WO2024066683A1 - 工业互联网操作系统和产品的处理方法 - Google Patents

工业互联网操作系统和产品的处理方法 Download PDF

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Publication number
WO2024066683A1
WO2024066683A1 PCT/CN2023/108001 CN2023108001W WO2024066683A1 WO 2024066683 A1 WO2024066683 A1 WO 2024066683A1 CN 2023108001 W CN2023108001 W CN 2023108001W WO 2024066683 A1 WO2024066683 A1 WO 2024066683A1
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Prior art keywords
product
processed
data
production line
engine
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PCT/CN2023/108001
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English (en)
French (fr)
Inventor
王超
鲁效平
孙明
景大智
于晓义
王玉梅
高亚琼
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卡奥斯工业智能研究院(青岛)有限公司
卡奥斯物联科技股份有限公司
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Publication of WO2024066683A1 publication Critical patent/WO2024066683A1/zh

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32252—Scheduling production, machining, job shop

Definitions

  • the embodiments of the present application belong to the field of industrial Internet technology, and specifically relate to an industrial Internet operating system and product processing method.
  • the production transition and industrial upgrading of discrete manufacturing industry are closely dependent on the development of information technology, and the key core is the industrial Internet operating system.
  • the industrial Internet operating system Through the industrial Internet operating system, the production process or related industrial products of discrete manufacturing industry can be empowered, and the intelligent brain can be added to industrial products and production systems, effectively improving the intelligence of products and production processes, meeting the requirements of discrete manufacturing industry for high efficiency, reliability, real-time, environmental protection and many other aspects, and finally realizing the automation and intelligence of products or processes.
  • the embodiment of the present application provides a processing method for an industrial Internet operating system and product.
  • an embodiment of the present application provides an industrial Internet operating system, including:
  • Heterogeneous data integration engine digital twin model engine, and dynamic multi-task scheduling engine
  • the heterogeneous data integration engine processes the initial production data of the product to be processed according to a preset data format to generate target production data
  • the digital twin model engine obtains the production scheduling information of the product to be processed based on the flexible production line data used to produce the product to be processed and the historical production scheduling information of other products of the same category as the product to be processed; and can also monitor the status of the flexible production line when the flexible production line produces the product to be processed based on the product information of the product to be processed, the production scheduling information and the target production data, and obtain the status data of the flexible production line;
  • the dynamic multi-task scheduling engine constructs a new flexible production line to enable continued production of the product to be processed on the new flexible production line.
  • system further includes:
  • the industrial big data knowledge engine obtains historical product information of the other products
  • the industrial edge intelligent CPS management shell determines the product information of the product to be processed based on the historical product information and the user's functional requirements and/or appearance requirements for the product to be processed.
  • system further includes:
  • a scheduling algorithm library stores scheduling algorithms, and the scheduling algorithms are used to implement the scheduling function of the dynamic multi-task scheduling engine.
  • an embodiment of the present application provides a product processing method, which is applied to a server in the industrial Internet operating system of the first aspect, and the method includes:
  • the production scheduling information of the product to be processed is obtained through the digital twin model engine
  • the initial production data of the product to be processed is processed according to a preset data format through a heterogeneous data integration engine to generate target production data;
  • the state of the flexible production line is monitored by the digital twin model engine to obtain the state data of the flexible production line;
  • a new flexible production line is constructed through a dynamic multi-task scheduling engine to enable continued production of the product to be processed on the new flexible production line.
  • the step of determining product information of the product to be processed includes:
  • the product information of the product to be processed is determined through the industrial edge intelligent information-physical system CPS management shell, and the demand information is used to represent the user's functional requirements and/or appearance requirements for the product to be processed.
  • the state of the flexible production line is monitored by the digital twin model engine according to the target production data to obtain the state data of the flexible production line, including:
  • the state of the flexible production line is monitored by the digital twin model engine to obtain the state monitoring data of the flexible production line;
  • the status of the flexible production line in the remaining production time is predicted by the industrial big data knowledge engine to obtain the status data.
  • the method before obtaining the production scheduling information of the product to be processed through the digital twin model engine based on the flexible production line data used to produce the product to be processed and the historical production scheduling information of other products of the same category as the product to be processed, the method further includes:
  • the flexible production line is constructed through the industrial edge intelligent CPS management shell to connect the business flow and data flow throughout the entire life cycle of the product.
  • the method when the flexible production line produces the product to be processed according to the product information and the production scheduling information, the method further includes:
  • the initial production data of the product to be processed is collected through the industrial edge intelligent CPS management shell.
  • a new flexible production line is constructed through a dynamic multi-task scheduling engine, including:
  • the new flexible production line is constructed.
  • judging whether there is a problem with the flexible production line based on the scheduling algorithm and the status data includes:
  • an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, and computer program instructions stored in the memory and executable on the processor, wherein the processor is used to implement the methods described in the second aspect and each technical solution when executing the computer program instructions.
  • an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the methods described in the above-mentioned second aspect and each technical solution.
  • an embodiment of the present invention provides a chip, comprising a memory and a processor, wherein the memory stores code and data, the memory is coupled to the processor, and the processor runs the program in the memory so that the chip is used to execute the methods described in the above-mentioned second aspect and each technical solution.
  • an embodiment of the present invention provides a program product, comprising: a computer program, when the program product is run on a computer, the computer executes the method described in the second aspect and each technical solution.
  • an embodiment of the present invention provides a computer program, which, when executed by a processor, is used to execute the method described in the above-mentioned second aspect and each technical solution.
  • the industrial Internet operating system and product processing method include a heterogeneous data integration engine, a digital twin model engine, and a dynamic multi-task scheduling engine.
  • the heterogeneous data integration engine processes the initial production data of the product to be processed according to a preset data format to generate target production data.
  • the digital twin model engine obtains the production scheduling information of the product to be processed based on the flexible production line data used to produce the product to be processed and the historical production scheduling information of other products of the same type as the product to be processed.
  • the digital twin model engine can also monitor the status of the flexible production line and obtain the status data of the flexible production line.
  • the dynamic multi-task scheduling engine constructs a new flexible production line to enable continued production of the product to be processed on the new flexible production line.
  • the heterogeneous data integration engine can define and express the data of the products to be processed in a unified format, thereby realizing the integration of heterogeneous data.
  • the digital twin model engine and the dynamic multi-task scheduling engine can monitor the flexible production line so that when there is a problem with the flexible production line, a new flexible production line can be built in time.
  • the flexible production line is used to continue the production of the products to be processed, thereby realizing the optimal scheduling of tasks and the coordination of resources.
  • FIG1 is a schematic diagram of the structure of an industrial Internet operating system provided in an embodiment of the present application.
  • FIG2 is another schematic diagram of the structure of the industrial Internet operating system provided in an embodiment of the present application.
  • FIG3 is a flow chart of a first embodiment of a method for processing a product provided in an embodiment of the present application.
  • connection should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components.
  • connection should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components.
  • the industrial Internet operating system of the existing technology mainly includes a platform layer and an application layer.
  • the platform layer includes artificial intelligence, big data, and cloud computing.
  • the application layer includes the user end and the developer end.
  • the platform layer and the application layer are protected by a security protection system.
  • the data information in the security protection system is stored in a database.
  • the security protection system includes data protection and physical protection.
  • Data protection includes a data screening module, a hazard judgment module, a hazard preprocessing module, a manual warning module, and an abnormal hazard processing module.
  • Physical protection includes a visual recognition module and a voice recognition module.
  • the present application provides an industrial Internet operating system, which includes a heterogeneous data integration engine, a digital twin model engine, and a dynamic multi-task scheduling engine.
  • the heterogeneous data integration engine can define and express the data of the products to be processed in a unified format to achieve the integration of heterogeneous data.
  • the digital twin model engine can monitor the status of the flexible production line when the flexible production line produces the products to be processed.
  • the dynamic multi-task scheduling engine can build a new flexible production line when the digital twin model engine monitors and finds problems in the flexible production line, so as to achieve continued production of the products to be processed on the new flexible production line, thereby achieving optimal scheduling of tasks and coordination of resources.
  • Fig. 1 is a schematic diagram of the structure of an industrial Internet operating system provided in an embodiment of the present application.
  • the industrial Internet operating system may include: a heterogeneous data integration engine, a digital twin model engine, and a dynamic multi-task scheduling engine.
  • the heterogeneous data integration engine processes the initial production data of the product to be processed according to the preset data format to generate target production data.
  • the above processing may be: automatically formulating a conversion rule according to the above initial production data, converting the format of the above initial production data into a preset data format through the conversion rule, thereby generating target production data.
  • the heterogeneous data integration engine can also build a flexible production line in the design, production, service and other business processes of the products to be processed in the discrete manufacturing industry, based on the business logic in the product information of the products to be processed and the functions of each physical discrete manufacturing equipment, so as to connect the business flow and data flow of the products to be processed throughout their entire life cycle.
  • the product information is determined based on the user's functional requirements and/or appearance requirements for the product to be processed.
  • the heterogeneous data integration engine can also build a virtual flexible production line based on the above business logic through relevant models to achieve model semantic consistency conversion.
  • the above-mentioned related models can be stored in a model resource library, and the heterogeneous data integration engine can call the above-mentioned related models through the industrial edge intelligent cyber-physical system (Cyber-Physical Systems, CPS) management shell.
  • CPS industrial edge intelligent cyber-physical system
  • the digital twin model engine obtains the production scheduling information of the product to be processed based on the flexible production line data used to produce the product to be processed and the historical production scheduling information of other products of the same category as the product to be processed.
  • the digital twin model engine can also monitor the status of the flexible production line and obtain the status data of the flexible production line when the flexible production line produces the product to be processed based on the product information, production scheduling information and target production data of the product to be processed.
  • the digital twin model engine can also build a full-process digital twin model of the product to be processed based on the virtual flexible production line built by the above-mentioned heterogeneous data integration engine and the historical production scheduling information of other products of the same type as the product to be processed, as well as the digital twin mechanism model in the scenario-based mechanism model library, and realize virtual-reality mapping through the industrial edge intelligent CPS management shell.
  • the digital twin model engine can use the above-mentioned full-process digital twin model to predict the production planning, working conditions, equipment status, etc. of the products to be processed, thereby determining the production scheduling plan and monitoring the status of the flexible production line.
  • the dynamic multi-task scheduling engine can build a new flexible production line when the status data indicates that there is a problem with the flexible production line, so as to continue to produce the products to be processed on the new flexible production line.
  • the dynamic multi-task scheduling engine can schedule production resources that meet production needs.
  • An embodiment of the present application provides an industrial Internet operating system, which includes a heterogeneous data integration engine, a digital twin model engine, and a dynamic multi-task scheduling engine.
  • the heterogeneous data integration engine processes the initial production data of the product to be processed according to a preset data format to generate target production data.
  • the digital twin model engine obtains the production scheduling information of the product to be processed based on the flexible production line data used to produce the product to be processed and the historical production scheduling information of other products of the same type as the product to be processed.
  • the digital twin model engine can also monitor the status of the flexible production line and obtain the status data of the flexible production line based on the product information, production scheduling information and target production data of the product to be processed.
  • the dynamic multi-task scheduling engine constructs a new flexible production line to enable continued production of the product to be processed on the new flexible production line.
  • the heterogeneous data integration engine can define and express the data of the products to be processed in a unified format, thereby realizing the integration of heterogeneous data.
  • the digital twin model engine and the dynamic multi-task scheduling engine can monitor the flexible production line so that when there is a problem with the flexible production line, a new flexible production line can be built in time.
  • the flexible production line is used to continue the production of the products to be processed, thereby realizing the optimal scheduling of tasks and the coordination of resources.
  • the industrial Internet operating system may also include: an industrial big data knowledge engine and an industrial edge intelligent CPS management shell.
  • the industrial big data knowledge engine obtains historical product information of other products.
  • the industrial big data knowledge engine can also obtain historical production scheduling information of other products of the same category as the product to be processed.
  • the industrial big data knowledge engine can also obtain the status monitoring data of the flexible production line by monitoring the status of the flexible production line through the digital twin model engine, predict the status of the flexible production line in the remaining production time, and obtain status data.
  • the industrial big data knowledge engine can also store relevant data processed by the heterogeneous data integration engine, wherein the relevant data can be target production data, as well as relevant data on product design, production, service processes, etc.
  • the industrial big data knowledge engine may include the following modules:
  • Target domain data enhancement knowledge representation module target domain self-supervised learning knowledge representation module, and cross-domain deep transfer learning module based on multi-task meta-learning.
  • the target domain data enhancement knowledge representation module can use signal domain conversion, adversarial learning, automatic data augmentation and other technologies to perform data enhancement to address problems such as insufficient and low-quality historical industrial data samples of the products to be processed, generate enhanced samples that integrate cross-domain knowledge, and use them in the transfer learning model of cross-domain samples, which facilitates the classification (fault diagnosis, anomaly detection) and prediction (life prediction, inventory prediction) problems of complex industrial scenarios.
  • the target domain self-supervised learning knowledge representation module can perform self-supervised knowledge representation on the industrial heterogeneous data collected in real time during the product design and manufacturing process, combine the industrial multi-source data representations in the time domain and space domain, and use supervised learning to obtain deep decoupling of multi-source heterogeneous data with invariance, equivariance, and mobility to adapt to dynamically changing complex industrial tasks.
  • the cross-domain deep transfer learning module based on multi-task meta-learning can perform single-task and multi-task migration and multiple auxiliary task selection mechanism design based on the transfer learning mechanism of the meta-learning mechanism according to the product to be processed and the relevant historical data of the product to be processed, so as to facilitate classification and accurate prediction in complex industrial scenarios, and ultimately realize the deep cross-domain knowledge transfer of new tasks.
  • the industrial edge intelligent CPS management shell determines the product information of the products to be processed based on the historical product information and the demand information of the products to be processed.
  • the above-mentioned demand information may be the user's demand for the product to be processed, including functional requirements and/or appearance requirements.
  • the industrial edge intelligent CPS management shell can also collect initial production data of the product to be processed.
  • the above-mentioned collection and processing can be: accessing physical discrete manufacturing heterogeneous resources and collecting all-round data of discrete manufacturing equipment in real time.
  • the intelligent CPS management shell through the intelligent CPS management shell, the discrete manufacturing physical equipment, products and service resources are semantically understood, operated and scheduled, and the intelligent perception network is used to realize the automatic perception of resources and the intelligent adaptive matching of heterogeneous equipment protocols.
  • the model is automatically labeled, identified, classified, retrieved, and compiled and optimized to achieve adaptive mapping between entities and models.
  • the product information of the product to be processed is determined through the intelligent CPS management shell and the industrial big data knowledge engine, so as to obtain a design solution suitable for the user, which lays the foundation for subsequent product processing and improves the accuracy of processing.
  • the industrial Internet operating system may further include:
  • the scheduling algorithm library stores scheduling algorithms, and the scheduling algorithms are used to implement the scheduling function of the dynamic multi-task scheduling engine.
  • the scheduling algorithm library can store scheduling algorithms for easy calling by the dynamic multi-task scheduling engine.
  • the dynamic multi-task scheduling engine calls the scheduling algorithm, the flexible production line can be optimized and designed to build a new flexible production line suitable for the business objectives, thereby achieving efficient and accurate scheduling under multi-task.
  • FIG2 is another schematic diagram of the structure of the industrial Internet operating system provided in an embodiment of the present application. As shown in FIG2, the industrial Internet operating system includes:
  • Discrete industry applications industrial application mobile software (application, APP), core components, basic common components, discrete manufacturing resources, safety protection system and standard identification system.
  • discrete industry applications include user interaction, R&D innovation, precision sales, collaborative procurement, intelligent manufacturing, smart logistics, and intelligent services, as well as other full life cycle applications of discrete manufacturing products.
  • industrial application APPs include interactive customization industrial APPs, development and design industrial APPs, precision sales industrial APPs, modular procurement industrial APPs, intelligent manufacturing industrial APPs, intelligent logistics industrial APPs, and intelligent service industrial APPs, so as to realize personalized customization, networked collaboration, intelligent production and service extension.
  • the core components include the industrial cloud layer and the industrial edge layer.
  • the industrial cloud layer includes low-code rapid construction tools for industrial Internet applications, scenario-based mechanism model library, industrial engine, scheduling algorithm library and big data lake.
  • the industrial edge layer includes model resource library, industrial edge intelligent CPS management shell and interface protocol library.
  • the Industrial Internet Application Low-Code Rapid Construction Tool is based on other core component libraries, targeting the personalized customization and networked collaborative application needs of discrete industries, integrating core components such as industrial edge intelligence, model resource library, interface protocol library, big data lake, industrial engine, scenario-based mechanism model library, etc., to build an Industrial Internet Application Code Rapid Development Tool. It covers the model resource library and compiler of cloud-native applications such as software user interface (UI), services, controls, entities, processes, rules, etc., and realizes the rapid construction of model-driven cloud-native applications based on graphical low-code rapid development technology, reduces the difficulty of building industrial APPs, and provides underlying architecture support for the developer community and the formation of a large-scale customized industrial chain ecosystem.
  • UI software user interface
  • the scenario-based mechanism model library stores digital twin mechanism models for different application scenarios of discrete manufacturing, which facilitates scheduling applications.
  • the industrial engine includes a digital twin model engine and a dynamic multi-task scheduling engine.
  • the big data lake includes heterogeneous data integration engine, big data lake governance tool and industrial big data knowledge engine.
  • the industrial edge layer includes the model resource library, the industrial edge intelligent CPS management shell and the interface protocol library.
  • the model resource library extracts a subset of common information model elements from the classification structure of production equipment, products and services to establish a unified multi-dimensional semantic ontology model of all factors of various equipment, product and service resources.
  • the model resource library establishes associations between various resource models through semantic analysis technology, further instantiates the associations, and provides a retrieval basis for the full-factor resource perception and adaptive intelligent matching of the industrial edge intelligent CPS management shell.
  • the interface protocol library provides corresponding interface protocols for discrete manufacturing equipment, product and service resource semantic models to facilitate intelligent perception of all-factor resources within the factory network.
  • the core components formulate a field-oriented component service assembly mechanism, adopt standardized industrial microservice technology, and unify the interface standards and gateways between the logical execution modules of the industrial Internet operating system components, and form a core component library based on microservices and container technologies through component assembly and orchestration models.
  • basic common components include basic component libraries such as container services, load balancing, cloud storage, and content delivery networks (Content Delivery Network, CDN) and other components; development component libraries such as the general term for processes, methods and systems (a combination of Development and Operations, DevOps), microservice governance, function services, open application programming interfaces (open Application Programming Interface, OpenAPI), etc.; middleware components such as databases, message queues, caches, and search engines; operation and maintenance component libraries such as monitoring and early warning, log services, cloud backup, and off-site disaster recovery, etc., and also include servers, storage devices, network devices, security devices, network bandwidth, encryption machines and other related components.
  • development component libraries such as the general term for processes, methods and systems (a combination of Development and Operations, DevOps), microservice governance, function services, open application programming interfaces (open Application Programming Interface, OpenAPI), etc.
  • middleware components such as databases, message queues, caches, and search engines
  • operation and maintenance component libraries such as monitoring and early warning, log services, cloud backup
  • discrete manufacturing resources include CNC bed, industrial robots, transport vehicles (Automated Guided Vehicle, AGV), sensors, industrial switches, cameras, augmented reality (Augmented Reality, AR) glasses, testing equipment and other resources.
  • a unified semantic model of all-factor resources of discrete manufacturing equipment, products and services is constructed, and a model resource library is constructed.
  • Equipment is divided into industrial robots, CNC machine tools, AGVs, detection equipment, etc. according to basic types, products are divided into terminal intelligent consumer products, intelligent electromechanical products, etc., and services are divided into R&D design services, production and manufacturing services, business management services, after-sales operation and maintenance services, etc. according to the whole life cycle.
  • the embodiments of the present application propose a unified semantic and extensible model of all-factor resources of discrete manufacturing equipment, products and services based on multi-dimensional semantic modeling, establish an intelligent CPS management shell for adaptive adaptation of industrial heterogeneous protocols, and break through the bottleneck of the difficulty in unified standardized modeling and adaptive access of discrete manufacturing resources.
  • the embodiments of the present application can assist enterprises in establishing a unified business operation platform based on existing factor resources.
  • the functions and data of all software and equipment are scheduled through this unified platform, which will break through the core bottleneck problems of the industrial Internet operating system, form new systems, new components, and new applications, realize continuous improvement and iterative optimization of factories, and enable high-quality development of the discrete manufacturing industry.
  • the embodiments of this application focus on the three bottleneck challenges of "incomplete connection” of industrial Internet factor resources, "insufficient integration” of heterogeneous data, and “inaccurate control” of collaborative scheduling.
  • the edge intelligence, big data space, deep transfer learning, digital twin, reinforcement learning scheduling decision-making and other technologies are integrated and innovated with the characteristics of discrete industry industrial Internet, breaking through the three key problems of self-adaptation of semantic modeling of all factor resources, deep cross-domain knowledge migration of strong heterogeneous data, and precise control of high dynamic task uncertainty, forming intelligent, standardized, and autonomous core technology components, and creating a discrete industry industrial Internet operating system with comprehensive intelligent connection, deep intelligent integration, and precise intelligent control.
  • the present invention has important scientific value and application value for the breakthrough and development of new theories and technologies in the frontier field of industrial Internet, especially discrete industry industrial Internet operating system.
  • the industrial Internet operating system will become the core common support platform for industrial Internet industry applications, and will provide core technology and system support for the transformation and upgrading of discrete manufacturing enterprises, cost-effectiveness and profitability.
  • FIG3 is a flow chart of a first embodiment of a method for processing a product provided in an embodiment of the present application. As shown in FIG3 , the method for processing a product is applied to a server in an industrial Internet operating system described in any of the above embodiments, and the method for processing a product may include the following steps:
  • S301 Determine product information of the product to be processed according to the user's functional requirements and/or appearance requirements of the product to be processed.
  • the server needs to obtain the customer's personalized needs for the product to be processed, and then determine the product information of the product to be processed according to the personalized needs.
  • the user may input personalized requirements through the front-end device of the industrial Internet operating system, and the above-mentioned server responds to the user's input operation to obtain the personalized requirements of the product to be processed input by the user.
  • the above-mentioned input operation can be a voice input operation, a text input operation, and a click input operation of a related control, etc., which can be determined according to actual conditions.
  • the embodiment of the present application does not limit the specific input operation method.
  • the product to be processed will enter the production and manufacturing stage.
  • a large number of products to be processed put forward higher requirements for flexible production lines and collaborative optimization, so it is also necessary to determine the production schedule of the products to be processed to ensure the efficient production of the products to be processed.
  • the full-process digital twin model in the digital twin model engine can be used to predict the production plan, operating conditions, equipment status, etc. of the products to be processed based on the flexible production line data and historical production scheduling information, thereby determining the production scheduling plan.
  • the above-mentioned flexible production line data may be all-round data of the flexible production line.
  • the above-mentioned flexible production line data can be obtained in advance through the industrial edge intelligent CPS management shell, and the above-mentioned historical production scheduling information can be mined and migrated from historical production scheduling cases of other equipment through the industrial big data knowledge engine.
  • the initial production data may be production data of each discrete manufacturing device in the flexible production line.
  • the above processing may be: automatically formulating conversion rules according to the above initial production data through a heterogeneous data integration engine, converting the format of the above initial production data into a preset data format through the conversion rules, thereby generating target production data.
  • the initial production data of the above-mentioned products to be processed can be collected through the industrial edge intelligent CPS management shell.
  • the state of the flexible production line is monitored by the digital twin model engine to obtain the state data of the flexible production line.
  • condition monitoring includes monitoring of working conditions and/or equipment.
  • a new flexible production line is constructed through a dynamic multi-task scheduling engine to enable continued production of the product to be processed on the new flexible production line.
  • An embodiment of the present application provides a product processing method, which determines product information of the product to be processed according to the user's functional requirements and/or appearance requirements for the product to be processed, obtains the production scheduling information of the product to be processed through a digital twin model engine based on the flexible production line data used to produce the product to be processed and the historical production scheduling information of other products of the same category as the product to be processed, and when the product to be processed is produced on the flexible production line according to the product information and the production scheduling information, the initial production data of the product to be processed is processed according to a preset data format through a heterogeneous data integration engine to generate target production data, and monitors the status of the flexible production line through the digital twin model engine based on the target production data to obtain the status data of the flexible production line, and when the status data indicates that there is a problem with the flexible production line, constructs a new flexible production line through a dynamic multi-task scheduling engine to enable continued production of the product to be processed on the new flexible production line.
  • This technical solution can be applied to application scenarios such as flexible planning and scheduling, flexible production line reconstruction, working condition monitoring and prediction, dynamic coordination scheduling, etc.
  • the data of the products to be processed can be defined and expressed in a unified format through the heterogeneous data integration engine.
  • a new flexible production line can be built through the digital twin model engine and the dynamic multi-task scheduling engine to enable continued production of the products to be processed on the new flexible production line, thereby achieving optimal scheduling of tasks and coordination of resources.
  • S301 may be implemented by the following steps:
  • the industrial big data knowledge engine Through the industrial big data knowledge engine, historical product information of other products is obtained. Based on the historical product information and the user's demand for the products to be processed, the product information of the products to be processed is determined through the industrial edge CPS management shell.
  • the industrial big data knowledge engine is called to provide knowledge support, and then the industrial edge CPS management shell is called according to the user's demand for the product to be processed to optimize and improve the historical product information, thereby determining the product information of the product to be processed, and obtaining the design plan for the product to be processed, thereby improving the matching degree between the produced product to be processed and the user's demand.
  • S304 may be implemented by the following steps:
  • the state of the flexible production line is monitored through the digital twin model engine to obtain the state monitoring data of the flexible production line.
  • the state of the flexible production line in the remaining production time is predicted through the industrial big data knowledge engine to obtain the state data.
  • the digital twin model engine can be called during the production execution process to monitor the real-time status of the flexible production line, and the industrial big data knowledge engine can be called to predict abnormal working conditions, equipment failures, timing trends, etc., and obtain status data, which can effectively prevent The flexible production line encounters anomalies during the remaining production time, affecting the production of the products to be processed.
  • the product processing method may further include the following steps:
  • a flexible production line is built through a heterogeneous data integration engine to connect the business flow and data flow throughout the entire life cycle of the product.
  • the heterogeneous data integration engine constructs a flexible production line based on the business logic in the product information and the functions of each physical discrete manufacturing equipment, laying the foundation for the subsequent production of the products to be processed on the flexible production line and improving production efficiency.
  • the product processing method may further include the following steps:
  • the initial production data of the products to be processed is collected through the industrial edge intelligent CPS management shell.
  • the industrial edge intelligent CPS management shell can access physical discrete manufacturing heterogeneous resources, and collect production data of each discrete manufacturing equipment in the flexible production line in real time, thereby obtaining the initial production data of the product to be processed, so that the flexible production line can be monitored subsequently to ensure the smooth production of the product to be processed.
  • S305 may be implemented by the following steps:
  • the scheduling algorithm is called from the scheduling algorithm library. Based on the scheduling algorithm, it is determined according to the status data whether there is a problem with the flexible production line. When it is determined that there is a problem with the flexible production line, a new flexible production line is constructed.
  • the scheduling algorithm library stores scheduling algorithms
  • the dynamic multi-task scheduling engine can call corresponding scheduling algorithms to schedule resources according to different scenarios, thereby solving the problem of inaccurate collaborative scheduling control in the prior art.
  • the above scheduling algorithm is based on judging whether there is a problem with the flexible production line according to the status data, which can be achieved through the following steps.
  • the working condition and equipment are two major factors affecting the production of the product to be processed, it is possible to determine whether the flexible production line has abnormal working conditions and equipment through the status data. If there is a fault or equipment failure, it can be determined based on the judgment result whether the flexible production line affects the production process of the product to be processed, so that subsequent scheduling and processing can be carried out in a timely manner in the event of an impact.
  • This embodiment also provides an electronic device, including:
  • processor memory, and,interface
  • a memory used to store executable instructions of the processor, and the memory may also be a flash memory;
  • the processor is configured to execute the various steps in the above method by executing the executable instructions. For details, please refer to the relevant description in the above method embodiment.
  • the memory can be independent or integrated with the processor.
  • the electronic device may further include:
  • a bus is used to connect the processor, the memory and an interface.
  • the interface includes a communication interface for data transmission and a display interface or an operation interface for human-computer interaction.
  • This embodiment further provides a readable storage medium, in which a computer program is stored.
  • a computer program is stored.
  • the electronic device executes the methods provided in the above-mentioned various implementation modes.
  • This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.
  • This embodiment also provides a chip, which includes a memory and a processor.
  • the memory stores code and data.
  • the memory is coupled to the processor.
  • the processor runs the program in the memory so that the chip is used to execute the methods provided in the above various embodiments.
  • This embodiment also provides a computer program, which, when executed by a processor, is used to execute the methods provided in the aforementioned various implementation modes.

Abstract

一种工业互联网操作系统和产品的处理方法，工业互联网操作系统包括异构数据集成引擎、数字孪生模型引擎和动态多任务调度引擎，异构数据集成引擎将待处理产品数据进行统一格式的定义与表达，实现异构数据的融通，数字孪生模型引擎在柔性生产线对待处理产品进行生产时，对生产产品的柔性生产线进行状态监控，同时，动态多任务调度引擎在数字孪生模型引擎监控发现柔性生产线存在问题时，构建用于继续生产待处理产品的新的柔性生产线，从而实现任务的优化调度以及资源的协同。

Description

工业互联网操作系统和产品的处理方法
本申请要求于2022年09月29日提交中国专利局、申请号为2022112020937、申请名称为“工业互联网操作系统和产品的处理方法”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请实施例属于工业互联网技术领域，具体涉及一种工业互联网操作系统和产品的处理方法。
背景技术
随着新兴技术的发展，离散制造业行业领先企业和智能制造试点示范企业正加快向智能化水平迈进，积极开展智能化布局。
离散制造业的生产跃迁和产业升级紧紧依赖于信息技术的发展，而这其中的关键核心就是工业互联网操作系统。通过工业互联网操作系统赋能离散制造业生产流程或相关工业产品，给工业产品和生产系统增加智慧的大脑，有效提高产品及生产过程的智能化，满足离散制造业工业生产对高效、可靠、实时、环保等诸多方面的要求，最终实现产品或流程的自动化与智能化。
然而，现有的工业互联网操作系统尚处于发展初期，数据挖掘分析应用能力不足，无法实现异构数据的融通、任务的优化调度以及资源的协同。
发明内容
为了解决现有技术中的上述问题，即为了解决现有技术中工业互联网操作系统数据挖掘分析应用能力不足，无法实现异构数据的融通、任务的优化调度以及资源的协同的问题，本申请实施例提供了一种工业互联网操作系统和产品的处理方法。
第一方面，本申请实施例提供一种工业互联网操作系统，包括：
异构数据集成引擎、数字孪生模型引擎以及动态多任务调度引擎；
所述异构数据集成引擎将待处理产品的初始生产数据根据预设数据格式进行处理，生成目标生产数据；
所述数字孪生模型引擎根据用于生产所述待处理产品的柔性生产线数据，以及所述待处理产品所属种类的其他产品的历史排产信息，获取所述待处理产品的排产信息；还可以根据所述待处理产品的产品信息、所述排产信息以及所述目标生产数据，在柔性生产线对所述待处理产品进行生产时，对所述柔性生产线进行状态监控，获取所述柔性生产线的状态数据；
所述动态多任务调度引擎在所述状态数据指示所述柔性生产线存在问题时，构建新的柔性生产线，以实现在所述新的柔性生产线对所述待处理产品进行继续生产。
在上述工业互联网操作系统的优选技术方案中，所述系统还包括：
工业大数据知识引擎和工业边缘智能CPS管理壳；
所述工业大数据知识引擎获取所述其他产品的历史产品信息；
所述工业边缘智能CPS管理壳根据所述历史产品信息以及用户对所述待处理产品的功能需求和/或外观需求，确定所述待处理产品的所述产品信息。
在上述工业互联网操作系统的优选技术方案中，所述系统还包括：
调度算法库，所述调度算法库存储有调度算法，所述调度算法用于实现动态多任务调度引擎的调度功能。
第二方面，本申请实施例提供一种产品的处理方法，应用于第一方面的工业互联网操作系统中的服务器，所述方法包括：
根据用户对所述待处理产品的功能需求和/或外观需求，确定待处理产品的产品信息；
根据用于生产所述待处理产品的柔性生产线数据，以及所述待处理产品所属种类的其他产品的历史排产信息，通过数字孪生模型引擎获取所述待处理产品的排产信息；
当根据所述产品信息以及所述排产信息，在柔性生产线对所述待处理产品进行生产时，通过异构数据集成引擎将所述待处理产品的初始生产数据根据预设数据格式进行处理，生成目标生产数据；
根据所述目标生产数据，通过所述数字孪生模型引擎对所述柔性生产线进行状态监控，获取所述柔性生产线的状态数据；
当所述状态数据指示所述柔性生产线存在问题时，通过动态多任务调度引擎构建新的柔性生产线，以实现在所述新的柔性生产线对所述待处理产品进行继续生产。
在上述产品的处理方法的优选技术方案中，所述确定待处理产品的产品信息，包括：
通过工业大数据知识引擎，获取所述其他产品的历史产品信息；
根据所述历史产品信息以及所述待处理产品的需求信息，通过工业边缘智能信息物理系统CPS管理壳，确定所述待处理产品的所述产品信息，所述需求信息用于表示用户对所述待处理产品的功能需求和/或外观需求。
在上述产品的处理方法的优选技术方案中，所述根据所述目标生产数据，通过所述数字孪生模型引擎对所述柔性生产线进行状态监控，获取所述柔性生产线的状态数据，包括：
根据所述目标生产数据，通过所述数字孪生模型引擎对所述柔性生产线进行状态监控，获取所述柔性生产线的状态监控数据；
根据所述状态监控数据，通过所述工业大数据知识引擎对所述柔性生产线在剩下的生产时长中的状态进行预测，获取所述状态数据。
在上述产品的处理方法的优选技术方案中，在所述根据用于生产所述待处理产品的柔性生产线数据，以及所述待处理产品所属种类的其他产品的历史排产信息，通过数字孪生模型引擎获取所述待处理产品的排产信息之前，所述方法还包括：
根据所述产品信息中的业务逻辑以及各实体离散制造设备的功能，通过所述工业边缘智能CPS管理壳构建所述柔性生产线，以贯通产品全生命周期的业务流与数据流。
在上述产品的处理方法的优选技术方案中，在根据所述产品信息以及所述排产信息，在所述柔性生产线对所述待处理产品进行生产时，所述方法还包括：
通过所述工业边缘智能CPS管理壳采集所述待处理产品的所述初始生产数据。
在上述产品的处理方法的优选技术方案中，所述当所述状态数据指示所述柔性生产线存在问题时，通过动态多任务调度引擎构建新的柔性生产线，包括：
通过所述动态多任务调度引擎，从调度算法库调用调度算法；
基于所述调度算法，根据所述状态数据判断所述柔性生产线是否存在问题；
在确定所述柔性生产线存在问题时，构建所述新的柔性生产线。
在上述产品的处理方法的优选技术方案中，所述基于所述调度算法，根据所述状态数据判断所述柔性生产线是否存在问题，包括：
基于所述调度算法，根据所述状态数据判断所述柔性生产线是否存在工况异常和/或设备故障。
第三方面，本发明实施例提供一种电子设备，包括：处理器、存储器及存储在所述存储器上并可在处理器上运行的计算机程序指令，所述处理器执行所述计算机程序指令时用于实现上述第二方面及各技术方案中所述的方法。
第四方面，本发明实施例提供一种计算机可读存储介质，所述计算机可读存储介质中存储有计算机执行指令，所述计算机执行指令被处理器执行时用于实现上述第二方面及各技术方案中所述的方法。
第五方面，本发明实施例提供一种芯片，所述芯片包括存储器、处理器，所述存储器中存储代码和数据，所述存储器与所述处理器耦合，所述处理器运行所述存储器中的程序使得所述芯片用于执行上述第二方面及各技术方案中所述的方法。
第六方面，本发明实施例提供一种程序产品，包括：计算机程序，当所述程序产品在计算机上运行时，使得所述计算机执行上述第二方面及各技术方案中所述的方法。
第七方面，本发明实施例提供一种计算机程序，当所述计算机程序被处理器执行时，用于执行上述第二方面及各技术方案中所述的方法。
本领域技术人员能够理解的是，本申请实施例提供的工业互联网操作系统和产品的处理方法，该工业互联网操作系统包括异构数据集成引擎、数字孪生模型引擎以及动态多任务调度引擎，异构数据集成引擎将待处理产品的初始生产数据根据预设数据格式进行处理，生成目标生产数据，数字孪生模型引擎根据用于生产待处理产品的柔性生产线数据，以及待处理产品所属种类的其他产品的历史排产信息，获取待处理产品的排产信息，在根据待处理产品的产品信息、排产信息以及目标生产数据，在柔性生产线对待处理产品进行生产时，数字孪生模型引擎还可以对柔性生产线进行状态监控，获取柔性生产线的状态数据，动态多任务调度引擎在状态数据指示柔性生产线存在问题时，构建新的柔性生产线，以实现在新的柔性生产线对待处理产品进行继续生产。在本申请实施例中，异构数据集成引擎能够将待处理产品数据进行统一格式的定义与表达，实现异构数据的融通，数字孪生模型引擎以及动态多任务调度引擎能够对柔性生产线进行监控，以使在柔性生产线存在问题时，及时构建新的柔性生产线，该柔性生产线用于对待处理产品进行继续生产，从而实现任务的优化调度以及资源的协同。
附图说明
下面参照附图来描述本申请的工业互联网操作系统和产品的处理方法，附图为：
图1为本申请实施例提供的工业互联网操作系统的一种结构示意图；
图2为本申请实施例提供的工业互联网操作系统的另一种结构示意图；
图3为本申请实施例提供的产品的处理方法实施例一的流程示意图。
具体实施方式
首先，本领域技术人员应当理解的是，这些实施方式仅仅用于解释本申请的技术原理，并非旨在限制本申请的保护范围。本领域技术人员可以根据需要对其作出调整，以便适应具体的应用场合。
其次，需要说明的是，在本申请实施例的描述中，术语“内”、“外”等指示的方向或位置关系的术语是基于附图所示的方向或位置关系，这仅仅是为了便于描述，而不是指示或暗示所述装置或构件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本申请的限制。
此外，还需要说明的是，在本申请实施例的描述中，除非另有明确的规定和限定，术语“相连”、“连接”应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或一体地连接；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个构件内部的连通。对于本领域技术人员而言，可根据具体情况理解上述术语在本申请实施例中的具体含义。
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，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他实施例，都属于本申请保护的范围。
在介绍本申请的实施例之前，首先对本申请实施例的应用背景进行解释：
近年来，互联网、云计算、大数据和人工智能等技术发展迅猛，各企业都开展了工业互联网操作系统的研究，生成了许多具有代表性的工业互联网操作系统。针对离散行业来讲，工业互联网操作系统在屏蔽工业底层异构性的基础上，以数据和工业机理模型为核心，为上层离散制造全生命周期应用提供数字化、网络化、智能化的服务，具有重要意义。
其中，现有技术的工业互联网操作系统主要包括平台层和应用层，平台层包括人工智能、大数据、云计算，应用层包括用户端和开发者端，平台层、应用层由安全防护系统防护，安全防护系统中的数据信息存储于数据库中，安全防护系统包括数据防护和物理防护，数据防护包括数据筛选模块、危险判断模块、危险预处理模块、人工预警模块、异常危险处理模块，物理防护包括视觉识别模块和语音识别模块。
然而，现有的工业互联网操作系统的数据挖掘分析应用能力不足，存在以下问题：
(1)、离散行业的工业互联网操作系统的基础核心支撑是工业数据的融会贯通，然而，工业数据涉及离散制造全生命周期的不同阶段、不同业务活动、各类异构系统以及各类异构数据类型，导致存在异构数据融通难题。
(2)、离散行业的工业互联网操作系统的应用核心功能是资源的协同调控，然而，由于工业互联网资源调度任务的规模化增长、跨组织协作以及动态多变环境等因素的影响，导致存在动态多任务难以协同调度的难题。
综上所述，传统自动化系统解决方案仍占据市场的主流位置，为离散制造业行业智能化升级需求提供整体解决方案的工业互联网操作系统尚处于发展初期，能够在行业内形成可推广和可复制的成熟工业互联网操作系统并不多，规模有限。
针对上述问题，本申请提供一种工业互联网操作系统，该工业互联网操作系统包括异构数据集成引擎、数字孪生模型引擎以及动态多任务调度引擎。其中，异构数据集成引擎可以将待处理产品数据进行统一格式的定义与表达，实现异构数据的融通，数字孪生模型引擎可以在柔性生产线对待处理产品进行生产时，对生产产品的柔性生产线进行状态监控，同时，动态多任务调度引擎可以在数字孪生模型引擎监控发现柔性生产线存在问题时，构建新的柔性生产线，以实现在新的柔性生产线对待处理产品进行继续生产，从而实现任务的优化调度以及资源的协同。
下面，通过具体实施例对本申请的技术方案进行详细说明。
需要说明的是，下面这几个具体的实施例可以相互结合，对于相同或相似的概念或过程可能在某些实施例中不再赘述。
图1为本申请实施例提供的工业互联网操作系统的一种结构示意图。如图1所示，该工业互联网操作系统可以包括：异构数据集成引擎、数字孪生模型引擎以及动态多任务调度引擎。
其中，异构数据集成引擎将待处理产品的初始生产数据根据预设数据格式进行处理，生成目标生产数据。
可选的，上述处理可以为：根据上述初始生产数据自动制定转换规则，将上述初始生产数据的格式通过转化规则转化为预设数据格式，从而生成目标生产数据。
应理解，该预设数据格式可以是相关工作人员根据实际需求预先设置的，本申请实施例对此不进行具体限制。
可选的，异构数据集成引擎还可以在离散制造业中待处理产品的设计、生产、服务等业务过程中，根据待处理产品的产品信息中的业务逻辑以及各实体离散制造设备的功能，构建柔性生产线，以贯通待处理产品全生命周期的业务流与数据流。
其中，产品信息是根据用户对待处理产品的功能需求和/或外观需求确定的。
可选的，异构数据集成引擎还可以通过相关模型构建基于上述业务逻辑的虚拟柔性生产线，以实现模型语义一致性转换。
可选的，上述相关模型可以存储在模型资源库中，异构数据集成引擎可以通过工业边缘智能信息物理系统(Cyber-Physical Systems，CPS)管理壳调用上述相关模型。
其中，数字孪生模型引擎根据用于生产待处理产品的柔性生产线数据，以及待处理产品所属种类的其他产品的历史排产信息，获取待处理产品的排产信息。
可选的，数字孪生模型引擎还可以根据待处理产品的产品信息、排产信息以及目标生产数据，在柔性生产线对待处理产品进行生产时，对柔性生产线进行状态监控，获取柔性生产线的状态数据。
可选的，数字孪生模型引擎还可以根据上述异构数据集成引擎已构建的虚拟柔性生产线以及待处理产品所属种类的其他产品的历史排产信息、以及场景化机理模型库中数字孪生机理模型，构建待处理产品的全流程数字孪生模型，并通过工业边缘智能CPS管理壳实现虚实映射， 以使数字孪生模型引擎通过上述全流程数字孪生模型实现对待处理产品的生产规划、工况情况、设备状态等进行预测，从而确定排产方案，以及对柔性生产线进行状态监控。
其中，动态多任务调度引擎可以在状态数据指示柔性生产线存在问题时，构建新的柔性生产线，以实现在新的柔性生产线对待处理产品进行继续生产。也就是说，针对生产业务变化以及多客户大规模个性化定制产品的需求，动态多任务调度引擎能够调度满足生产需求的生产资源。
本申请实施例提供一种工业互联网操作系统，该工业互联网操作系统包括异构数据集成引擎、数字孪生模型引擎以及动态多任务调度引擎，异构数据集成引擎将待处理产品的初始生产数据根据预设数据格式进行处理，生成目标生产数据，数字孪生模型引擎根据用于生产待处理产品的柔性生产线数据，以及待处理产品所属种类的其他产品的历史排产信息，获取待处理产品的排产信息，在根据待处理产品的产品信息、排产信息以及目标生产数据，在柔性生产线对待处理产品进行生产时，数字孪生模型引擎还可以对柔性生产线进行状态监控，获取柔性生产线的状态数据，动态多任务调度引擎在状态数据指示柔性生产线存在问题时，构建新的柔性生产线，以实现在新的柔性生产线对待处理产品进行继续生产。在本申请实施例中，异构数据集成引擎能够将待处理产品数据进行统一格式的定义与表达，实现异构数据的融通，数字孪生模型引擎以及动态多任务调度引擎能够对柔性生产线进行监控，以使在柔性生产线存在问题时，及时构建新的柔性生产线，该柔性生产线用于对待处理产品进行继续生产，从而实现任务的优化调度以及资源的协同。
基于图1所示实施例，在一些实施例中，该工业互联网操作系统还可以包括：工业大数据知识引擎和工业边缘智能CPS管理壳。
其中，工业大数据知识引擎获取其他产品的历史产品信息。
可选的，工业大数据知识引擎还可以获取待处理产品所属种类的其他产品的历史排产信息。
可选的，工业大数据知识引擎还可以根据数字孪生模型引擎对柔性生产线进行状态监控获取的柔性生产线的状态监控数据，对柔性生产线在剩下的生产时长中的状态进行预测，获取状态数据。
可选的，工业大数据知识引擎还可以存储异构数据集成引擎处理后的相关数据，其中，该相关数据可以为目标生产数据，还可以为产品设计、生产、服务过程等相关数据。
可选的，为了实现对柔性生产线在剩下的生产时长中的状态进行预测，该工业大数据知识引擎可以包括以下模块：
目标域数据增强知识表征模块、目标域自监督学习知识表征模块、基于多任务元学习的跨域深度迁移学习模块。
其中，目标域数据增强知识表征模块，可以针对待处理产品历史工业数据样本不足且低质量等问题，利用信号域转换、对抗学习、自动数据增广等技术进行数据增强，生成融合跨域知识的增强样本，用于跨域样本的迁移学习模型，便于实现工业复杂场景的分类(故障诊断，异常检测)与预测(寿命预测，库存量预测)问题。
目标域自监督学习知识表征模块，可以对待处理产品设计、制造过程中实时采集到的工业异构数据进行自监督知识表征，将时间域与空间域的工业多源数据表征进行组合，利用监督学习得到具备不变性、等变性、迁移性的多源异构数据深度解耦，以适应动态变化的复杂工业任务。
基于多任务元学习的跨域深度迁移学习模块，可以根据待处理产品，利用该待处理产品相关历史数据，基于元学习机制的迁移学习机制，进行单任务与多任务的迁移以及多个辅助任务的选择机制设计，便于进行工业复杂场景下的分类与精准预测，最终实现新任务的跨域知识深度迁移。
其中，工业边缘智能CPS管理壳根据历史产品信息以及待处理产品的需求信息，确定待处理产品的产品信息。
其中，上述需求信息可以是用户对待处理产品的需求，包括功能需求和/或外观需求。
可选的，工业边缘智能CPS管理壳还可以采集待处理产品的初始生产数据。其中，上述采集处理可以为：接入实体离散制造异构资源，实时采集离散制造设备的全方位数据。
其中，通过智能化CPS管理壳，对离散制造实体设备、产品与服务资源进行语义理解、操作与调度，通过智能感知网络实现资源的自动化感知以及设备异构协议智能自适应匹配。此外，通过对模型资源库中 的模型进行自动标注、识别、分类、检索以及编译优化，实现实体与模型之间的自适应映射。
在上述实施例中，通过智能化CPS管理壳和工业大数据知识引擎确定待处理产品的产品信息，从而得到适合该用户的设计方案，后续对产品进行处理奠定了基础，提高了处理的准确性。
基于图1所示实施例，在一些实施例中，该工业互联网操作系统还可以包括：
调度算法库，该调度算法库存储有调度算法，调度算法用于实现动态多任务调度引擎的调度功能。
在本实施例中，调度算法库可以存储调度算法，以便于动态多任务调度引擎调用，调度算法在动态多任务调度引擎调用后，可以对柔性生产线进行优化设计，构建适于业务目标的新的柔性生产线，从而实现多任务下的高效精准调度。
图2为本申请实施例提供的工业互联网操作系统的另一种结构示意图。如图2所示，该工业互联网操作系统包括：
离散行业应用、工业应用手机软件(application，APP)、核心组件、基础通用组件、离散制造资源、安全防护体系以及标准标识体系。
其中，离散行业应用包括用户交互、研发创新、精准销售、协同采购、智能制造、智慧物流以及智能服务等离散制造业产品全生命周期应用。
其中，工业应用APP包括交互定制类工业APP、开发设计类工业APP、精准销售类工业APP、模块化采购类工业APP、智能制造类工业APP、智能物流类工业APP、智能服务类工业APP，从而实现个性化定制、网络化协同、智能化生产以及服务化延伸。
其中，核心组件包括工业云层以及工业边缘层，工业云层包括工业互联网应用低代码快速构建工具、场景化机理模型库、工业引擎、调度算法库以及大数据湖，工业边缘层包括模型资源库、工业边缘智能CPS管理壳以及接口协议库。
其中，工业互联网应用低代码快速构建工具是在其它核心组件库的基础上，面向离散行业个性化定制和网络化协同应用需求，融合工业边缘智能、模型资源库、接口协议库、大数据湖、工业引擎、场景化机理模型库等核心组件，构建工业互联网应用代码快速开发工具。该工具覆 盖软件用户界面(User Interface，UI)、服务、控制、实体、流程、规则等云原生应用的模型资源库及编译器，基于图形式低代码快速开发技术实现模型驱动的云原生应用快速构造，降低工业APP构建难度，为开发者社群及大规模定制产业链生态形成提供底层架构支持。
其中，场景化机理模型库存储针对离散制造不同应用场景需求的数字孪生机理模型，便于进行调度应用。
其中，工业引擎包括数字孪生模型引擎以及动态多任务调度引擎。
其中，大数据湖包括异构数据集成引擎、大数据湖治理工具以及工业大数据知识引擎。
其中，工业边缘层包括模型资源库、工业边缘智能CPS管理壳以及接口协议库。
可选的，模型资源库通过对生产设备、产品和服务分类结构提炼共性信息模型元素子集，建立各类设备、产品和服务资源的全要素资源统一多维语义本体模型。此外，模型资源库将各类资源模型通过语义分析技术建立关联关系，进一步实现关联关系实例化，为工业边缘智能CPS管理壳的全要素资源感知和自适应智能匹配提供检索基础。
可选的，接口协议库为离散制造设备、产品和服务资源语义模型提供相应的接口协议，以便于实现工厂网络内的全要素资源智能感知。
在本实施例中，针对工业互联网平台中资源接入、数据融通、任务调度等组件的多态、异构、混合的特点，核心组件制定面向领域的组件服务化组装机制，采用标准化工业微服务技术，进行工业互联网操作系统组件各逻辑执行模块间的统一接口标准和网关，通过构件组装和编排模型形成基于微服务和容器技术的核心组件库。
其中，基础通用组件包括基础类组件库如容器服务、负载均衡、云存储以及内容分发网络(Content Delivery Network，CDN)等组件；开发类组件库如过程、方法与系统的统称(Development和Operations的组合，DevOps)、微服务治理、函数服务、开放的应用编程接口(open Application Programming Interface，OpenAPI)等；中间件组件如数据库、消息队列、缓存以及搜索引擎等；运维类组件库如监控预警、日志服务、云备份以及异地容灾等等，同时还包括服务器、存储设备、网络设备、安全设备、网络带宽以及加密机等相关组件。
其中，离散制造资源包括数控基床、工业机器人、运输车(Automated Guided Vehicle，AGV)、传感器、工业交换机、摄像头、增强现实(Augmented Reality，AR)眼镜、检测设备以及其他资源。
在本申请实施例中，面向离散制造行业设备、产品和服务全要素资源接入需求，针对全要素资源语义的建模自适配问题，构建离散制造设备、产品和服务全要素资源统一语义模型，构建模型资源库。设备按照基本类型分为工业机器人、数控机床、AGV、检测设备等，产品分为终端智能消费产品、智能机电产品等，服务按照全生命周期分为研发设计服务、生产制造服务、经营管理服务、售后运维服务等。在分类基础上提炼共性信息，融合多特征的语义信息标注方法以及语义信息跨分类检索匹配方法，通过对设备、产品和服务分类结构提炼共性信息模型元素子集，建立各类设备、产品和服务资源的统一多维语义本体模型，形成模型资源库。构建工业异构协议高效智能自适应适配方法并打造工业边缘智能CPS管理壳、模型资源库、接口协议库等核心组件，有效解决现有技术中由于离散制造资源种类繁多、范围巨大、差异巨大，导致的异构要素资源的接入难问题。
进一步的，面向离散制造行业异构大数据空间共享融通需求，针对强异构数据跨域知识迁移问题，进行时空多尺度业务过程数据集成，进行多维语义关联数据空间隐性知识融合，进行工业异构跨域知识深度迁移；面向离散制造流程多任务调度优化的需求，针对高动态任务不确定性精准调控问题，提出离散制造调度流程数字孪生建模，实现基于数字孪生预测的多任务精准优化调度。
本申请实施例提出基于多维语义建模的离散制造设备、产品和服务全要素资源统一语义可扩展模型，建立面向工业异构协议自适应适配的智能CPS管理壳，突破离散制造资源难以统一标准化建模与自适应接入的瓶颈。同时，突破工业异构数据空间深层次知识难以表征迁移的瓶颈，提出基于高阶张量空间建模的多维关联异构数据空间隐性知识表征融合方法，利用基于多任务元学习的工业知识跨域迁移学习方法，形成业务流程异构数据集成引擎和工业大数据知识引擎等核心组件。进一步的，提出基于虚实融合预测的多任务智能优化调度，突破离散制造复杂不确定环境下动态多任务难以精准调控的瓶颈局限，形成工业互联网数字孪生模型引擎和动态多任务调度引擎等核心组件。
本申请实施例通过构建离散制造行业的工业互联网操作系统，能够协助企业在现有要素资源的基础上建立一个统一的业务操作平台，所有软件和设备的功能和数据，均通过这个统一平台调度，将突破工业互联网操作系统核心瓶颈问题，形成新系统、新组件、新应用，实现工厂的持续改进与迭代优化，赋能离散制造行业高质量发展。
本申请实施例围绕工业互联网要素资源“接不全”、异构数据“融不深”、协同调度“控不精”等三个瓶颈挑战，结合新一代信息技术发展趋势，将边缘智能、大数据空间、深度迁移学习、数字孪生、强化学习调度决策等技术与离散行业工业互联网特点进行融合创新，突破全要素资源语义建模自适配、强异构数据跨域知识深度迁移、高动态任务不确定性精准调控等三个关键问题，形成智能化、标准化、自主化核心技术组件，打造全面智接、深度智融、精准智控的离散行业工业互联网操作系统。本发明对于工业互联网这一前沿领域、尤其是离散行业工业互联网操作系统新理论、新技术的突破与发展具有重要的科学价值与应用价值。工业互联网操作系统将成为工业互联网产业界应用的核心共性支撑平台，将为离散制造业企业的转型升级、将本增效和盈利提供核心技术与系统支撑。
图3为本申请实施例提供的产品的处理方法实施例一的流程示意图。如图3所示，该产品的处理方法应用于上述任一实施例所述的工业互联网操作系统中的服务器，该产品的处理方法可以包括如下步骤：
S301、根据用户对待处理产品的功能需求和/或外观需求，确定待处理产品的产品信息。
在实际应用中，服务器需要获取客户对待处理产品的个性化需求，从而根据上述个性化需求确定待处理产品的产品信息。
可选的，用户可以通过工业互联网操作系统的前端设备输入个性化需求，上述服务器响应于用户的输入操作，获取用户输入的待处理产品的个性化需求。
可选的，上述输入操作可以为语音输入操作、文字输入操作以及相关控件的点击输入操作等，可以根据实际情况确定，本申请实施例不对具体的输入操作方式进行限定。
S302、根据用于生产待处理产品的柔性生产线数据，以及待处理产品所属种类的其他产品的历史排产信息，通过数字孪生模型引擎获取待处理产品的排产信息。
在实际应用中，在确定待处理产品的产品信息后，该待处理产品即将进入生产制造环节。然而，大量的待处理产品对于柔性生产线以及协同优化提出了更高的要求，因此还需要确定待处理产品的排产计划，从而保证待处理产品的高效生产。
可选的，可以通过数字孪生模型引擎中的全流程数字孪生模型，根据柔性生产线数据以及历史排产信息，对待处理产品的生产规划、工况情况、设备状态等进行预测，从而确定排产方案。
其中，上述柔性生产线数据可以为该柔性生产线的全方位数据。
可选的，上述柔性生产线数据可以是预先通过工业边缘智能CPS管理壳获取的，上述历史排产信息可以是预先通过工业大数据知识引擎从其他设备的历史排产案例中进行挖掘并迁移得到的。
S303、当根据产品信息以及排产信息，在柔性生产线对待处理产品进行生产时，通过异构数据集成引擎将待处理产品的初始生产数据根据预设数据格式进行处理，生成目标生产数据。
其中，初始生产数据可以为该柔性生产线中各采集离散制造设备的生产数据。
可选的，上述处理可以为：通过异构数据集成引擎根据上述初始生产数据自动制定转换规则，将上述初始生产数据的格式通过转化规则转化为预设数据格式，从而生成目标生产数据。
应理解，该预设数据格式可以是相关工作人员根据实际需求预先设置的，本申请实施例对此不进行具体限制。
可选的，上述待处理产品的初始生产数据可以通过工业边缘智能CPS管理壳采集得到。
S304、根据目标生产数据，通过数字孪生模型引擎对柔性生产线进行状态监控，获取柔性生产线的状态数据。
其中，状态监控包括对工况和/或设备进行监控。
S305、当状态数据指示柔性生产线存在问题时，通过动态多任务调度引擎构建新的柔性生产线，以实现在新的柔性生产线对待处理产品进行继续生产。
在实际应用中，在通过数字孪生模型引擎检测到柔性生产线的状态存在问题时，需要将待处理产品转换到其他柔性生产线上继续生产， 以免影响该待处理产品的生产进度。
本申请实施例提供一种产品的处理方法，通过根据用户对待处理产品的功能需求和/或外观需求，确定待处理产品的产品信息，根据用于生产待处理产品的柔性生产线数据，以及待处理产品所属种类的其他产品的历史排产信息，通过数字孪生模型引擎获取待处理产品的排产信息，当根据产品信息以及排产信息，在柔性生产线对待处理产品进行生产时，通过异构数据集成引擎将待处理产品的初始生产数据根据预设数据格式进行处理，生成目标生产数据，根据目标生产数据，通过数字孪生模型引擎对柔性生产线进行状态监控，获取柔性生产线的状态数据，当状态数据指示柔性生产线存在问题时，通过动态多任务调度引擎构建新的柔性生产线，以实现在新的柔性生产线对待处理产品进行继续生产。本技术方案能够应用于柔性计划排产、柔性生产线重构、工况监控预测、动态协调调度等应用场景，通过异构数据集成引擎将待处理产品数据进行统一格式的定义与表达，通过数字孪生模型引擎和动态多任务调度引擎在柔性生产线存在问题时，构建新的柔性生产线，以实现在新的柔性生产线对待处理产品进行继续生产，从而实现任务的优化调度以及资源的协同。
可选的，基于图3所示实施例，S301可以通过以下步骤实现：
通过工业大数据知识引擎，获取其他产品的历史产品信息，根据历史产品信息以及用户对待处理产品的需求，通过工业边缘CPS管理壳，确定待处理产品的产品信息。
在本申请实施例中，通过调用工业大数据知识引擎提供知识支撑，然后根据用户对待处理产品的需求调用工业边缘CPS管理壳，对历史产品信息进行优化改进，从而确定待处理产品的产品信息，得到待处理产品的设计方案，提高了生产出的待处理产品与用户需求的匹配度。
可选的，基于图3所示实施例，S304可以通过以下步骤实现：
根据目标生产数据，通过数字孪生模型引擎对柔性生产线进行状态监控，获取柔性生产线的状态监控数据，根据状态监控数据，通过工业大数据知识引擎对柔性生产线在剩下的生产时长中的状态进行预测，获取状态数据。
在本申请实施例中，可以在生产执行过程中调用数字孪生模型引擎对柔性生产线进行实时状态监控，并调用工业大数据知识引擎对工况异常、设备故障、时序趋势等进行预测，获取状态数据，能够有效防止 柔性生产线在剩下的生产时长中出现异常，影响待处理产品的生产工作的问题。
可选的，基于图3所示实施例，在S302之前，该产品的处理方法还可以包括以下步骤实现：
根据产品信息中的业务逻辑以及各实体离散制造设备的功能，通过异构数据集成引擎构建柔性生产线，以贯通产品全生命周期的业务流与数据流。
在本申请实施例中，异构数据集成引擎根据产品信息中的业务逻辑以及各实体离散制造设备的功能，构建柔性生产线，为后续将待处理产品在柔性生产线上进行生产奠定了基础，提高了生产效率。
可选的，基于图3所示实施例，在根据产品信息以及排产信息，在柔性生产线对待处理产品进行生产时，该产品的处理方法还可以包括以下步骤实现：
通过工业边缘智能CPS管理壳采集待处理产品的初始生产数据。
在本申请实施例中，工业边缘智能CPS管理壳可以接入实体离散制造异构资源，实时该柔性生产线中各采集离散制造设备的生产数据，从而获取该待处理产品的初始生产数据，以使后续能对柔性生产线进行监控，保证待处理产品的顺利生产。
可选的，基于图3所示实施例，S305可以通过以下步骤实现：
通过动态多任务调度引擎，从调度算法库调用调度算法，基于调度算法，根据状态数据判断柔性生产线是否存在问题，在确定柔性生产线存在问题时，构建新的柔性生产线。
在上述实施例中，调度算法库中存储有调度算法，动态多任务调度引擎可以根据不同场景调用相应的调度算法对资源进行调度，从而解决现有技术中协同调度控不精的问题。
可选的，在上述实施例的基础上，上述基于调度算法，根据状态数据判断柔性生产线是否存在问题，可以通过以下步骤实现。
基于调度算法，根据状态数据判断柔性生产线是否存在工况异常和/或设备故障。
在上述实施例中，由于工况和设备是影响待处理产品生产的两大因素，因此，可以通过状态数据判断柔性生产线是否存在工况异常和 /或设备故障，就能够根据判断结果确定柔性生产线是否影响待处理产品的生产过程，以使在影响的情况及时进行后续调度处理。
本实施例还提供一种电子设备，包括：
处理器，存储器，以及，接口；
存储器，用于存储所述处理器的可执行指令，该存储器还可以是flash(闪存)；
其中，所述处理器配置为经由执行所述可执行指令来执行上述方法中的各个步骤。具体可以参见前面方法实施例中的相关描述。
可选地，存储器既可以是独立的，也可以跟处理器集成在一起。
当所述存储器是独立于处理器之外的器件时，所述电子设备还可以包括：
总线，用于连接所述处理器以及所述存储器以及接口。该接口包括用于进行数据传输的通信接口以及用于进行人机交互的显示界面或者操作界面等。
本实施例还提供一种可读存储介质，可读存储介质中存储有计算机程序，当电子设备的至少一个处理器执行该计算机程序时，电子设备执行上述的各种实施方式提供的方法。
本实施例还提供一种程序产品，该程序产品包括计算机程序，该计算机程序存储在可读存储介质中。电子设备的至少一个处理器可以从可读存储介质读取该计算机程序，至少一个处理器执行该计算机程序使得电子设备实施上述的各种实施方式提供的方法。
本实施例还提供一种芯片，所述芯片包括存储器、处理器，所述存储器中存储代码和数据，所述存储器与所述处理器耦合，所述处理器运行所述存储器中的程序使得所述芯片用于执行上述各种实施方式提供的方法。
本实施例还提供一种计算机程序，当所述计算机程序被处理器执行时，用于执行前述各种实施方式提供的方法。
应当理解的是，本申请并不局限于上面已经描述并在附图中示出的精确结构，并且可以在不脱离其范围进行各种修改和改变。本申请的范围仅由所附的权利要求书来限制。

Claims (15)

  1. 一种工业互联网操作系统，其特征在于，包括：
    异构数据集成引擎、数字孪生模型引擎以及动态多任务调度引擎；
    所述异构数据集成引擎将待处理产品的初始生产数据根据预设数据格式进行处理，生成目标生产数据；
    所述数字孪生模型引擎根据用于生产所述待处理产品的柔性生产线数据，以及所述待处理产品所属种类的其他产品的历史排产信息，获取所述待处理产品的排产信息；还可以根据所述待处理产品的产品信息、所述排产信息以及所述目标生产数据，在柔性生产线对所述待处理产品进行生产时，对所述柔性生产线进行状态监控，获取所述柔性生产线的状态数据，所述产品信息是根据用户对所述待处理产品的功能需求和/或外观需求确定的；
    所述动态多任务调度引擎在所述状态数据指示所述柔性生产线存在问题时，构建新的柔性生产线，以实现在所述新的柔性生产线对所述待处理产品进行继续生产。
  2. 根据权利要求1所述的系统，其特征在于，所述系统还包括：
    工业大数据知识引擎和工业边缘智能信息物理系统CPS管理壳；
    所述工业大数据知识引擎获取所述其他产品的历史产品信息；
    所述工业边缘智能CPS管理壳根据所述历史产品信息以及用户对所述待处理产品的功能需求和/或外观需求，确定所述待处理产品的所述产品信息。
  3. 根据权利要求2所述的系统，其特征在于，所述系统还包括：
    调度算法库，所述调度算法库存储有调度算法，所述调度算法用于实现动态多任务调度引擎的调度功能。
  4. 一种产品的处理方法，其特征在于，应用于权利要求1至3任一项所述的工业互联网操作系统中的服务器，所述方法包括：
    根据用户对所述待处理产品的功能需求和/或外观需求，确定待处理产品的产品信息；
    根据用于生产所述待处理产品的柔性生产线数据，以及所述待处理产品所属种类的其他产品的历史排产信息，通过数字孪生模型引擎获取所述待处理产品的排产信息；
    当根据所述产品信息以及所述排产信息，在柔性生产线对所述待处理产品进行生产时，通过异构数据集成引擎将所述待处理产品的初始生产数据根据预设数据格式进行处理，生成目标生产数据；
    根据所述目标生产数据，通过所述数字孪生模型引擎对所述柔性生产线进行状态监控，获取所述柔性生产线的状态数据；
    当所述状态数据指示所述柔性生产线存在问题时，通过动态多任务调度引擎构建新的柔性生产线，以实现在所述新的柔性生产线对所述待处理产品进行继续生产。
  5. 根据权利要求4所述的方法，其特征在于，所述确定待处理产品的产品信息，包括：
    通过工业大数据知识引擎，获取所述其他产品的历史产品信息；
    根据所述历史产品信息以及用户对所述待处理产品的需求，通过工业边缘智能信息物理系统CPS管理壳，确定所述待处理产品的所述产品信息。
  6. 根据权利要求5所述的方法，其特征在于，所述根据所述目标生产数据，通过所述数字孪生模型引擎对所述柔性生产线进行状态监控，获取所述柔性生产线的状态数据，包括：
    根据所述目标生产数据，通过所述数字孪生模型引擎对所述柔性生产线进行状态监控，获取所述柔性生产线的状态监控数据；
    根据所述状态监控数据，通过所述工业大数据知识引擎对所述柔性生产线在剩下的生产时长中的状态进行预测，获取所述状态数据。
  7. 根据权利要求5或6所述的方法，其特征在于，在所述根据用于生产所述待处理产品的柔性生产线数据，以及所述待处理产品所属种类的其他产品的历史排产信息，通过数字孪生模型引擎获取所述待处理产品的排产信息之前，所述方法还包括：
    根据所述产品信息中的业务逻辑以及各实体离散制造设备的功能，通过所述异构数据集成引擎构建所述柔性生产线，以贯通产品全生命周期的 业务流与数据流。
  8. 根据权利要求7所述的方法，其特征在于，在根据所述产品信息以及所述排产信息，在所述柔性生产线对所述待处理产品进行生产时，所述方法还包括：
    通过所述工业边缘智能CPS管理壳采集所述待处理产品的所述初始生产数据。
  9. 根据权利要求8所述的方法，其特征在于，所述当所述状态数据指示所述柔性生产线存在问题时，通过动态多任务调度引擎构建新的柔性生产线，包括：
    通过所述动态多任务调度引擎，从调度算法库调用调度算法；
    基于所述调度算法，根据所述状态数据判断所述柔性生产线是否存在问题；
    在确定所述柔性生产线存在问题时，构建所述新的柔性生产线。
  10. 根据权利要求9所述的方法，其特征在于，所述基于所述调度算法，根据所述状态数据判断所述柔性生产线是否存在问题，包括：
    基于所述调度算法，根据所述状态数据判断所述柔性生产线是否存在工况异常和/或设备故障。
  11. 一种电子设备，其特征在于，包括：
    处理器，存储器，以及，接口；
    存储器，用于存储所述处理器的可执行指令；
    其中，所述处理器配置为经由执行所述可执行指令来执行权利要求4至10任一项所述的方法。
  12. 一种存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现权利要求4至10任一项所述的方法。
  13. 一种芯片，其特征在于，所述芯片包括存储器、处理器，所述存储器 中存储代码和数据，所述存储器与所述处理器耦合，所述处理器运行所述存储器中的程序使得所述芯片用于执行上述权利要求4至10任一项所述的方法。
  14. 一种程序产品，其特征在于，包括：计算机程序，当所述程序产品在计算机上运行时，使得所述计算机执行上述权利要求4至10任一项所述的方法。
  15. 一种计算机程序，其特征在于，当所述计算机程序被处理器执行时，用于执行上述权利要求4至10任一项所述的方法。
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