensure smooth production.
从运营层、控制层开展建设，如图1所示，统一实施部署总装车间生产管理，按照分级存放的原则，在总装车间设置物料缓存库及线边库，按***规划、分级实施的步骤开展建设，建成高效透明、高度柔性、准时配送、人机协同特征显著的智能车间。The construction is carried out from the operation and control layers. As shown in Figure 1, the production management of the assembly workshop is unified and deployed. According to the principle of hierarchical storage, the material cache library and the line-side warehouse are set up in the assembly workshop, and the steps are carried out according to the system planning and hierarchical implementation. Build an intelligent workshop with high efficiency, transparency, high flexibility, punctual delivery, and outstanding human-machine collaboration.
增加产线柔性，提高产线效率，对各型号产品进行详细工艺流程梳理、分析归类、优化调整。Increase the flexibility of the production line, improve the efficiency of the production line, and carry out detailed process analysis, analysis and classification, and optimization and adjustment of various types of products.
产品聚类分析，如图4所示，依据产品的结构组成及工艺特点，分别设计不同领域不同类型的产品产线，建立任务、工艺流程、人、装置、物料齐套、物流配送、工艺指导模型或文件、测量装置等利用率以及问题发生的时间序列模型，分析各种资源随时间变化的利用率，利用聚类分析方法，判断瓶颈资源的发生模式。Product cluster analysis, as shown in Figure 4, according to the product structure and process characteristics, design different types of product lines in different fields, establish tasks, process flow, people, equipment, materials, logistics distribution, process guidance Models, files, measuring devices, etc., and the time series model of the occurrence of problems, analyze the utilization of various resources over time, and use cluster analysis to determine the occurrence mode of bottleneck resources.
工艺流程再造，突破传统设计思路，全寿命周期分析产品生产过程，再造产品工艺流程，以适应同类产品生产。Reengineering the technological process, breaking through the traditional design ideas, analyzing the production process of the product in the whole life cycle, and reengineering the technological process of the product to adapt to the production of similar products.
工位布局优化，分析产品装配过程，对每个工位的操作内容进行整合及优化。Work station layout optimization, analyze the product assembly process, and integrate and optimize the operation content of each station.
基于工艺流程分析结果，开展装配设备的智能化升级研究，通过各种装备的数字化、网络化、智能化升级，实现关键资源设备的互联互通，实现风险件关键参数可控，实现装配过程中简单质量问题的快速决策处理，同时也为智能车间集成应用建设提供基础硬件条件。Based on the analysis results of the process flow, research on the intelligent upgrade of assembly equipment is carried out. Through the digital, networked, and intelligent upgrade of various equipment, the interconnection of key resource equipment is realized, the key parameters of risk parts are controllable, and the assembly process is simple The rapid decision-making and processing of quality issues also provide basic hardware conditions for the construction of integrated applications in intelligent workshops.
面对不同类型的产品、零部件、物料、设备、人员和环境等物理实体类型和多样化功能，以及实体产生的数据，构建数字空间中的孪生模型，研究实体空间数据和信号的逻辑处理方法，提出数字空间实时映射运行流程，利用实时数据驱动数字孪生模型的高度拟真化运行，实现数字空间和逻辑空间的同步运行，实现产品、设备、人员、***和环境在数字空间和实体空间的映射与交互。Facing different types of products, parts, materials, equipment, personnel, environment and other physical entity types and diversified functions, as well as the data generated by entities, construct a twin model in the digital space, and study the logical processing methods of physical space data and signals , Proposes the real-time mapping operation process of the digital space, uses real-time data to drive the highly realistic operation of the digital twin model, realizes the synchronous operation of the digital space and the logical space, and realizes the integration of products, equipment, personnel, systems and environments in the digital space and the physical space. Mapping and interaction.
生产过程数字孪生模型采用统一表达式DT ws＝DT equip∪DT prod∪DT pers∪DT env，其中DT ws为车间生产过程数字模型，DT equip为设备数字孪生模型，DT prod为产品数字孪生模型，DT pers为人员数字孪生模型，DT env为环境孪生模型。 The digital twin model of the production process adopts the unified expression DT ws = DT equip ∪DT prod ∪DT pers ∪DT env , where DT ws is the digital model of the workshop production process, DT equip is the digital twin model of the equipment, and DT prod is the digital twin model of the product. DT pers is a human digital twin model, and DT env is an environmental twin model.
基于数字孪生信息与统计学习、深度学习技术，完成对总装车间运行状态的专业诊断，实现对异常的通知、判定、处理、跟踪、分析追偿和关闭，形成车间异常管理的闭环，提高异常处理的及时性。Based on digital twin information, statistical learning, and deep learning technology, complete professional diagnosis of the operating status of the assembly shop, realize the notification, determination, processing, tracking, analysis, recovery and closure of abnormalities, forming a closed loop of abnormal management of the workshop, and improving the abnormal handling Timeliness.
对质量数据实时采集，将采集的数据与生产订单、批次等信息进行关联，及时发现潜在的质量风险和问题，实现返工返修、质量可视化，以及质量全过程追溯管理，利用积累的历史数据进行统计分析和数据驱动的设备状态建模，针对不同类别设备对象的非线性预测模型、参数优化技术和报警策略，准确把握设备状态的劣化趋势，防止设备出现异常，确保生产顺利进行。Collect real-time quality data, associate the collected data with production orders, batches and other information, discover potential quality risks and problems in time, realize rework and repair, quality visualization, and traceability management throughout the quality process, using accumulated historical data for Statistical analysis and data-driven equipment status modeling, non-linear predictive models, parameter optimization techniques and alarm strategies for different types of equipment objects, accurately grasp the deterioration trend of equipment status, prevent equipment abnormalities, and ensure smooth production.
输入原始数据融合并从其较低层提取基本信息，将基本信息融合到其中间层的更高表示信息和决策中，进一步将这些决策和信息融合在其较高层中，形成最终分类结果，自动发现复杂的数据结构，并从原始数据逐层学习有用的特征，自适应优化不同融合级别的组合，以满足生产管理中智能决策对数据的要求。The input raw data is fused and the basic information is extracted from the lower layer, and the basic information is fused into the higher representation information and decision-making of the middle layer, and these decisions and information are further fused in the higher layer to form the final classification result. Discover complex data structures, learn useful features layer by layer from the original data, and adaptively optimize the combination of different fusion levels to meet the data requirements of intelligent decision-making in production management.
构建产品全生命周期的数字孪生模型，对车间运行状态进行实时仿真，计算实际装配中各个工步的进度偏差，根据工步偏差调整总装工艺流程，进一步优化工艺流程、均衡工位节拍、增强工位装配能力，提升总装效能。Construct a digital twin model of the product's full life cycle, perform real-time simulation of the operating status of the workshop, calculate the progress deviation of each process step in the actual assembly, adjust the final assembly process flow according to the process step deviation, further optimize the process flow, balance the station beat, and enhance the process. Position assembly capabilities to enhance the efficiency of the final assembly.
再次在数字孪生模型中进行工艺流程模拟，计算实际装配中各个工步的进度偏差，完成新一轮的工艺流程优化。The process flow simulation is performed in the digital twin model again, the progress deviation of each work step in the actual assembly is calculated, and a new round of process flow optimization is completed.
在实际装配作业中，往往工艺流程不是最佳状态，工位设置不合理，存在各工位作业时间相差较大的问题，工位中装配工具及设备较为传统，装配效率低下，过程数据无法自动采集，不利于质量监控和过程追溯，通过进一步优化工艺流程、均衡工位节拍、工位装配能力提升等方面提升总装效能。In actual assembly operations, the process flow is often not in the best state, the station settings are unreasonable, and there is a large difference in the operating time of each station. The assembly tools and equipment in the station are relatively traditional, the assembly efficiency is low, and the process data cannot be automated. Collection is not conducive to quality monitoring and process traceability, and the final assembly efficiency is improved by further optimizing the process flow, balancing the station beat, and improving the station assembly capacity.
传统工艺流程如图2所示，天线阵面、平台和雷达车总成的机装与电装内容未开展并行作业，导致作业效率低下，作业周期长的问题，电讯测试在测试架上进行，增加阵面与测试架的拼装和分离的时间，面板装配工位、高频箱装配、高频箱拼装工位划分不合理，各工位时间不均衡。The traditional process flow is shown in Figure 2. The antenna array, platform, and radar car assembly are not operated in parallel with the electrical components, resulting in low operating efficiency and long operating cycles. Telecommunications testing is carried out on the test rack. Increase the time for the assembly and separation of the front and the test frame. The division of the panel assembly stations, high-frequency box assembly, and high-frequency box assembly stations is unreasonable, and the time of each station is not balanced.
优化后的工艺流程如图3所示，高频箱装配工位的内容拆分至面板装配工位与高频箱拼装工位，从零部件出库到成品入库的整个生命周期都由实际产生数据驱动，完成产品的演化，产品的工艺数据、质量数据等动态存储与虚拟产品的标签中，伴随产品的全生命周期。The optimized process flow is shown in Figure 3. The content of the high-frequency box assembly station is split into the panel assembly station and the high-frequency box assembly station. Generate data-driven, complete product evolution, dynamic storage of product process data, quality data, etc., and virtual product labels, which accompany the full life cycle of the product.
对生产线中机器人、AGV、加工设备等各种设备的动作、空间位置、运行状态进行实时映射，完成每个工位的加工，实时映射人员身份、位置等信息，完成人员的可视化管理，实时映射生产计划进度、作业计划进度、工序进度等信息，其中库存状态、物流情况、加工工位的流程、在制品数据量等信息均可由数字孪生空间进行可视化分析与管理。Real-time mapping of the action, spatial position, and operating status of various equipment such as robots, AGVs, and processing equipment in the production line, completing the processing of each station, real-time mapping of personnel identity, location and other information, completing the visual management of personnel, real-time mapping Information such as production plan progress, operation plan progress, and process progress, among which information such as inventory status, logistics status, processing station flow, and work-in-process data can be visually analyzed and managed by the digital twin space.
对于历史数据必须进行关联建模，重点是综合考虑任务驱动下的人机料法环测等综合因素的运行关联建模，并且以时间轴为导向形成综合模型。For historical data, it is necessary to carry out association modeling. The focus is on the operation association modeling that comprehensively considers the task-driven man-machine material method and environmental testing and other comprehensive factors, and takes the time axis as the direction to form a comprehensive model.
建立任务、工艺流程、人、装置、物料齐套、物流配送、工艺指导模型或文件、测量装置等利用率以及问题发生的时间序列模型，分析各种资源随时间变化的利用率，利用聚类分析方法，判断瓶颈资源的发生模式，为后续的实际执行提供指导。Establish the utilization rate of tasks, process flow, people, equipment, materials, logistics distribution, process guidance models or documents, measurement devices, etc., and time series models of problems, analyze the utilization rate of various resources over time, and use clustering Analyze methods, determine the occurrence mode of bottleneck resources, and provide guidance for subsequent actual implementation.
在***中建立核心装备数据库，实时展示核心装备的使用情况、核心装备的使用率、不合格产品计数、统计该设备的产品合格率，在***中实时展示核心装备当前的状态监控，运行、停机、维护、故障等，在鼠标移动到该设备时停留，会显示该设备的运行状况，运行参数，待机时间，力矩压装位移等，与OEE相关联，可以在软件中实时看到统计分析报告。Establish a core equipment database in the system to display the use of core equipment, the utilization rate of core equipment, the count of unqualified products, and the product qualification rate of the equipment in real time. The current status monitoring, operation and shutdown of the core equipment are displayed in the system in real time. , Maintenance, failure, etc., when the mouse is moved to the device, it will display the operating status of the device, operating parameters, standby time, torque press displacement, etc., which are associated with OEE, and the statistical analysis report can be seen in the software in real time .
对总装工艺流程细化梳理，通过多工种并行作业、工位整合优化和测试过程优化等措施，将总装总调效率提升28.7％，周期缩短29天，由101天减至72天。The final assembly process has been refined and sorted out, and the total adjustment efficiency of the final assembly has been increased by 28.7%, and the cycle time has been shortened by 29 days from 101 days to 72 days through measures such as parallel operation of multiple work types, optimization of station integration and optimization of test processes.
上述作为本发明的实施例，并不限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均包含在本发明的保护范围之内。The foregoing as the embodiments of the present invention do not limit the present invention. Any modification, equivalent replacement and improvement made within the spirit and principle of the present invention are all included in the protection scope of the present invention.

Claims (6)

  1. 一种基于数字孪生的生产过程仿真优化方法，其特征在于，包括：监控装配过程、运行状态、车间管控，采集分析设备数据，实时仿真车间运行状态，建造智能车间数字孪生模型；分析产品的结构组成、装配过程、工艺特点，设计不同领域不同类型的产线；整合工位的操作内容，计算各工步的进度偏差，调整工艺流程，均衡工位节拍、提升装配能力；建立数据库，实时展示使用情况、使用率、合格率，监控当前的运行、停机、维护、故障状态，基于数据库深度技术，诊断总装车间运行状态，形成车间管理的闭环。A production process simulation optimization method based on digital twins, which is characterized by: monitoring the assembly process, operating status, workshop management and control, collecting and analyzing equipment data, real-time simulation of the workshop running status, building a digital twin model of the intelligent workshop, and analyzing the structure of the product Composition, assembly process, process characteristics, design different types of production lines in different fields; integrate the operation content of the station, calculate the progress deviation of each process step, adjust the process flow, balance the station beat, and improve the assembly capacity; establish a database for real-time display Usage, usage rate, qualification rate, monitor the current operation, shutdown, maintenance, and fault status, based on the in-depth database technology, diagnose the operation status of the assembly shop, forming a closed loop of shop management.
  2. 根据权利要求1所述的基于数字孪生的生产过程仿真优化方法，其特征在于，所述建造智能车间数字孪生模型，包括：建立产品、设备、人员、***和环的模型，测量各资源的利用率，建立事件发生的时间序列模型，实现数字空间和逻辑空间的同步运行。The production process simulation optimization method based on digital twins according to claim 1, wherein the construction of a digital twin model of an intelligent workshop includes: establishing models of products, equipment, personnel, systems, and rings, and measuring the utilization of various resources Rate, establish the time series model of the occurrence of events, and realize the synchronous operation of the digital space and the logical space.
  3. 根据权利要求2所述的基于数字孪生的生产过程仿真优化方法，其特征在于，所述建立产品、设备、人员、***和环的模型，包括：采用统一表达式The production process simulation optimization method based on digital twins according to claim 2, characterized in that, the establishment of models of products, equipment, personnel, systems, and loops includes: adopting a unified expression
    DT ws＝DT equip∪DT prod∪DT pers∪DT env描述车间生产过程，其中DT ws为车间生产过程数字孪生模型，DT equip为设备数字孪生模型，DT prod为产品数字孪生模型，DT pers为人员数字孪生模型，DT env为环境孪生模型。 DT ws = DT equip ∪DT prod ∪DT pers ∪DT env describes the workshop production process, where DT ws is the workshop production process digital twin model, DT equip is the equipment digital twin model, DT prod is the product digital twin model, and DT pers is the personnel Digital twin model, DT env is an environmental twin model.
  4. 根据权利要求2所述的基于数字孪生的生产过程仿真优化方法，其特征在于，所述实现数字空间和逻辑空间的同步运行，包括：分析各资源随时间变化的利用率，采用聚类分析方法，获取瓶颈资源的发生模式，获取实体空间数据和信号的逻辑处理数据，通过实时数据驱动数字孪生模型的拟真化运行，实现产品、设备、人员、***和环境在数字空间和实体空间的映射与交互。The production process simulation optimization method based on digital twins according to claim 2, wherein the realization of the synchronous operation of the digital space and the logical space includes: analyzing the utilization rate of each resource over time, and adopting a cluster analysis method , Obtain the occurrence mode of bottleneck resources, obtain the physical space data and the logical processing data of the signal, and drive the virtual operation of the digital twin model through real-time data to realize the mapping of products, equipment, personnel, systems and environments in the digital space and the physical space Interact with.
  5. 根据权利要求4所述的基于数字孪生的生产过程仿真优化方法，其特征在于，所述采用聚类分析方法，包括：合并原始数据融，从较低层提取基本信息，将基本信息融合到中间层的表示信息和决策，再将这些决策和信息融合在较高层，形成最终分类结果，结合从原始数据逐层学习特征，自适应优化不同融合级别的组合，满足生产管理中智能决策对数据的要求。The production process simulation optimization method based on digital twins according to claim 4, wherein the cluster analysis method includes: merging the original data, extracting basic information from lower layers, and fusing the basic information into the middle Layers represent information and decisions, and then merge these decisions and information at higher layers to form the final classification result. Combining with layer-by-layer learning features from the original data, it adaptively optimizes the combination of different fusion levels to meet the requirements of intelligent decision-making on data in production management. Require.
  6. 根据权利要求1所述的基于数字孪生的生产过程仿真优化方法，其特征在于，所述整合工位的操作内容，包括：实时映射设备的加工动作、空间位置、运行状态；实时映射人员的身份和位置；实时映射生产计划进度、作业计划进度、工序进度；分析与管理库存状态、物流情况、加工工位的流程、在制品数据量。The digital twin-based production process simulation optimization method according to claim 1, wherein the operation content of the integration station includes: real-time mapping of the processing actions, spatial positions, and operating status of the equipment; real-time mapping of the identity of the personnel And location; real-time mapping of production plan progress, operation plan progress, and process progress; analysis and management of inventory status, logistics, processing station flow, and work-in-process data volume.
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