CN115097788A - Intelligent management and control platform based on digital twin factory - Google Patents

Abstract

The invention relates to the field of control platforms, in particular to an intelligent control platform based on a digital twin factory, which comprises industrial equipment for providing bottom hardware support and data sources, an edge computing layer for providing data computation, data storage and network bandwidth, an operation service platform, a data suite, a data analysis component and a cloud native architecture industrial PaaS layer, wherein processed and mined data information is provided for the digital twin factory, a digital model of an entity object is established, the state of the physical entity object is sensed, diagnosed and predicted in real time, the behavior of the physical entity object is regulated and controlled, and the decision of a stakeholder in the life cycle of the physical entity object is improved; the method is used for realizing industrial application of accurate prediction and control of the production process, production self-organization optimization scheduling, equipment full life cycle management, product quality tracing and control, and realizing accurate mapping, virtual-real interaction and intelligent intervention.

Description

Translated fromChinese

一种基于数字孪生工厂的智能管控平台An intelligent management and control platform based on digital twin factory

技术领域technical field

本发明涉及管控平台领域，具体涉及一种基于数字孪生工厂的智能管控平台。The invention relates to the field of management and control platforms, in particular to an intelligent management and control platform based on a digital twin factory.

背景技术Background technique

数字孪生是指以数字化的方式，结合大数据、三维可视化、虚拟仿真、物联网等手段实现对工厂物理对象的虚拟映射，用来描述、模拟、诊断、分析和预测工厂在现实环境中的行为，对产品的设计、制造、工艺，甚至整个工厂进行虚拟仿真，从而实现提高对工厂整体态势的管控能力、分析能力，助于提高产品研发和生产效率，提前预判出错的可能，以此降低事故风险。Digital twin refers to the virtual mapping of factory physical objects in a digital way, combined with big data, 3D visualization, virtual simulation, Internet of Things and other means, to describe, simulate, diagnose, analyze and predict the behavior of the factory in the real environment. , virtual simulation of product design, manufacturing, process, and even the entire factory, so as to improve the ability to control and analyze the overall situation of the factory, help improve product development and production efficiency, and predict the possibility of errors in advance, so as to reduce accident risk.

现代化工厂的自动化、信息化程度都相对较高，各种系统的部署，软硬件的联合应用，都使得工厂管理的重要性更为突出，同时也增加了管理的工作量。究其根本原因，还是因为庞大体系下的工厂信息不透明导致的。工厂集中监控可视化管理平台的缺失，导致管理人员无法实时、全面、准确的得知各生产及相关环节的实际状况，更无法及时的进行排查及做好及时处理，这也造成了工厂管理的窘境。The degree of automation and informatization of modern factories is relatively high. The deployment of various systems and the joint application of software and hardware make the importance of factory management more prominent, and also increase the workload of management. The root cause is the opaque factory information under the huge system. The lack of a centralized monitoring and visual management platform in the factory makes it impossible for managers to know the actual situation of each production and related links in a real-time, comprehensive and accurate manner, and it is also impossible to conduct timely investigation and timely processing, which also causes the dilemma of factory management. .

因此亟需一种工业数字孪生智能管控平台，融合数字孪生体与信息物理系统，开发虚实融合的数据处理、仿真分析、虚拟验证及生产过程运行决策等关键技术，实现精准映射、虚实交互、智能干预。管理人员能够实时掌握生产现场的生产进度，计划、目标达成情况，以及生产的人员、设备、物料、质量的相关信息等，达到透明化管理，最终满足三维数字孪生工厂的管理需求。Therefore, there is an urgent need for an industrial digital twin intelligent management and control platform, which integrates digital twins and cyber-physical systems, and develops key technologies such as data processing, simulation analysis, virtual verification, and production process operation decision-making for virtual-real integration to achieve accurate mapping, virtual-real interaction, intelligent intervention. Managers can grasp the production progress of the production site in real time, the achievement of plans and goals, and the relevant information of production personnel, equipment, materials, and quality, etc., to achieve transparent management and ultimately meet the management needs of the 3D digital twin factory.

发明内容SUMMARY OF THE INVENTION

本发明的目的在于提供能够有效进行虚实融合的数据处理、仿真分析、虚拟验证及生产过程运行决策，通过建立实体对象的数字模型，实时感知、诊断、预测物理实体对象的状态，调控物理实体对象的行为，改进利益相关方在物理实体对象生命周期内的决策，实现精准映射、虚实交互、智能干预的一种基于数字孪生工厂的智能管控平台。The purpose of the present invention is to provide data processing, simulation analysis, virtual verification and production process operation decision that can effectively carry out virtual and real fusion, by establishing a digital model of the physical object, real-time perception, diagnosis, and prediction of the state of the physical entity object, and control the physical entity object. It is an intelligent management and control platform based on digital twin factories, which can improve the decision-making of stakeholders in the life cycle of physical objects, and realize precise mapping, virtual-real interaction, and intelligent intervention.

为实现上述技术目的，达到上述技术效果，本发明是通过以下技术方案实现：In order to realize the above-mentioned technical purpose and achieve the above-mentioned technical effect, the present invention is realized through the following technical solutions:

一种基于数字孪生工厂的智能管控平台，其特征在于，包括：An intelligent management and control platform based on a digital twin factory, characterized in that it includes:

工业设备，用以提供底层硬件支持与数据来源，完成工业生产过程并将生产过程中产生的数据提供给边缘计算层；Industrial equipment to provide underlying hardware support and data sources, complete the industrial production process and provide the data generated in the production process to the edge computing layer;

边缘计算层，用以提供数据计算、数据存储与网络带宽，用于接收、处理来自工业设备的数据，包括数据实时表达式的计算、报警事件的生成、数据转换转储、实时数据推送与视频平台。The edge computing layer is used to provide data computing, data storage and network bandwidth for receiving and processing data from industrial equipment, including the calculation of real-time expressions of data, generation of alarm events, data conversion and dumping, real-time data push and video platform.

工业PaaS层，用以提供操作服务平台、数据套件、数据分析组件与云原生架构，用以提高数据的挖掘与应用、发挥云计算平台优势，并将部署、处理后的数据提供给数字孪生工厂；The industrial PaaS layer is used to provide operational service platforms, data suites, data analysis components and cloud-native architecture to improve data mining and application, leverage the advantages of cloud computing platforms, and provide deployed and processed data to digital twin factories ;

数字孪生工厂，包含多个数字孪生体，数字孪生体通过建立实体对象的数字模型，实时感知、诊断、预测物理实体对象的状态，调控物理实体对象的行为，改进利益相关方在物理实体对象生命周期内的决策；The digital twin factory includes multiple digital twins. The digital twin can perceive, diagnose, and predict the status of physical objects in real time by establishing digital models of physical objects, regulate the behavior of physical objects, and improve the life of stakeholders in physical objects. in-cycle decisions;

工业应用，基于数字孪生技术，包括开发设备自诊断、自组织运行与调度、工艺协同优化与生产过程运行控制，用以实现生产过程精准预测与控制、生产自组织优化调度、设备全生命周期管理、产品质量追溯与管控。Industrial application, based on digital twin technology, including developing equipment self-diagnosis, self-organizing operation and scheduling, process collaborative optimization and production process operation control, to achieve accurate production process prediction and control, production self-organization optimization scheduling, and equipment life cycle management , Product quality traceability and control.

进一步的，所述工业设备包括：Further, the industrial equipment includes:

无人化装备，用以完成系统的全天候工作过程；Unmanned equipment to complete the all-weather working process of the system;

智能装备，用以完成关键部件与高端装备的生产；Intelligent equipment to complete the production of key components and high-end equipment;

工业监控，用以远程维护监测系统，组件生产过程的安全体系；Industrial monitoring for remote maintenance monitoring system, safety system of component production process;

PLC/DCS/Scada，用以预设与载入控制指令，进行生产系统的控制；PLC/DCS/Scada, used to preset and load control instructions to control the production system;

物流仓储，用以生产产物的存储与转运。Logistics warehousing is used for the storage and transshipment of production products.

进一步的，所述边缘计算层包括：Further, the edge computing layer includes:

智能感知，用以识别设有的硬件设备接收的物理信号，映射到数字世界；IntelliSense, which is used to identify the physical signals received by the installed hardware devices and map them to the digital world;

机理建模，用以接收智能感知得到的数据信息，根据物理的变化规律建立系统模型；Mechanism modeling, which is used to receive the data information obtained by intelligent perception, and establish a system model according to the changing laws of physics;

精准控制，用以实现机理建模建立的系统模型高精度的工作与工作状态的改变；Precise control to achieve high-precision work and change of working state of the system model established by mechanism modeling;

视觉分析，用以识别所建立的系统模型工作状况与收集工作过程中产生的数据信息；Visual analysis to identify the working status of the established system model and collect data information generated during the working process;

数据分析，用以接收视觉分析下系统模型工作过程的数据信息，并进行转换计算与处理；Data analysis, which is used to receive the data information of the working process of the system model under the visual analysis, and perform conversion calculation and processing;

数据挖掘，用以接收数据分析下的数据信息，挖掘经过数据分析后的数据信息，将数据信息转化为生产信息与生产特征数据；Data mining is used to receive data information under data analysis, mine the data information after data analysis, and convert the data information into production information and production characteristic data;

过程优化，用以接收数据挖掘下的生产信息与生产特征数据，进行生产过程优化；Process optimization, which is used to receive production information and production characteristic data under data mining, and optimize the production process;

边缘智能，用于运算生成回归模型预测经过过程优化的生产过程，进行生产过程优化的核验与运算；Edge intelligence is used to generate regression models to predict the optimized production process, and to verify and calculate the optimization of the production process;

所述边缘计算层还包括：The edge computing layer also includes:

实时表达式的计算，用以实时监测系统模型工作状态，进行系统模型工作过程产生数据的实时计算；The calculation of real-time expressions is used to monitor the working status of the system model in real time, and perform real-time calculation of the data generated during the working process of the system model;

报警事件的生成，用以预设报警阈值，与实时表达式的计算数据信息实时对比，用以保障系统模型的安全工作与事故及时发现；The generation of alarm events is used to preset alarm thresholds and compare them in real time with the calculation data information of real-time expressions to ensure the safe work of the system model and the timely detection of accidents;

数据转换转储，用于将系统模型工作过程产生的数据信息转换与DBA定期保存；Data conversion dump, which is used to convert the data information generated by the working process of the system model and save it regularly by the DBA;

实时数据推送，用于将经过数据转换转储的数据信息实时推送；Real-time data push, which is used to push the data information dumped after data conversion in real time;

视频平台，用于将实时数据推送的数据信息呈现于视频平台，用以进行系统模型工作的呈现；The video platform is used to present the data information pushed by the real-time data on the video platform for the presentation of the system model work;

所述边缘计算层设有三组用于存储数据的数据库，所述三组数据库分别为内存数据库、关系数据库与时序数据库，所述边缘计算层设有视频通信服务，所述视频通信服务的通讯协议包括但不限于TCP/IP、MQTT、OPC、Modbus、OPC-UA与104。The edge computing layer is provided with three groups of databases for storing data, the three groups of databases are memory database, relational database and time series database respectively, and the edge computing layer is provided with a video communication service, and the communication protocol of the video communication service is Including but not limited to TCP/IP, MQTT, OPC, Modbus, OPC-UA and 104.

进一步的，所述工业PaaS层分为三部分，第一部分包括通用服务组件、人工智能、大数据套件(5S)，第二部分为数据分析组件，第三部分为云原生；Further, the industrial PaaS layer is divided into three parts, the first part includes general service components, artificial intelligence, and big data suite (5S), the second part is data analysis components, and the third part is cloud native;

所述通用服务组件包括：The general service components include:

企业网关，用以实现高层协议不同的网络互连，并对系统信息进行过滤与安全保障；The enterprise gateway is used to realize network interconnection of different high-level protocols, and to filter and secure system information;

安全中心，用以保障系统安全，实现病毒检索、响应与处理；Security Center, to ensure system security and realize virus retrieval, response and processing;

任务管理，用以安排系统工作任务，显示系统工作运行状态；Task management, which is used to arrange system work tasks and display system work operation status;

消息服务，用以显示待处理的系统任务，进行所需处理任务提示；The message service is used to display the pending system tasks and prompt the required processing tasks;

服务编排，用以部署完成系统运行的各个组件，进行服务配置；Service orchestration, which is used to deploy various components that complete the system operation and perform service configuration;

流程服务，用以进行系统运行进程的优化，用以提高系统的处理效率；Process service, which is used to optimize the system running process to improve the processing efficiency of the system;

所述大数据套件(5S)包括：The Big Data Suite (5S) includes:

数存，用以进行数据统一管理与数据存储；Data storage for unified data management and data storage;

数成，用以进行保障数据安全、数据加密、数据规划、数据开发、数据运行监控与元数据采集，进行数据的采集与分类；It is used to ensure data security, data encryption, data planning, data development, data operation monitoring and metadata collection, and to collect and classify data;

数智，用以进行智能运算，进行数据预处理、算法建模、模型训练、模型部署与模型调优，用以提高数据的契合性；Data intelligence is used to perform intelligent operations, data preprocessing, algorithm modeling, model training, model deployment and model tuning to improve data fit;

数现，用以数据可视化，进行数据报表、可视化报告与可视化视频；Data visualization for data visualization, data reporting, visual reporting and visual video;

数典，用以进行数据标准管理与数据资产管理，进行数据的规范管理；Data dictionary, used for data standard management and data asset management, and standardized management of data;

所述数据分析组件包括：The data analysis component includes:

分布氏计算，用以数据分解、数据分配进行多核处理，用以节约整体计算时间、提高计算效率；Distributed computing, which is used for data decomposition and data allocation for multi-core processing to save overall computing time and improve computing efficiency;

流式计算，用以进行大规模流动数据在不断变化的运动过程的实时分析；Streaming computing for real-time analysis of large-scale streaming data in changing motion processes;

数据模型，用以进行数据特征的抽象化，进行描述系统的静态特征、动态行为和约束条件；The data model is used to abstract data features and describe the static features, dynamic behaviors and constraints of the system;

数据算法，用以进行数据模型的分析与定义，进行挖掘数据模型的最佳参数；Data algorithms are used to analyze and define the data model, and to mine the best parameters of the data model;

多维分析，用以进行数据拆解与数据分离，进行多维度数据分析，用以提高数据特征的捕捉；Multi-dimensional analysis, for data disassembly and data separation, for multi-dimensional data analysis, to improve the capture of data features;

自助分析，用以进行数据资源的整合，以及数据的处理与获取；Self-service analysis to integrate data resources and process and obtain data;

BI分析，用以进行获取数据、分析信息以及改进，并根据商业规划优化决策；BI analysis to obtain data, analyze information and improve, and optimize decision-making based on business planning;

所述云原生包括：The cloud native includes:

容器云，用以进行数据的打包、储存、管理与转运；Container cloud for data packaging, storage, management and transfer;

微治理服务，用以划分单一应用程度、协调配合服务，进行服务资源的整合、KPI数据的容量管理；Micro-governance services are used to divide a single application level, coordinate and cooperate services, integrate service resources, and manage KPI data capacity;

Dev Ops，用于促进开发(应用程序/软件工程)、技术运营和质量保障(QA)部门之间的沟通、协作与整合；Dev Ops to facilitate communication, collaboration and integration between development (application/software engineering), technical operations and quality assurance (QA) departments;

代码管理，用于进行代码工程化的管理，进行高效的开发、测试与部署；Code management, for code engineering management, efficient development, testing and deployment;

应用包管理，用以根据不同程序的运行提供最佳的可用匹配应用包；Application package management to provide the best available matching application package according to the operation of different programs;

应用部署，用以创建、部署、查看、更新和删除系统的应用，以及编辑和释放系统的部署环境。Application deployment, to create, deploy, view, update, and delete the system's applications, as well as edit and release the system's deployment environment.

进一步的，所述数字孪生工厂包括：Further, the digital twin factory includes:

设备维护孪生体，用以进行物理世界中设备维护的数据信息映射于数字世界中构建数字模型；The equipment maintenance twin is used to map the data information of equipment maintenance in the physical world to build a digital model in the digital world;

生产调度孪生体，用以进行物理世界中生产调度的数据信息映射于数字世界中构建数字模型；The production scheduling twin is used to map the data information of production scheduling in the physical world to build a digital model in the digital world;

结构检测孪生体，用以进行物理世界中结构检测的数据信息映射于数字世界中构建数字模型；Structural detection twin, which is used to map the data information of structure detection in the physical world to build a digital model in the digital world;

工艺参数孪生体，用以进行物理世界中工艺参数的数据信息映射于数字世界中构建数字模型；Process parameter twin, which is used to map the data information of process parameters in the physical world to build a digital model in the digital world;

所述数字孪生工厂还包括云平台、工业互联网与移动互联技术，用以支撑数字孪生映射关系的信息技术基础架构；设有的数据挖掘、数字网格、机器学习、大数据分析、模拟仿真与可视化操作，用以支撑数字孪生应用于分析、预测、决策环节。The digital twin factory also includes a cloud platform, industrial Internet and mobile internet technologies to support the information technology infrastructure of the digital twin mapping relationship; data mining, digital grid, machine learning, big data analysis, simulation and simulation are provided. Visual operations are used to support the application of digital twins in analysis, prediction, and decision-making.

进一步的，所述工业应用包括：Further, the industrial applications include:

设备自诊断，用以判别设备自身有无故障并确定故障部位，且进行故障类型的判断；Equipment self-diagnosis is used to determine whether the equipment itself has faults, determine the fault location, and judge the fault type;

数字化学习工厂，用以工厂、车间和生产线以及产品的设计到制造的转化，减低设计到生产制造之间的不确定性；The digital learning factory is used for the transformation of factories, workshops and production lines and product design to manufacturing, reducing the uncertainty between design and manufacturing;

集控系统，用以整合数据，进行数据监视、报警、分析、计算、使用，用以实现数据与管理一体化，进行数据归纳、分析和整理；The centralized control system is used to integrate data, perform data monitoring, alarm, analysis, calculation and use, to realize the integration of data and management, and to conduct data induction, analysis and sorting;

决策支持，用以提供分析问题、建立模型、模拟决策过程和方案的环境，以及调用各种信息资源和分析工具；Decision support, which provides an environment for analyzing problems, building models, simulating decision-making processes and solutions, and calling various information resources and analysis tools;

工艺协同优化，用以提供设计规则，协同设计与工艺的要求；Process collaborative optimization to provide design rules, collaborative design and process requirements;

物料配方优化，用以进行试验、优化、评价，选用原辅材料，并确定各种原辅材料的用量配比关系；Material formula optimization is used for testing, optimization, evaluation, selection of raw and auxiliary materials, and determination of the proportioning relationship of various raw and auxiliary materials;

生产过程运行控制，用以协同各个生产工序，进行生产全流程的产品质量、产量、消耗、成本综合生产指标的优化；The production process operation control is used to coordinate the various production processes to optimize the comprehensive production indicators of product quality, output, consumption and cost in the whole production process;

自组织运行与调度，用以进行组织、指挥、指导与协调。Self-organizing operation and scheduling for organization, command, guidance and coordination.

进一步的，所述智能管控平台分为网络体系与安全体系，网络体系包括工业应用与数字孪生工厂，用以支撑系统的运行与状态；安全体系包括工业设备与边缘计算层，用以支撑系统的通信安全与生产安全；工业Paas层为网络体系与安全体系配合工作部分，用以联系网络体系与安全体系。Further, the intelligent management and control platform is divided into a network system and a security system. The network system includes industrial applications and digital twin factories, which are used to support the operation and status of the system; the security system includes industrial equipment and edge computing layers, which are used to support the system. Communication security and production security; the industrial Paas layer is the working part of the network system and the security system, which is used to connect the network system and the security system.

进一步的，所述数字孪生工厂组成要素包括物理真实工厂、生产现场过程数据与数字工厂模型，物理真实工厂生产过程生产生产现场过程数据，数字工厂模型接收处理生产现场过程数据，数字工厂模型通过生产现场过程数据进行与物理真实工厂进行关联映射与匹配；基于云平台、物联网、移动互联与工业互联网，运用数据挖掘、数字网络、机器学习与大数据分析，进行生产现场过程数据的分析、预测与决策支持，进行对制造单元、生产进度、物流、质量的实时动态优化调整，进行生产过程的模拟仿真、可视化操作与虚拟现实计算。Further, the components of the digital twin factory include a physical real factory, production site process data and a digital factory model, the physical real factory production process produces production site process data, the digital factory model receives and processes the production site process data, and the digital factory model passes the production process data. On-site process data is mapped and matched with physical real factories; based on cloud platform, Internet of Things, mobile Internet and industrial Internet, data mining, digital network, machine learning and big data analysis are used to analyze and predict production site process data With decision support, real-time dynamic optimization and adjustment of manufacturing units, production schedules, logistics, and quality, and simulation of production processes, visual operations, and virtual reality calculations.

本发明的有益效果：Beneficial effects of the present invention:

一种基于数字孪生工厂的智能管控平台能够有效进行虚实融合的数据处理、仿真分析、虚拟验证及生产过程运行决策，通过建立实体对象的数字模型，实时感知、诊断、预测物理实体对象的状态，调控物理实体对象的行为，改进利益相关方在物理实体对象生命周期内的决策，实现精准映射、虚实交互、智能干预。An intelligent management and control platform based on a digital twin factory can effectively carry out data processing, simulation analysis, virtual verification and production process operation decision-making of virtual and real integration. Regulate the behavior of physical objects, improve the decision-making of stakeholders in the life cycle of physical objects, and achieve accurate mapping, virtual-real interaction, and intelligent intervention.

本发明的数字孪生工厂包含多个数字孪生体，包括设备维护孪生体、生产调度孪生体、结构检测孪生体、工艺参数孪生体等。数字孪生体通过建立实体对象的数字模型，实时感知、诊断、预测物理实体对象的状态，调控物理实体对象的行为，改进利益相关方在物理实体对象生命周期内的决策。通过云平台、工业互联网、移动互联技术等，支撑数字孪生映射关系的信息技术基础架构。数据挖掘、数字网格、机器学习、大数据分析、模拟仿真、可视化操作等技术，支撑着数字孪生在分析、预测、决策环节实现应用价值。The digital twin factory of the present invention includes a plurality of digital twins, including equipment maintenance twins, production scheduling twins, structure inspection twins, process parameter twins, and the like. By establishing a digital model of the physical object, the digital twin can perceive, diagnose, and predict the state of the physical object in real time, regulate the behavior of the physical object, and improve the decision-making of stakeholders in the life cycle of the physical object. The information technology infrastructure that supports the digital twin mapping relationship through cloud platform, industrial Internet, mobile Internet technology, etc. Technologies such as data mining, digital grid, machine learning, big data analysis, simulation and visualization support the realization of application value of digital twins in analysis, prediction, and decision-making.

本发明的数字孪生工厂组成要素包括物理真实工厂、生产现场过程数据与数字工厂模型，物理真实工厂生产过程生产生产现场过程数据，数字工厂模型接收处理生产现场过程数据，数字工厂模型通过生产现场过程数据进行与物理真实工厂进行关联映射与匹配；基于云平台、物联网、移动互联与工业互联网，运用数据挖掘、数字网络、机器学习与大数据分析，进行生产现场过程数据的分析、预测与决策支持，进行对制造单元、生产进度、物流、质量的实时动态优化调整，进行生产过程的模拟仿真、可视化操作与虚拟现实计算。The components of the digital twin factory of the present invention include a physical real factory, production site process data and a digital factory model. The physical real factory production process produces production site process data. The digital factory model receives and processes the production site process data. Data is mapped and matched with physical real factories; based on cloud platform, Internet of Things, mobile Internet and industrial Internet, data mining, digital network, machine learning and big data analysis are used to analyze, predict and make decisions on production site process data Support, carry out real-time dynamic optimization and adjustment of manufacturing units, production schedule, logistics, and quality, and carry out simulation simulation, visual operation and virtual reality calculation of production process.

本发明的工业设备，用以提供底层硬件支持与数据来源，边缘计算层，用以提供数据计算、数据存储与网络带宽，工业PaaS层，用以提供操作服务平台、数据套件、数据分析组件与云原生架构，将经过处理挖掘的数据信息与数据模型提供给数字孪生工厂，保证数字孪生工厂与物理真实工厂的良好映射，能够对工厂进行设备运行数据分析，设备故障预警与远程解决设备故障，实现故障预判和及时维修；通过数字孪生技术，开发设备自诊断、自组织运行与调度、工艺协同优化、生产过程运行控制等应用，实现生产过程精准预测与控制、生产自组织优化调度、设备全生命周期管理、产品质量追溯与管控。The industrial equipment of the present invention is used to provide underlying hardware support and data sources, the edge computing layer is used to provide data computing, data storage and network bandwidth, and the industrial PaaS layer is used to provide operation service platforms, data suites, data analysis components and The cloud-native architecture provides the processed and mined data information and data models to the digital twin factory to ensure a good mapping between the digital twin factory and the physical real factory. Realize fault prediction and timely maintenance; through digital twin technology, develop applications such as equipment self-diagnosis, self-organized operation and scheduling, process collaborative optimization, and production process operation control to achieve accurate production process prediction and control, production self-organization optimization scheduling, equipment Full life cycle management, product quality traceability and control.

当然，实施本发明的任一产品并不一定需要同时达到以上所述的所有优点。Of course, it is not necessary for any product embodying the present invention to achieve all of the above-described advantages simultaneously.

附图说明Description of drawings

为了更清楚地说明本发明实施例的技术方案，下面将对实施例描述所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

图1为本发明实施例所述一种基于数字孪生工厂的智能管控平台的整体示意图；1 is an overall schematic diagram of an intelligent management and control platform based on a digital twin factory according to an embodiment of the present invention;

图2为本发明实施例所述一种基于数字孪生工厂的智能管控平台的数字孪生工厂组成要素示意图；2 is a schematic diagram of the components of a digital twin factory of an intelligent management and control platform based on a digital twin factory according to an embodiment of the present invention;

具体实施方式Detailed ways

下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

实施例1Example 1

如图1所示As shown in Figure 1

一种基于数字孪生工厂的智能管控平台，其特征在于，包括：An intelligent management and control platform based on a digital twin factory, characterized in that it includes:

工业设备，用以提供底层硬件支持与数据来源，完成工业生产过程并将生产过程中产生的数据提供给边缘计算层；Industrial equipment to provide underlying hardware support and data sources, complete the industrial production process and provide the data generated in the production process to the edge computing layer;

边缘计算层，用以提供数据计算、数据存储与网络带宽，用于接收、处理来自工业设备的数据，包括数据实时表达式的计算、报警事件的生成、数据转换转储、实时数据推送与视频平台。The edge computing layer is used to provide data computing, data storage and network bandwidth for receiving and processing data from industrial equipment, including the calculation of real-time expressions of data, generation of alarm events, data conversion and dumping, real-time data push and video platform.

工业PaaS层，用以提供操作服务平台、数据套件、数据分析组件与云原生架构，用以提高数据的挖掘与应用、发挥云计算平台优势，并将部署、处理后的数据提供给数字孪生工厂；The industrial PaaS layer is used to provide operational service platforms, data suites, data analysis components and cloud-native architecture to improve data mining and application, leverage the advantages of cloud computing platforms, and provide deployed and processed data to digital twin factories ;

数字孪生工厂，包含多个数字孪生体，数字孪生体通过建立实体对象的数字模型，实时感知、诊断、预测物理实体对象的状态，调控物理实体对象的行为，改进利益相关方在物理实体对象生命周期内的决策；The digital twin factory includes multiple digital twins. The digital twin can perceive, diagnose, and predict the status of physical objects in real time by establishing digital models of physical objects, regulate the behavior of physical objects, and improve the life of stakeholders in physical objects. in-cycle decisions;

工业应用，基于数字孪生技术，包括开发设备自诊断、自组织运行与调度、工艺协同优化与生产过程运行控制，用以实现生产过程精准预测与控制、生产自组织优化调度、设备全生命周期管理、产品质量追溯与管控。Industrial application, based on digital twin technology, including developing equipment self-diagnosis, self-organizing operation and scheduling, process collaborative optimization and production process operation control, to achieve accurate production process prediction and control, production self-organization optimization scheduling, and equipment life cycle management , Product quality traceability and control.

实施例2Example 2

如图1所示As shown in Figure 1

所述工业设备包括：The industrial equipment includes:

无人化装备，用以完成系统的全天候工作过程；Unmanned equipment to complete the all-weather working process of the system;

智能装备，用以完成关键部件与高端装备的生产；Intelligent equipment to complete the production of key components and high-end equipment;

工业监控，用以远程维护监测系统，组件生产过程的安全体系；Industrial monitoring for remote maintenance monitoring system, safety system of component production process;

PLC/DCS/Scada，用以预设与载入控制指令，进行生产系统的控制；PLC/DCS/Scada, used to preset and load control instructions to control the production system;

物流仓储，用以生产产物的存储与转运。Logistics warehousing is used for the storage and transshipment of production products.

实施例3Example 3

如图1所示As shown in Figure 1

所述边缘计算层包括：The edge computing layer includes:

智能感知，用以识别设有的硬件设备接收的物理信号，映射到数字世界；IntelliSense, which is used to identify the physical signals received by the installed hardware devices and map them to the digital world;

机理建模，用以接收智能感知得到的数据信息，根据物理的变化规律建立系统模型；Mechanism modeling, which is used to receive the data information obtained by intelligent perception, and establish a system model according to the changing laws of physics;

精准控制，用以实现机理建模建立的系统模型高精度的工作与工作状态的改变；Precise control to achieve high-precision work and change of working state of the system model established by mechanism modeling;

视觉分析，用以识别所建立的系统模型工作状况与收集工作过程中产生的数据信息；Visual analysis to identify the working status of the established system model and collect data information generated during the working process;

数据分析，用以接收视觉分析下系统模型工作过程的数据信息，并进行转换计算与处理；Data analysis, which is used to receive the data information of the working process of the system model under the visual analysis, and perform conversion calculation and processing;

数据挖掘，用以接收数据分析下的数据信息，挖掘经过数据分析后的数据信息，将数据信息转化为生产信息与生产特征数据；Data mining is used to receive data information under data analysis, mine the data information after data analysis, and convert the data information into production information and production characteristic data;

过程优化，用以接收数据挖掘下的生产信息与生产特征数据，进行生产过程优化；Process optimization, which is used to receive production information and production characteristic data under data mining, and optimize the production process;

边缘智能，用于运算生成回归模型预测经过过程优化的生产过程，进行生产过程优化的核验与运算；Edge intelligence is used to generate regression models to predict the optimized production process, and to verify and calculate the optimization of the production process;

所述边缘计算层还包括：The edge computing layer also includes:

实时表达式的计算，用以实时监测系统模型工作状态，进行系统模型工作过程产生数据的实时计算；The calculation of real-time expressions is used to monitor the working status of the system model in real time, and perform real-time calculation of the data generated during the working process of the system model;

报警事件的生成，用以预设报警阈值，与实时表达式的计算数据信息实时对比，用以保障系统模型的安全工作与事故及时发现；The generation of alarm events is used to preset alarm thresholds and compare them in real time with the calculation data information of real-time expressions to ensure the safe work of the system model and the timely detection of accidents;

数据转换转储，用于将系统模型工作过程产生的数据信息转换与DBA定期保存；Data conversion dump, which is used to convert the data information generated by the working process of the system model and save it regularly by the DBA;

实时数据推送，用于将经过数据转换转储的数据信息实时推送；Real-time data push, which is used to push the data information dumped after data conversion in real time;

视频平台，用于将实时数据推送的数据信息呈现于视频平台，用以进行系统模型工作的呈现；The video platform is used to present the data information pushed by the real-time data on the video platform for the presentation of the system model work;

所述边缘计算层设有三组用于存储数据的数据库，所述三组数据库分别为内存数据库、关系数据库与时序数据库，所述边缘计算层设有视频通信服务，所述视频通信服务的通讯协议包括但不限于TCP/IP、MQTT、OPC、Mod bus、OPC-UA与104。The edge computing layer is provided with three groups of databases for storing data, the three groups of databases are memory database, relational database and time series database respectively, and the edge computing layer is provided with a video communication service, and the communication protocol of the video communication service is Including but not limited to TCP/IP, MQTT, OPC, Modbus, OPC-UA and 104.

实施例4Example 4

如图1所示As shown in Figure 1

所述工业PaaS层分为三部分，第一部分包括通用服务组件、人工智能、大数据套件(5S)，第二部分为数据分析组件，第三部分为云原生；The industrial PaaS layer is divided into three parts, the first part includes general service components, artificial intelligence, and big data suite (5S), the second part is data analysis components, and the third part is cloud native;

所述通用服务组件包括：The general service components include:

企业网关，用以实现高层协议不同的网络互连，并对系统信息进行过滤与安全保障；The enterprise gateway is used to realize network interconnection of different high-level protocols, and to filter and secure system information;

安全中心，用以保障系统安全，实现病毒检索、响应与处理；Security Center, to ensure system security and realize virus retrieval, response and processing;

任务管理，用以安排系统工作任务，显示系统工作运行状态；Task management, which is used to arrange system work tasks and display system work operation status;

消息服务，用以显示待处理的系统任务，进行所需处理任务提示；The message service is used to display the pending system tasks and prompt the required processing tasks;

服务编排，用以部署完成系统运行的各个组件，进行服务配置；Service orchestration, which is used to deploy various components that complete the system operation and perform service configuration;

流程服务，用以进行系统运行进程的优化，用以提高系统的处理效率；Process service, which is used to optimize the system running process to improve the processing efficiency of the system;

所述大数据套件(5S)包括：The Big Data Suite (5S) includes:

数存，用以进行数据统一管理与数据存储；Data storage for unified data management and data storage;

数成，用以进行保障数据安全、数据加密、数据规划、数据开发、数据运行监控与元数据采集，进行数据的采集与分类；It is used to ensure data security, data encryption, data planning, data development, data operation monitoring and metadata collection, and to collect and classify data;

数智，用以进行智能运算，进行数据预处理、算法建模、模型训练、模型部署与模型调优，用以提高数据的契合性；Data intelligence is used to perform intelligent operations, data preprocessing, algorithm modeling, model training, model deployment and model tuning to improve data fit;

数现，用以数据可视化，进行数据报表、可视化报告与可视化视频；Data visualization for data visualization, data reporting, visual reporting and visual video;

数典，用以进行数据标准管理与数据资产管理，进行数据的规范管理；Data dictionary, used for data standard management and data asset management, and standardized management of data;

所述数据分析组件包括：The data analysis component includes:

分布氏计算，用以数据分解、数据分配进行多核处理，用以节约整体计算时间、提高计算效率；Distributed computing, which is used for data decomposition and data allocation for multi-core processing to save overall computing time and improve computing efficiency;

流式计算，用以进行大规模流动数据在不断变化的运动过程的实时分析；Streaming computing for real-time analysis of large-scale streaming data in changing motion processes;

数据模型，用以进行数据特征的抽象化，进行描述系统的静态特征、动态行为和约束条件；The data model is used to abstract data features and describe the static features, dynamic behaviors and constraints of the system;

数据算法，用以进行数据模型的分析与定义，进行挖掘数据模型的最佳参数；Data algorithms are used to analyze and define the data model, and to mine the best parameters of the data model;

多维分析，用以进行数据拆解与数据分离，进行多维度数据分析，用以提高数据特征的捕捉；Multi-dimensional analysis, for data disassembly and data separation, for multi-dimensional data analysis, to improve the capture of data features;

自助分析，用以进行数据资源的整合，以及数据的处理与获取；Self-service analysis to integrate data resources and process and obtain data;

BI分析，用以进行获取数据、分析信息以及改进，并根据商业规划优化决策；BI analysis to obtain data, analyze information and improve, and optimize decision-making based on business planning;

所述云原生包括：The cloud native includes:

容器云，用以进行数据的打包、储存、管理与转运；Container cloud for data packaging, storage, management and transfer;

微治理服务，用以划分单一应用程度、协调配合服务，进行服务资源的整合、KPI数据的容量管理；Micro-governance services are used to divide a single application level, coordinate and cooperate services, integrate service resources, and manage KPI data capacity;

Dev Ops，用于促进开发(应用程序/软件工程)、技术运营和质量保障(QA)部门之间的沟通、协作与整合；Dev Ops to facilitate communication, collaboration and integration between development (application/software engineering), technical operations and quality assurance (QA) departments;

代码管理，用于进行代码工程化的管理，进行高效的开发、测试与部署；Code management, for code engineering management, efficient development, testing and deployment;

应用包管理，用以根据不同程序的运行提供最佳的可用匹配应用包；Application package management to provide the best available matching application package according to the operation of different programs;

应用部署，用以创建、部署、查看、更新和删除系统的应用，以及编辑和释放系统的部署环境。Application deployment, to create, deploy, view, update, and delete the system's applications, as well as edit and release the system's deployment environment.

实施例5Example 5

如图1所示As shown in Figure 1

所述数字孪生工厂包括：The digital twin factory includes:

设备维护孪生体，用以进行物理世界中设备维护的数据信息映射于数字世界中构建数字模型；The equipment maintenance twin is used to map the data information of equipment maintenance in the physical world to build a digital model in the digital world;

生产调度孪生体，用以进行物理世界中生产调度的数据信息映射于数字世界中构建数字模型；The production scheduling twin is used to map the data information of production scheduling in the physical world to build a digital model in the digital world;

结构检测孪生体，用以进行物理世界中结构检测的数据信息映射于数字世界中构建数字模型；Structural detection twin, which is used to map the data information of structure detection in the physical world to build a digital model in the digital world;

工艺参数孪生体，用以进行物理世界中工艺参数的数据信息映射于数字世界中构建数字模型；Process parameter twin, which is used to map the data information of process parameters in the physical world to build a digital model in the digital world;

所述数字孪生工厂还包括云平台、工业互联网与移动互联技术，用以支撑数字孪生映射关系的信息技术基础架构；设有的数据挖掘、数字网格、机器学习、大数据分析、模拟仿真与可视化操作，用以支撑数字孪生应用于分析、预测、决策环节。The digital twin factory also includes a cloud platform, industrial Internet and mobile internet technologies to support the information technology infrastructure of the digital twin mapping relationship; data mining, digital grid, machine learning, big data analysis, simulation and simulation are provided. Visual operations are used to support the application of digital twins in analysis, prediction, and decision-making.

实施例6Example 6

如图1所示As shown in Figure 1

所述工业应用包括：The industrial applications include:

设备自诊断，用以判别设备自身有无故障并确定故障部位，且进行故障类型的判断；Equipment self-diagnosis is used to determine whether the equipment itself has faults, determine the fault location, and judge the fault type;

数字化学习工厂，用以工厂、车间和生产线以及产品的设计到制造的转化，减低设计到生产制造之间的不确定性；The digital learning factory is used for the transformation of factories, workshops and production lines and product design to manufacturing, reducing the uncertainty between design and manufacturing;

集控系统，用以整合数据，进行数据监视、报警、分析、计算、使用，用以实现数据与管理一体化，进行数据归纳、分析和整理；The centralized control system is used to integrate data, perform data monitoring, alarm, analysis, calculation and use, to realize the integration of data and management, and to conduct data induction, analysis and sorting;

决策支持，用以提供分析问题、建立模型、模拟决策过程和方案的环境，以及调用各种信息资源和分析工具；Decision support, which provides an environment for analyzing problems, building models, simulating decision-making processes and solutions, and calling various information resources and analysis tools;

工艺协同优化，用以提供设计规则，协同设计与工艺的要求；Process collaborative optimization to provide design rules, collaborative design and process requirements;

物料配方优化，用以进行试验、优化、评价，选用原辅材料，并确定各种原辅材料的用量配比关系；Material formula optimization is used for testing, optimization, evaluation, selection of raw and auxiliary materials, and determination of the proportioning relationship of various raw and auxiliary materials;

生产过程运行控制，用以协同各个生产工序，进行生产全流程的产品质量、产量、消耗、成本综合生产指标的优化；The production process operation control is used to coordinate the various production processes to optimize the comprehensive production indicators of product quality, output, consumption and cost in the whole production process;

自组织运行与调度，用以进行组织、指挥、指导与协调。Self-organizing operation and scheduling for organization, command, guidance and coordination.

实施例7Example 7

如图1所示As shown in Figure 1

所述智能管控平台分为网络体系与安全体系，网络体系包括工业应用与数字孪生工厂，用以支撑系统的运行与状态；安全体系包括工业设备与边缘计算层，用以支撑系统的通信安全与生产安全；工业Paas层为网络体系与安全体系配合工作部分，用以联系网络体系与安全体系。The intelligent management and control platform is divided into a network system and a security system. The network system includes industrial applications and digital twin factories, which are used to support the operation and status of the system; the security system includes industrial equipment and edge computing layers, which are used to support the communication security and Production safety; the industrial Paas layer is the working part of the network system and the security system, which is used to connect the network system and the security system.

实施例8Example 8

如图2所示as shown in picture 2

所述数字孪生工厂组成要素包括物理真实工厂、生产现场过程数据与数字工厂模型，物理真实工厂生产过程生产生产现场过程数据，数字工厂模型接收处理生产现场过程数据，数字工厂模型通过生产现场过程数据进行与物理真实工厂进行关联映射与匹配；基于云平台、物联网、移动互联与工业互联网，运用数据挖掘、数字网络、机器学习与大数据分析，进行生产现场过程数据的分析、预测与决策支持，进行对制造单元、生产进度、物流、质量的实时动态优化调整，进行生产过程的模拟仿真、可视化操作与虚拟现实计算。The components of the digital twin factory include a physical real factory, production site process data and a digital factory model, the physical real factory production process produces production site process data, the digital factory model receives and processes the production site process data, and the digital factory model passes the production site process data. Carry out correlation mapping and matching with physical real factories; based on cloud platform, Internet of Things, mobile Internet and industrial Internet, use data mining, digital network, machine learning and big data analysis to analyze, predict and support production site process data , carry out real-time dynamic optimization and adjustment of manufacturing unit, production schedule, logistics, and quality, and carry out simulation simulation, visual operation and virtual reality calculation of production process.

实施例9Example 9

如图1-2所示As shown in Figure 1-2

一种基于数字孪生工厂的智能管控平台，其特征在于，包括：An intelligent management and control platform based on a digital twin factory, characterized in that it includes:

工业设备，用以提供底层硬件支持与数据来源，完成工业生产过程并将生产过程中产生的数据提供给边缘计算层；Industrial equipment to provide underlying hardware support and data sources, complete the industrial production process and provide the data generated in the production process to the edge computing layer;

边缘计算层，用以提供数据计算、数据存储与网络带宽，用于接收、处理来自工业设备的数据，包括数据实时表达式的计算、报警事件的生成、数据转换转储、实时数据推送与视频平台。The edge computing layer is used to provide data computing, data storage and network bandwidth for receiving and processing data from industrial equipment, including the calculation of real-time expressions of data, generation of alarm events, data conversion and dumping, real-time data push and video platform.

工业PaaS层，用以提供操作服务平台、数据套件、数据分析组件与云原生架构，用以提高数据的挖掘与应用、发挥云计算平台优势，并将部署、处理后的数据提供给数字孪生工厂；The industrial PaaS layer is used to provide operational service platforms, data suites, data analysis components and cloud-native architecture to improve data mining and application, leverage the advantages of cloud computing platforms, and provide deployed and processed data to digital twin factories ;

数字孪生工厂，包含多个数字孪生体，数字孪生体通过建立实体对象的数字模型，实时感知、诊断、预测物理实体对象的状态，调控物理实体对象的行为，改进利益相关方在物理实体对象生命周期内的决策；The digital twin factory includes multiple digital twins. The digital twin can perceive, diagnose, and predict the status of physical objects in real time by establishing digital models of physical objects, regulate the behavior of physical objects, and improve the life of stakeholders in physical objects. in-cycle decisions;

工业应用，基于数字孪生技术，包括开发设备自诊断、自组织运行与调度、工艺协同优化与生产过程运行控制，用以实现生产过程精准预测与控制、生产自组织优化调度、设备全生命周期管理、产品质量追溯与管控。Industrial application, based on digital twin technology, including developing equipment self-diagnosis, self-organizing operation and scheduling, process collaborative optimization and production process operation control, to achieve accurate production process prediction and control, production self-organization optimization scheduling, and equipment life cycle management , Product quality traceability and control.

所述工业设备包括：The industrial equipment includes:

无人化装备，用以完成系统的全天候工作过程；Unmanned equipment to complete the all-weather working process of the system;

智能装备，用以完成关键部件与高端装备的生产；Intelligent equipment to complete the production of key components and high-end equipment;

工业监控，用以远程维护监测系统，组件生产过程的安全体系；Industrial monitoring for remote maintenance monitoring system, safety system of component production process;

PLC/DCS/Scada，用以预设与载入控制指令，进行生产系统的控制；PLC/DCS/Scada, used to preset and load control instructions to control the production system;

物流仓储，用以生产产物的存储与转运。Logistics warehousing is used for the storage and transshipment of production products.

所述边缘计算层包括：The edge computing layer includes:

智能感知，用以识别设有的硬件设备接收的物理信号，映射到数字世界；IntelliSense, which is used to identify the physical signals received by the installed hardware devices and map them to the digital world;

机理建模，用以接收智能感知得到的数据信息，根据物理的变化规律建立系统模型；Mechanism modeling, which is used to receive the data information obtained by intelligent perception, and establish a system model according to the changing laws of physics;

精准控制，用以实现机理建模建立的系统模型高精度的工作与工作状态的改变；Precise control to achieve high-precision work and change of working state of the system model established by mechanism modeling;

视觉分析，用以识别所建立的系统模型工作状况与收集工作过程中产生的数据信息；Visual analysis to identify the working status of the established system model and collect data information generated during the working process;

数据分析，用以接收视觉分析下系统模型工作过程的数据信息，并进行转换计算与处理；Data analysis, which is used to receive the data information of the working process of the system model under the visual analysis, and perform conversion calculation and processing;

数据挖掘，用以接收数据分析下的数据信息，挖掘经过数据分析后的数据信息，将数据信息转化为生产信息与生产特征数据；Data mining is used to receive data information under data analysis, mine the data information after data analysis, and convert the data information into production information and production characteristic data;

过程优化，用以接收数据挖掘下的生产信息与生产特征数据，进行生产过程优化；Process optimization, which is used to receive production information and production characteristic data under data mining, and optimize the production process;

边缘智能，用于运算生成回归模型预测经过过程优化的生产过程，进行生产过程优化的核验与运算；Edge intelligence is used to generate regression models to predict the optimized production process, and to verify and calculate the optimization of the production process;

所述边缘计算层还包括：The edge computing layer also includes:

实时表达式的计算，用以实时监测系统模型工作状态，进行系统模型工作过程产生数据的实时计算；The calculation of real-time expressions is used to monitor the working status of the system model in real time, and perform real-time calculation of the data generated during the working process of the system model;

报警事件的生成，用以预设报警阈值，与实时表达式的计算数据信息实时对比，用以保障系统模型的安全工作与事故及时发现；The generation of alarm events is used to preset alarm thresholds and compare them in real time with the calculation data information of real-time expressions to ensure the safe work of the system model and the timely detection of accidents;

数据转换转储，用于将系统模型工作过程产生的数据信息转换与DBA定期保存；Data conversion dump, which is used to convert the data information generated by the working process of the system model and save it regularly by the DBA;

实时数据推送，用于将经过数据转换转储的数据信息实时推送；Real-time data push, which is used to push the data information dumped after data conversion in real time;

视频平台，用于将实时数据推送的数据信息呈现于视频平台，用以进行系统模型工作的呈现；The video platform is used to present the data information pushed by the real-time data on the video platform for the presentation of the system model work;

所述边缘计算层设有三组用于存储数据的数据库，所述三组数据库分别为内存数据库、关系数据库与时序数据库，所述边缘计算层设有视频通信服务，所述视频通信服务的通讯协议包括但不限于TCP/IP、MQTT、OPC、Mod bus、OPC-UA与104。The edge computing layer is provided with three groups of databases for storing data, the three groups of databases are memory database, relational database and time series database respectively, and the edge computing layer is provided with a video communication service, and the communication protocol of the video communication service is Including but not limited to TCP/IP, MQTT, OPC, Modbus, OPC-UA and 104.

所述工业PaaS层分为三部分，第一部分包括通用服务组件、人工智能、大数据套件(5S)，第二部分为数据分析组件，第三部分为云原生；The industrial PaaS layer is divided into three parts, the first part includes general service components, artificial intelligence, and big data suite (5S), the second part is data analysis components, and the third part is cloud native;

所述通用服务组件包括：The general service components include:

企业网关，用以实现高层协议不同的网络互连，并对系统信息进行过滤与安全保障；The enterprise gateway is used to realize network interconnection of different high-level protocols, and to filter and secure system information;

安全中心，用以保障系统安全，实现病毒检索、响应与处理；Security Center, to ensure system security and realize virus retrieval, response and processing;

任务管理，用以安排系统工作任务，显示系统工作运行状态；Task management, which is used to arrange system work tasks and display system work operation status;

消息服务，用以显示待处理的系统任务，进行所需处理任务提示；The message service is used to display the pending system tasks and prompt the required processing tasks;

服务编排，用以部署完成系统运行的各个组件，进行服务配置；Service orchestration, which is used to deploy various components that complete the system operation and perform service configuration;

流程服务，用以进行系统运行进程的优化，用以提高系统的处理效率；Process service, which is used to optimize the system running process to improve the processing efficiency of the system;

所述大数据套件(5S)包括：The Big Data Suite (5S) includes:

数存，用以进行数据统一管理与数据存储；Data storage for unified data management and data storage;

数成，用以进行保障数据安全、数据加密、数据规划、数据开发、数据运行监控与元数据采集，进行数据的采集与分类；It is used to ensure data security, data encryption, data planning, data development, data operation monitoring and metadata collection, and to collect and classify data;

数智，用以进行智能运算，进行数据预处理、算法建模、模型训练、模型部署与模型调优，用以提高数据的契合性；Data intelligence is used to perform intelligent operations, data preprocessing, algorithm modeling, model training, model deployment and model tuning to improve data fit;

数现，用以数据可视化，进行数据报表、可视化报告与可视化视频；Data visualization, for data visualization, data reporting, visual reporting and visual video;

数典，用以进行数据标准管理与数据资产管理，进行数据的规范管理；Data dictionary, used for data standard management and data asset management, and standardized management of data;

所述数据分析组件包括：The data analysis component includes:

分布氏计算，用以数据分解、数据分配进行多核处理，用以节约整体计算时间、提高计算效率；Distributed computing, which is used for data decomposition and data allocation for multi-core processing to save overall computing time and improve computing efficiency;

流式计算，用以进行大规模流动数据在不断变化的运动过程的实时分析；Streaming computing for real-time analysis of large-scale streaming data in changing motion processes;

数据模型，用以进行数据特征的抽象化，进行描述系统的静态特征、动态行为和约束条件；The data model is used to abstract data features and describe the static features, dynamic behaviors and constraints of the system;

数据算法，用以进行数据模型的分析与定义，进行挖掘数据模型的最佳参数；Data algorithms are used to analyze and define the data model, and to mine the best parameters of the data model;

多维分析，用以进行数据拆解与数据分离，进行多维度数据分析，用以提高数据特征的捕捉；Multi-dimensional analysis, for data disassembly and data separation, for multi-dimensional data analysis, to improve the capture of data features;

自助分析，用以进行数据资源的整合，以及数据的处理与获取；Self-service analysis to integrate data resources and process and obtain data;

BI分析，用以进行获取数据、分析信息以及改进，并根据商业规划优化决策；BI analysis to obtain data, analyze information and improve, and optimize decision-making based on business planning;

所述云原生包括：The cloud native includes:

容器云，用以进行数据的打包、储存、管理与转运；Container cloud for data packaging, storage, management and transfer;

微治理服务，用以划分单一应用程度、协调配合服务，进行服务资源的整合、KPI数据的容量管理；Micro-governance services are used to divide a single application level, coordinate and cooperate services, integrate service resources, and manage KPI data capacity;

Dev Ops，用于促进开发(应用程序/软件工程)、技术运营和质量保障(QA)部门之间的沟通、协作与整合；Dev Ops to facilitate communication, collaboration and integration between development (application/software engineering), technical operations and quality assurance (QA) departments;

代码管理，用于进行代码工程化的管理，进行高效的开发、测试与部署；Code management, for code engineering management, efficient development, testing and deployment;

应用包管理，用以根据不同程序的运行提供最佳的可用匹配应用包；Application package management to provide the best available matching application package according to the operation of different programs;

应用部署，用以创建、部署、查看、更新和删除系统的应用，以及编辑和释放系统的部署环境。Application deployment, to create, deploy, view, update, and delete the system's applications, as well as edit and release the system's deployment environment.

所述数字孪生工厂包括：The digital twin factory includes:

设备维护孪生体，用以进行物理世界中设备维护的数据信息映射于数字世界中构建数字模型；The equipment maintenance twin is used to map the data information of equipment maintenance in the physical world to build a digital model in the digital world;

生产调度孪生体，用以进行物理世界中生产调度的数据信息映射于数字世界中构建数字模型；The production scheduling twin is used to map the data information of production scheduling in the physical world to build a digital model in the digital world;

结构检测孪生体，用以进行物理世界中结构检测的数据信息映射于数字世界中构建数字模型；Structural detection twin, which is used to map the data information of structure detection in the physical world to build a digital model in the digital world;

工艺参数孪生体，用以进行物理世界中工艺参数的数据信息映射于数字世界中构建数字模型；Process parameter twin, which is used to map the data information of process parameters in the physical world to build a digital model in the digital world;

所述数字孪生工厂还包括云平台、工业互联网与移动互联技术，用以支撑数字孪生映射关系的信息技术基础架构；设有的数据挖掘、数字网格、机器学习、大数据分析、模拟仿真与可视化操作，用以支撑数字孪生应用于分析、预测、决策环节。The digital twin factory also includes a cloud platform, industrial Internet and mobile internet technologies to support the information technology infrastructure of the digital twin mapping relationship; data mining, digital grid, machine learning, big data analysis, simulation and simulation are provided. Visual operations are used to support the application of digital twins in analysis, prediction, and decision-making.

所述工业应用包括：The industrial applications include:

设备自诊断，用以判别设备自身有无故障并确定故障部位，且进行故障类型的判断；Equipment self-diagnosis is used to determine whether the equipment itself has faults, determine the fault location, and judge the fault type;

数字化学习工厂，用以工厂、车间和生产线以及产品的设计到制造的转化，减低设计到生产制造之间的不确定性；The digital learning factory is used for the transformation of factories, workshops and production lines and product design to manufacturing, reducing the uncertainty between design and manufacturing;

集控系统，用以整合数据，进行数据监视、报警、分析、计算、使用，用以实现数据与管理一体化，进行数据归纳、分析和整理；The centralized control system is used to integrate data, perform data monitoring, alarm, analysis, calculation and use, to realize the integration of data and management, and to conduct data induction, analysis and sorting;

决策支持，用以提供分析问题、建立模型、模拟决策过程和方案的环境，以及调用各种信息资源和分析工具；Decision support, which provides an environment for analyzing problems, building models, simulating decision-making processes and solutions, and calling various information resources and analysis tools;

工艺协同优化，用以提供设计规则，协同设计与工艺的要求；Process collaborative optimization to provide design rules, collaborative design and process requirements;

物料配方优化，用以进行试验、优化、评价，选用原辅材料，并确定各种原辅材料的用量配比关系；Material formula optimization is used for testing, optimization, evaluation, selection of raw and auxiliary materials, and determination of the proportioning relationship of various raw and auxiliary materials;

生产过程运行控制，用以协同各个生产工序，进行生产全流程的产品质量、产量、消耗、成本综合生产指标的优化；The production process operation control is used to coordinate the various production processes to optimize the comprehensive production indicators of product quality, output, consumption and cost in the whole production process;

自组织运行与调度，用以进行组织、指挥、指导与协调。Self-organizing operation and scheduling for organization, command, guidance and coordination.

所述智能管控平台分为网络体系与安全体系，网络体系包括工业应用与数字孪生工厂，用以支撑系统的运行与状态；安全体系包括工业设备与边缘计算层，用以支撑系统的通信安全与生产安全；工业Paas层为网络体系与安全体系配合工作部分，用以联系网络体系与安全体系。The intelligent management and control platform is divided into a network system and a security system. The network system includes industrial applications and digital twin factories, which are used to support the operation and status of the system; the security system includes industrial equipment and edge computing layers, which are used to support the communication security and Production safety; the industrial Paas layer is the working part of the network system and the security system, which is used to connect the network system and the security system.

所述数字孪生工厂组成要素包括物理真实工厂、生产现场过程数据与数字工厂模型，物理真实工厂生产过程生产生产现场过程数据，数字工厂模型接收处理生产现场过程数据，数字工厂模型通过生产现场过程数据进行与物理真实工厂进行关联映射与匹配；基于云平台、物联网、移动互联与工业互联网，运用数据挖掘、数字网络、机器学习与大数据分析，进行生产现场过程数据的分析、预测与决策支持，进行对制造单元、生产进度、物流、质量的实时动态优化调整，进行生产过程的模拟仿真、可视化操作与虚拟现实计算。The components of the digital twin factory include a physical real factory, production site process data and a digital factory model, the physical real factory production process produces production site process data, the digital factory model receives and processes the production site process data, and the digital factory model passes the production site process data. Carry out correlation mapping and matching with physical real factories; based on cloud platform, Internet of Things, mobile Internet and industrial Internet, use data mining, digital network, machine learning and big data analysis to analyze, predict and support production site process data , carry out real-time dynamic optimization and adjustment of manufacturing unit, production schedule, logistics, and quality, and carry out simulation simulation, visual operation and virtual reality calculation of production process.

综上所述，本发明一种基于数字孪生工厂的智能管控平台能够有效进行虚实融合的数据处理、仿真分析、虚拟验证及生产过程运行决策，通过建立实体对象的数字模型，实时感知、诊断、预测物理实体对象的状态，调控物理实体对象的行为，改进利益相关方在物理实体对象生命周期内的决策，实现精准映射、虚实交互、智能干预。In summary, an intelligent management and control platform based on a digital twin factory of the present invention can effectively perform data processing, simulation analysis, virtual verification and production process operation decision-making of virtual-real integration. Predict the state of physical objects, regulate the behavior of physical objects, improve the decision-making of stakeholders in the life cycle of physical objects, and achieve accurate mapping, virtual-real interaction, and intelligent intervention.

在本说明书的描述中，参考术语“一个实施例”、“示例”、“具体示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。In the description of this specification, description with reference to the terms "one embodiment," "example," "specific example," etc. means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one aspect of the present invention. in one embodiment or example. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

以上公开的本发明优选实施例只是用于帮助阐述本发明。优选实施例并没有详尽叙述所有的细节，也不限制该发明仅为所述的具体实施方式。显然，根据本说明书的内容，可作很多的修改和变化。本说明书选取并具体描述这些实施例，是为了更好地解释本发明的原理和实际应用，从而使所属技术领域技术人员能很好地理解和利用本发明。本发明仅受权利要求书及其全部范围和等效物的限制。The above-disclosed preferred embodiments of the present invention are provided only to help illustrate the present invention. The preferred embodiments do not exhaust all the details, nor do they limit the invention to only the described embodiments. Obviously, many modifications and variations are possible in light of the contents of this specification. The present specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is to be limited only by the claims and their full scope and equivalents.

Claims (8)

Translated fromChinese

1.一种基于数字孪生工厂的智能管控平台，其特征在于，包括：1. an intelligent management and control platform based on digital twin factory, is characterized in that, comprises:工业设备，用以提供底层硬件支持与数据来源，完成工业生产过程并将生产过程中产生的数据提供给边缘计算层；Industrial equipment to provide underlying hardware support and data sources, complete the industrial production process and provide the data generated in the production process to the edge computing layer;边缘计算层，用以提供数据计算、数据存储与网络带宽，用于接收、处理来自工业设备的数据，包括数据实时表达式的计算、报警事件的生成、数据转换转储、实时数据推送与视频平台。The edge computing layer is used to provide data computing, data storage and network bandwidth for receiving and processing data from industrial equipment, including the calculation of real-time expressions of data, generation of alarm events, data conversion and dumping, real-time data push and video platform.工业PaaS层，用以提供操作服务平台、数据套件、数据分析组件与云原生架构，用以提高数据的挖掘与应用、发挥云计算平台优势，并将部署、处理后的数据提供给数字孪生工厂；The industrial PaaS layer is used to provide operational service platforms, data suites, data analysis components and cloud-native architecture to improve data mining and application, leverage the advantages of cloud computing platforms, and provide deployed and processed data to digital twin factories ;数字孪生工厂，包含多个数字孪生体，数字孪生体通过建立实体对象的数字模型，实时感知、诊断、预测物理实体对象的状态，调控物理实体对象的行为，改进利益相关方在物理实体对象生命周期内的决策；The digital twin factory includes multiple digital twins. The digital twin can perceive, diagnose, and predict the status of physical objects in real time by establishing digital models of physical objects, regulate the behavior of physical objects, and improve the life of stakeholders in physical objects. in-cycle decisions;工业应用，基于数字孪生技术，包括开发设备自诊断、自组织运行与调度、工艺协同优化与生产过程运行控制，用以实现生产过程精准预测与控制、生产自组织优化调度、设备全生命周期管理、产品质量追溯与管控。Industrial application, based on digital twin technology, including developing equipment self-diagnosis, self-organizing operation and scheduling, process collaborative optimization and production process operation control, to achieve accurate production process prediction and control, production self-organization optimization scheduling, and equipment life cycle management , Product quality traceability and control.2.如权利要求1所述的一种基于数字孪生工厂的智能管控平台，其特征在于，所述工业设备包括：2. A kind of intelligent management and control platform based on digital twin factory as claimed in claim 1, is characterized in that, described industrial equipment comprises:无人化装备，用以完成系统的全天候工作过程；Unmanned equipment to complete the all-weather working process of the system;智能装备，用以完成关键部件与高端装备的生产；Intelligent equipment to complete the production of key components and high-end equipment;工业监控，用以远程维护监测系统，组件生产过程的安全体系；Industrial monitoring for remote maintenance monitoring system, safety system of component production process;PLC/DCS/Scada，用以预设与载入控制指令，进行生产系统的控制；PLC/DCS/Scada, used to preset and load control instructions to control the production system;物流仓储，用以生产产物的存储与转运。Logistics warehousing is used for the storage and transshipment of production products.3.如权利要求1所述的一种基于数字孪生工厂的智能管控平台，其特征在于，所述边缘计算层包括：3. The intelligent management and control platform based on a digital twin factory according to claim 1, wherein the edge computing layer comprises:智能感知，用以识别设有的硬件设备接收的物理信号，映射到数字世界；IntelliSense, which is used to identify the physical signals received by the installed hardware devices and map them to the digital world;机理建模，用以接收智能感知得到的数据信息，根据物理的变化规律建立系统模型；Mechanism modeling, which is used to receive the data information obtained by intelligent perception, and establish a system model according to the changing laws of physics;精准控制，用以实现机理建模建立的系统模型高精度的工作与工作状态的改变；Precise control to achieve high-precision work and change of working state of the system model established by mechanism modeling;视觉分析，用以识别所建立的系统模型工作状况与收集工作过程中产生的数据信息；Visual analysis to identify the working status of the established system model and collect data information generated during the working process;数据分析，用以接收视觉分析下系统模型工作过程的数据信息，并进行转换计算与处理；Data analysis, which is used to receive the data information of the working process of the system model under the visual analysis, and perform conversion calculation and processing;数据挖掘，用以接收数据分析下的数据信息，挖掘经过数据分析后的数据信息，将数据信息转化为生产信息与生产特征数据；Data mining is used to receive data information under data analysis, mine the data information after data analysis, and convert the data information into production information and production characteristic data;过程优化，用以接收数据挖掘下的生产信息与生产特征数据，进行生产过程优化；Process optimization, which is used to receive production information and production characteristic data under data mining, and optimize the production process;边缘智能，用于运算生成回归模型预测经过过程优化的生产过程，进行生产过程优化的核验与运算；Edge intelligence is used to generate regression models to predict the optimized production process, and to verify and calculate the optimization of the production process;所述边缘计算层还包括：The edge computing layer also includes:实时表达式的计算，用以实时监测系统模型工作状态，进行系统模型工作过程产生数据的实时计算；The calculation of real-time expressions is used to monitor the working status of the system model in real time, and perform real-time calculation of the data generated during the working process of the system model;报警事件的生成，用以预设报警阈值，与实时表达式的计算数据信息实时对比，用以保障系统模型的安全工作与事故及时发现；The generation of alarm events is used to preset alarm thresholds and compare them in real time with the calculation data information of real-time expressions to ensure the safe work of the system model and the timely detection of accidents;数据转换转储，用于将系统模型工作过程产生的数据信息转换与DBA定期保存；Data conversion dump, which is used to convert the data information generated by the working process of the system model and save it regularly by the DBA;实时数据推送，用于将经过数据转换转储的数据信息实时推送；Real-time data push, which is used to push the data information dumped after data conversion in real time;视频平台，用于将实时数据推送的数据信息呈现于视频平台，用以进行系统模型工作的呈现；The video platform is used to present the data information pushed by the real-time data on the video platform for the presentation of the system model work;所述边缘计算层设有三组用于存储数据的数据库，所述三组数据库分别为内存数据库、关系数据库与时序数据库，所述边缘计算层设有视频通信服务，所述视频通信服务的通讯协议包括但不限于TCP/IP、MQTT、OPC、Mod bus、OPC-UA与104。The edge computing layer is provided with three groups of databases for storing data, the three groups of databases are memory database, relational database and time series database respectively, and the edge computing layer is provided with a video communication service, and the communication protocol of the video communication service is Including but not limited to TCP/IP, MQTT, OPC, Modbus, OPC-UA and 104.4.如权利要求1所述的一种基于数字孪生工厂的智能管控平台，其特征在于，所述工业PaaS层分为三部分，第一部分包括通用服务组件、人工智能、大数据套件(5S)，第二部分为数据分析组件，第三部分为云原生；4. A kind of intelligent management and control platform based on digital twin factory as claimed in claim 1, it is characterized in that, described industrial PaaS layer is divided into three parts, the first part comprises general service component, artificial intelligence, big data suite (5S) , the second part is the data analysis component, and the third part is cloud native;所述通用服务组件包括：The general service components include:企业网关，用以实现高层协议不同的网络互连，并对系统信息进行过滤与安全保障；The enterprise gateway is used to realize network interconnection of different high-level protocols, and to filter and secure system information;安全中心，用以保障系统安全，实现病毒检索、响应与处理；Security Center, to ensure system security and realize virus retrieval, response and processing;任务管理，用以安排系统工作任务，显示系统工作运行状态；Task management, which is used to arrange system work tasks and display system work operation status;消息服务，用以显示待处理的系统任务，进行所需处理任务提示；The message service is used to display the pending system tasks and prompt the required processing tasks;服务编排，用以部署完成系统运行的各个组件，进行服务配置；Service orchestration, which is used to deploy various components that complete the system operation and perform service configuration;流程服务，用以进行系统运行进程的优化，用以提高系统的处理效率；Process service, which is used to optimize the system running process to improve the processing efficiency of the system;所述大数据套件(5S)包括：The Big Data Suite (5S) includes:数存，用以进行数据统一管理与数据存储；Data storage for unified data management and data storage;数成，用以进行保障数据安全、数据加密、数据规划、数据开发、数据运行监控与元数据采集，进行数据的采集与分类；It is used to ensure data security, data encryption, data planning, data development, data operation monitoring and metadata collection, and to collect and classify data;数智，用以进行智能运算，进行数据预处理、算法建模、模型训练、模型部署与模型调优，用以提高数据的契合性；Data intelligence is used to perform intelligent operations, data preprocessing, algorithm modeling, model training, model deployment and model tuning to improve data fit;数现，用以数据可视化，进行数据报表、可视化报告与可视化视频；Data visualization for data visualization, data reporting, visual reporting and visual video;数典，用以进行数据标准管理与数据资产管理，进行数据的规范管理；Data dictionary, used for data standard management and data asset management, and standardized management of data;所述数据分析组件包括：The data analysis component includes:分布氏计算，用以数据分解、数据分配进行多核处理，用以节约整体计算时间、提高计算效率；Distributed computing, which is used for data decomposition and data allocation for multi-core processing to save overall computing time and improve computing efficiency;流式计算，用以进行大规模流动数据在不断变化的运动过程的实时分析；Streaming computing for real-time analysis of large-scale streaming data in changing motion processes;数据模型，用以进行数据特征的抽象化，进行描述系统的静态特征、动态行为和约束条件；The data model is used to abstract data features and describe the static features, dynamic behaviors and constraints of the system;数据算法，用以进行数据模型的分析与定义，进行挖掘数据模型的最佳参数；Data algorithms are used to analyze and define the data model, and to mine the best parameters of the data model;多维分析，用以进行数据拆解与数据分离，进行多维度数据分析，用以提高数据特征的捕捉；Multi-dimensional analysis, for data disassembly and data separation, for multi-dimensional data analysis, to improve the capture of data features;自助分析，用以进行数据资源的整合，以及数据的处理与获取；Self-service analysis to integrate data resources and process and obtain data;BI分析，用以进行获取数据、分析信息以及改进，并根据商业规划优化决策；BI analysis to obtain data, analyze information and improve, and optimize decision-making based on business planning;所述云原生包括：The cloud native includes:容器云，用以进行数据的打包、储存、管理与转运；Container cloud for data packaging, storage, management and transfer;微治理服务，用以划分单一应用程度、协调配合服务，进行服务资源的整合、KPI数据的容量管理；Micro-governance services are used to divide a single application level, coordinate and cooperate services, integrate service resources, and manage KPI data capacity;Dev Ops，用于促进开发(应用程序/软件工程)、技术运营和质量保障(QA)部门之间的沟通、协作与整合；Dev Ops to facilitate communication, collaboration and integration between development (application/software engineering), technical operations and quality assurance (QA) departments;代码管理，用于进行代码工程化的管理，进行高效的开发、测试与部署；Code management, for code engineering management, efficient development, testing and deployment;应用包管理，用以根据不同程序的运行提供最佳的可用匹配应用包；Application package management to provide the best available matching application package according to the operation of different programs;应用部署，用以创建、部署、查看、更新和删除系统的应用，以及编辑和释放系统的部署环境。Application deployment, to create, deploy, view, update, and delete the system's applications, as well as edit and release the system's deployment environment.5.如权利要求1所述的一种基于数字孪生工厂的智能管控平台，其特征在于，所述数字孪生工厂包括：5. A kind of intelligent management and control platform based on digital twin factory as claimed in claim 1, is characterized in that, described digital twin factory comprises:设备维护孪生体，用以进行物理世界中设备维护的数据信息映射于数字世界中构建数字模型；The equipment maintenance twin is used to map the data information of equipment maintenance in the physical world to build a digital model in the digital world;生产调度孪生体，用以进行物理世界中生产调度的数据信息映射于数字世界中构建数字模型；The production scheduling twin is used to map the data information of production scheduling in the physical world to build a digital model in the digital world;结构检测孪生体，用以进行物理世界中结构检测的数据信息映射于数字世界中构建数字模型；Structural detection twin, which is used to map the data information of structure detection in the physical world to build a digital model in the digital world;工艺参数孪生体，用以进行物理世界中工艺参数的数据信息映射于数字世界中构建数字模型；Process parameter twin, which is used to map the data information of process parameters in the physical world to build a digital model in the digital world;所述数字孪生工厂还包括云平台、工业互联网与移动互联技术，用以支撑数字孪生映射关系的信息技术基础架构；设有的数据挖掘、数字网格、机器学习、大数据分析、模拟仿真与可视化操作，用以支撑数字孪生应用于分析、预测、决策环节。The digital twin factory also includes a cloud platform, industrial Internet and mobile internet technologies to support the information technology infrastructure of the digital twin mapping relationship; data mining, digital grid, machine learning, big data analysis, simulation and simulation are provided. Visual operations are used to support the application of digital twins in analysis, prediction, and decision-making.6.如权利要求1所述的一种基于数字孪生工厂的智能管控平台，其特征在于，所述工业应用包括：6. The intelligent management and control platform based on a digital twin factory according to claim 1, wherein the industrial application comprises:设备自诊断，用以判别设备自身有无故障并确定故障部位，且进行故障类型的判断；Equipment self-diagnosis is used to determine whether the equipment itself has faults, determine the fault location, and judge the fault type;数字化学习工厂，用以工厂、车间和生产线以及产品的设计到制造的转化，减低设计到生产制造之间的不确定性；The digital learning factory is used for the transformation of factories, workshops and production lines and product design to manufacturing, reducing the uncertainty between design and manufacturing;集控系统，用以整合数据，进行数据监视、报警、分析、计算、使用，用以实现数据与管理一体化，进行数据归纳、分析和整理；The centralized control system is used to integrate data, perform data monitoring, alarm, analysis, calculation and use, to realize the integration of data and management, and to conduct data induction, analysis and sorting;决策支持，用以提供分析问题、建立模型、模拟决策过程和方案的环境，以及调用各种信息资源和分析工具；Decision support, which provides an environment for analyzing problems, building models, simulating decision-making processes and solutions, and calling various information resources and analysis tools;工艺协同优化，用以提供设计规则，协同设计与工艺的要求；Process collaborative optimization to provide design rules, collaborative design and process requirements;物料配方优化，用以进行试验、优化、评价，选用原辅材料，并确定各种原辅材料的用量配比关系；Material formula optimization is used for testing, optimization, evaluation, selection of raw and auxiliary materials, and determination of the proportioning relationship of various raw and auxiliary materials;生产过程运行控制，用以协同各个生产工序，进行生产全流程的产品质量、产量、消耗、成本综合生产指标的优化；The production process operation control is used to coordinate the various production processes to optimize the comprehensive production indicators of product quality, output, consumption and cost in the whole production process;自组织运行与调度，用以进行组织、指挥、指导与协调。Self-organizing operation and scheduling for organization, command, guidance and coordination.7.如权利要求1所述的一种基于数字孪生工厂的智能管控平台，其特征在于：所述智能管控平台分为网络体系与安全体系，网络体系包括工业应用与数字孪生工厂，用以支撑系统的运行与状态；安全体系包括工业设备与边缘计算层，用以支撑系统的通信安全与生产安全；工业Paas层为网络体系与安全体系配合工作部分，用以联系网络体系与安全体系。7. A kind of intelligent management and control platform based on digital twin factory as claimed in claim 1, it is characterized in that: described intelligent management and control platform is divided into network system and security system, network system comprises industrial application and digital twin factory, in order to support The operation and status of the system; the security system includes the industrial equipment and edge computing layers, which are used to support the communication security and production security of the system; the industrial Paas layer is the working part of the network system and the security system, which is used to connect the network system and the security system.8.如权利要求1所述的一种基于数字孪生工厂的智能管控平台，其特征在于：所述数字孪生工厂组成要素包括物理真实工厂、生产现场过程数据与数字工厂模型，物理真实工厂生产过程生产生产现场过程数据，数字工厂模型接收处理生产现场过程数据，数字工厂模型通过生产现场过程数据进行与物理真实工厂进行关联映射与匹配；基于云平台、物联网、移动互联与工业互联网，运用数据挖掘、数字网络、机器学习与大数据分析，进行生产现场过程数据的分析、预测与决策支持，进行对制造单元、生产进度、物流、质量的实时动态优化调整，进行生产过程的模拟仿真、可视化操作与虚拟现实计算。8. The intelligent management and control platform based on a digital twin factory as claimed in claim 1, wherein the components of the digital twin factory include a physical real factory, production site process data and a digital factory model, and the physical real factory production process The production site process data, the digital factory model receives and processes the production site process data, and the digital factory model associates, maps and matches with the physical real factory through the production site process data; based on cloud platform, Internet of Things, mobile Internet and industrial Internet, use data Mining, digital network, machine learning and big data analysis, analysis, prediction and decision support of production site process data, real-time dynamic optimization and adjustment of manufacturing unit, production schedule, logistics and quality, simulation and visualization of production process Operations and Virtual Reality Computing.

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