WO2020172865A1 - 数字双胞胎建模仿真方法、装置和系统 - Google Patents

数字双胞胎建模仿真方法、装置和系统 Download PDF

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Publication number
WO2020172865A1
WO2020172865A1 PCT/CN2019/076549 CN2019076549W WO2020172865A1 WO 2020172865 A1 WO2020172865 A1 WO 2020172865A1 CN 2019076549 W CN2019076549 W CN 2019076549W WO 2020172865 A1 WO2020172865 A1 WO 2020172865A1
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Prior art keywords
simulation
module
management module
semantic
digital twin
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PCT/CN2019/076549
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English (en)
French (fr)
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李朝春
郁彦彬
王冬
李明
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西门子(中国)有限公司
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Priority to PCT/CN2019/076549 priority Critical patent/WO2020172865A1/zh
Priority to EP19916710.7A priority patent/EP3916501A4/en
Priority to CN201980006487.5A priority patent/CN111868645B/zh
Priority to US17/434,498 priority patent/US20220138376A1/en
Publication of WO2020172865A1 publication Critical patent/WO2020172865A1/zh

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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00Programme-control systems
    • G05B19/02Programme-control systems electric
    • G05B19/418Total 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] or computer integrated manufacturing [CIM]
    • G05B19/41885Total 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] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00Computer-aided design [CAD]
    • G06F30/20Design optimisation, verification or simulation
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems
    • G05B2219/32Operator till task planning
    • G05B2219/32301Simulate production, process stages, determine optimum scheduling rules
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems
    • G05B2219/32Operator till task planning
    • G05B2219/32339Object oriented modeling, design, analysis, implementation, simulation language
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02PCLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the invention relates to industrial digitization, in particular to a method, device and system for modeling and simulation of digital twins.
  • the production of digital twins is closely related to performance evaluation and operation improvement in production
  • the production of digital twins is playing an increasingly important role in the digitalization of manufacturing.
  • the factory's production activities can be fully simulated, the actual production capacity can be evaluated, and the dynamic bottleneck in the production process can also be determined.
  • the production process can give the digital twins global optimization, including scheduling and operation time adjustment.
  • developers need to repeatedly collect data from different levels of the factory, and also need to perform digital twin planning based on the collected data in commercial production modeling and software environment simulation. This is very time consuming, wastes manpower, increases the cost of data twin development, and is not good for the development and application of data twin technology.
  • the prior art does not have a solution for the automatic generation of digital twins.
  • Only some commercial software developers or integrators try to establish the backbone of the data flow through the entire production cycle, where the production cycle includes: product design, process design, and production simulation.
  • Data streams can be imported and exported from one platform to another.
  • the process design stage data can be exported to the production simulation software to automatically generate the execution model, such as layout information.
  • the above-mentioned solution has several disadvantages, one of which is cost.
  • the above solution requires the purchase of commercial software for the entire industrial process, and some small manufacturers cannot afford the software purchase cost.
  • Another disadvantage is that some outdated production lines have undergone several rounds of upgrades, resulting in the initial process design data being no longer suitable for digital production. Therefore, the existing technology has not established a digital twin modeling and simulation mechanism, and does not rely on senior personnel and domain experts.
  • the first aspect of the present invention provides a digital twin modeling and simulation method, which includes the following steps: S1, generating a manufacturing model body, collecting field data, and generating a semantic model instance based on the manufacturing model body and the field data S2, retrieve the attributes of the field data according to the type of the field data, extract the data from the semantic model instance according to the retrieval result, and simulate the semantic model instance on the simulation platform.
  • the simulation platform includes: a semantic retrieval module; a device generation module; a material preparation module; an order management module; a process management module; a logic management module; and a key performance indicator module.
  • the resource library of the simulation platform includes a device library, a transmission library, and a material space.
  • the step S2 also includes the following steps: the simulation platform completes the initialization trigger after obtaining the production digital twin model generation and simulation request, specifies the semantic retrieval module to perform the semantic retrieval and obtains the retrieval result; the process route of the semantic retrieval module is from the process management Module acquisition, set the device from the preset device library in the simulation template, and instruct the material preparation module to prepare materials with a copy of the original material entity in the material space, and provide raw materials to the simulation template, and the order management module uses predefined The order sequence downloads the order and provides it to the simulation template; the simulation is triggered, the process management module specifies the device and automatically selects the process time before placing the product on the device in the simulation. The logic management module detects the manufacturing status in each device.
  • the logic management module specifies the product-based process route of the production component to be transported to the downstream workstation; when the simulation is triggered, the simulation template will simulate The status is reported to the key performance indicator module, and the key performance output required by the customer is sent.
  • the manufacturing model ontology includes individual, type, target attribute and data attribute.
  • the manufacturing model ontology is classified into sales orders, resources, tasks, equipment, materials, parts, operation steps, equipment collections, raw materials, products, process routes, factory areas, purchase orders, Production orders, companies and factories.
  • the second aspect of the present invention provides a digital twin modeling and simulation system, which includes:
  • a processor and a memory coupled with the processor, the memory having instructions stored therein, which when executed by the processor cause the electronic device to perform actions, the actions including: S1, generating a manufacturing Model ontology, collecting field data, and generating semantic model instances based on the manufacturing model ontology and the field data; S2, retrieving the attributes of the field data according to the type of field data, and extracting the data from the semantic model instances according to the retrieval results Out, and simulate the semantic model instance on the simulation platform.
  • the simulation platform includes: a semantic retrieval module; a device generation module; a material preparation module; an order management module; a process management module; a logic management module; a key performance indicator module.
  • the resource library of the simulation platform includes a device library, a transmission library, and a material space.
  • the step S2 also includes the following steps: the simulation platform completes the initialization trigger after obtaining the production digital twin model generation and simulation request, specifies the semantic retrieval module to perform the semantic retrieval and obtains the retrieval result; the process route of the semantic retrieval module is from the process management Module acquisition, set the device from the preset device library in the simulation template, and instruct the material preparation module to prepare materials with a copy of the original material entity in the material space, and provide raw materials to the simulation template, and the order management module uses predefined The order sequence downloads the order and provides it to the simulation template; the simulation is triggered, the process management module specifies the device and automatically selects the process time before placing the product on the device in the simulation. The logic management module detects the manufacturing status in each device.
  • the logic management module specifies the product-based process route of the production component to be transported to the downstream workstation; when the simulation is triggered, the simulation template will simulate The status is reported to the key performance indicator module, and the key performance output required by the customer is sent.
  • the manufacturing model ontology includes individual, type, target attribute and data attribute.
  • the manufacturing model ontology is classified into sales orders, resources, tasks, equipment, materials, parts, operation steps, equipment collections, raw materials, products, process routes, factory areas, purchase orders, Production orders, companies and factories.
  • a third aspect of the present invention provides a digital twin modeling simulation device, which includes: a modeling device that generates a manufacturing model body, collects field data, and generates a semantic model based on the manufacturing model body and the field data Example; a simulation device that retrieves the attributes of the field data according to the type of the field data, extracts the data from the semantic model instance according to the retrieval result, and simulates the semantic model instance on the simulation platform.
  • the fourth aspect of the present invention provides a computer program product that is tangibly stored on a computer-readable medium and includes computer-executable instructions that, when executed, cause at least one processor to Perform the method described in the first aspect of the present invention.
  • the fifth aspect of the present invention provides a computer-readable medium on which computer-executable instructions are stored, and when executed, the computer-executable instructions cause at least one processor to perform the method according to the first aspect of the present invention.
  • the digital twin modeling simulation mechanism provided by the present invention has the ability to be applied to all types of production and manufacturing, and it is based on a unified standard production and manufacturing ontology, which fully demonstrates the flexibility of the present invention.
  • the invention can reduce the dependence of senior experts in the field on the production modeling work, and can reduce the manpower input in the complex production of digital twins.
  • Fig. 1 is a schematic diagram of a manufacturing model ontology of a digital twin modeling mechanism according to a specific embodiment of the present invention
  • FIG. 2 is a schematic diagram of an example of a semantic model of a digital twin modeling mechanism according to a specific embodiment of the present invention
  • FIG. 3 is a schematic diagram of the software interface of a simulation platform of a digital twin modeling mechanism according to a specific embodiment of the present invention
  • Fig. 4 is a schematic flowchart of a digital twin modeling mechanism according to a specific embodiment of the present invention.
  • the digital twin modeling and simulation mechanism provided by the present invention is divided into two parts: modeling and simulation.
  • the common manufacturing model body is used to describe the relationship of all aspects of manufacturing and integrate field data from the factory.
  • the semantic retrieval function is used to extract useful information and results, and transmit the information and results to the simulation platform for the production of digital twins for simulation.
  • the semantic model is used to describe the manufacturing process using an ontology description language, which is called a knowledge graph.
  • the manufacturing model ontology is a pre-defined standard, such as the ISA95 standard, which is a unified standard widely used in the field of manufacturing. Specifically, most standards are used to describe models, objects, activities, and integration in manufacturing. Each standard is based on a specific description element.
  • the digital twin modeling and simulation method provided by the present invention includes the following steps:
  • step S1 is performed to generate a manufacturing model ontology according to different protocols in the field, collect factory site data, and generate a semantic model template based on the manufacturing model ontology and the site data.
  • the manufacturing model body is based on the ISA95 standard.
  • the manufacturing model ontology is shown by the semantic model shown in Figure 1.
  • the manufacturing model ontology shown in Figure 1 has four basic elements, including individuals, classes, object properties, and data properties. Among them, the type is the core element of the manufacturing model ontology based on the ISA95 standard, which is the abstraction of the individual.
  • the target attribute is used to describe the relationship between different types. Data attributes are used to describe type characteristics.
  • the production, operation, and topology in manufacturing are regulated by ontology web language and integrated into the common manufacturing model ontology as shown in Figure 1.
  • the manufacturing model ontology based on the ISA95 standard is divided into purchase order, resources, job, equipment, and raw materials according to the classification and attributes of factory site data.
  • material parts (part), operation steps (operation), equipment collection (work unit), raw materials, products (product), routing (routing), factory area (area), purchase order (sales order), production order ( order), enterprise (enterprise) and factory (site).
  • the attributes of sales order 1 include purchase order number, arrival time, expiration time, status, product number and quantity
  • the attributes of production order 2 include order number, start time, end time, status, Product number and quantity.
  • the attributes of product 3 include product number and product name.
  • the attributes of raw material 4 include material number and material quantity.
  • the attributes of material 5 include material number and material name.
  • the attributes of purchase order 6 include purchase order number and purchase date. , Arrival date, material number and quantity.
  • the attributes of part 7 include part number and part name.
  • the attributes of operation step 8 include operation type, operation number and operation name.
  • the attributes of process route 9 include route number, route name and operation steps.
  • the attributes of task 10 include task type, task number, task name, start time and end time.
  • the attributes of resource 11 include resource type, resource number and resource name.
  • the attributes of device 12 include device type, device number and device name.
  • Equipment The attributes of the collection 13 include collection type, collection number, and collection name
  • the attributes of the factory area 14 include the area number and the area name
  • the attributes of the factory 15 include the factory number and the name of the factory.
  • the attributes of the company 16 include the company number and the company name.
  • the above classification runs through the entire manufacturing process. For example, manufacturing starts with receiving a sales order 1, and then converts the sales order 1 into a production order 2.
  • the production order 2 is used to produce product 3, and product 3 requires raw material 4.
  • the material 5 describes in detail the specific materials required by the different components of the entire product.
  • the specific materials are purchased through purchase order 6.
  • the component 7 is an intermediate generation part of different components of the entire product, which is manufactured by a single production step provided by operation step 8.
  • operation step 8 is divided into multiple tasks 10.
  • product 3 is manufactured according to process route 9.
  • the task 10 uses different devices 12, the device 12 belongs to the equipment set 13, and the equipment set 13 is a set of the same functional attribute equipment.
  • the equipment combination 13 belongs to a different factory area 14, the factory area 14 belongs to a specific factory 15, and the factory 15 belongs to a different enterprise 16.
  • a specific factory data includes topology data, device data, product and production data, which can generate a factory instance.
  • a factory has two machines, one machine is used for drilling operations, and the other machine is used for assembling operations.
  • the factory is involved in manufacturing tables and chairs.
  • the process route of the table includes two steps: first perform the drilling step and then perform the assembly step. If the factory receives a sales order for 100 tables.
  • a semantic model instance is generated based on the manufacturing model ontology shown in FIG. 1 and the field data of this embodiment.
  • the field data includes specific sales orders, products, process routes, raw materials, materials, operation steps, devices and factories, etc., as well as the above The attributes of the entity.
  • Figure 2 shows a semantic model example according to a specific embodiment of the present invention.
  • the semantic model example of this embodiment includes multiple entities and entity attributes.
  • the entities include: sales order 101, product 102, process route 103, Raw material 104, material 105, material 106, operation step 107, operation step 108, first device 109, second device 110, and first factory 111.
  • Each of the above entities has one or more entity attributes.
  • the factory received a sales order 101 for 100 tables.
  • the attributes of the sales order 101 include order number, arrival time, expiration time, product number and quantity.
  • the order number is "80010111” and the arrival time is “11 AM on January 1, 2019”
  • the expiration time is "12 AM on January 2, 2019”
  • the status is "not started”
  • the product number is "10011111”
  • the quantity is "100”.
  • the sales order 101 produces a product 102, and the attributes of the product 102 include a product number and a product name, where the product number is "1001111" and the product name is "table”.
  • the attributes of the raw material 104 of the product 102 include the name of the material and the quantity of the material.
  • the raw material 104 is further divided into a table top material 105 and a table leg material 106.
  • the attributes of the table top material 105 include material number and material name, and the attributes of the table leg material 106 include material number and material name. Further, the material number of the table top material 105 is "1000001", the name of the table top material 105 is “tabletop”, the material number of the table leg material 106 is "1000002", and the material name of the table leg material 106 is "table leg”.
  • the product 102 is manufactured based on the process route 103, and the attributes of the process route 103 include route number, operation number, and operation name.
  • the route number is "30001001”
  • the first operation number is "OP10”
  • the second operation number is "OP20”
  • the operation name of the first operation is "drilling”
  • the operation name of the second operation is "assembly”.
  • the process route 103 includes a first operation step 107 and a second operation step 108.
  • the attributes of the first operation step 107 include operation name and duration, where the operation name is "drilling" and the duration is "5 minutes”.
  • the attributes of the second operation step 108 include operation name and duration, where the operation name is "assembly” and the duration is "4.5 minutes”.
  • the first operation step 107 is performed in the first device 109, wherein the device number of the first device 109 is "2000100", and the device name of the first device 109 is "drilling station”.
  • the second operation step 108 is performed in the first device 110, wherein the device number of the second device 110 is "2000101", and the device name of the second device 110 is "assembly station". Both the first device 109 and the second device 110 belong to the first factory 111.
  • the attributes of the first factory 111 include the name of the factory, where the name of the factory is "Star manufacturing Ltd.”.
  • the relationship between multiple entities of the above semantic model instance is shown in Figure 2.
  • the sales order 101 produces the product 102
  • the process route 103 for producing the product 102 is obtained through process route query.
  • the product 102 has material requirements such as the raw material 104, where the raw material 104 learns the material 105 of the table top and the material 106 of the table leg after performing the material query.
  • the process route 103 includes a first operation step 107 and a second operation step 108. Among them, the first operation step 107 is performed by the first device 109 for equipment support, and the second operation step 108 is performed by the second device 110 for equipment support.
  • the first device 109 and the second device 110 belong to the first factory 111.
  • step S2 is executed to retrieve the attributes of the field data according to the type of the field data, extract the data from the semantic model instance according to the search result, and simulate the semantic model instance on the simulation platform.
  • the present invention in order to automatically generate semantic model instances, the present invention needs some key information from the factory.
  • the present invention needs to perform semantic retrieval to extract this information in the semantic model instance.
  • the present invention develops a query function library.
  • the query function library includes: machine retrieval, order retrieval, material retrieval, process route retrieval, etc.
  • the query function when performing semantic retrieval, includes multiple parameters.
  • the device connected to Star Manufacturing Ltd. includes a first device 109 and a second device 110.
  • the sales order and all its attributes are retrieved according to the date, and "January 1st" is entered into the semantic model retrieval function.
  • the sales order 101 and its attribute information can be queried, including the order number, arrival time, Expiration time, product number and quantity.
  • the raw materials and all their attributes are retrieved according to the product, and "table" is input into the semantic model retrieval function, and the raw materials 104 of the product 102 can be queried.
  • the properties of the raw materials 104 include the material name and the quantity of the material.
  • the attributes of the process route 103 include route number, operation number and operation name. Among them, the route number is "30001001", the first operation number is “OP10”, the second operation number is “OP20”, the operation name of the first operation is “drilling”, and the operation name of the second operation is "assembly”.
  • the information obtained by searching the semantic model of the present invention can be used to automatically generate and produce digital twins.
  • the production digital twin module receives a model generation request, it will trigger a semantic search in the semantic model instance.
  • the search results will be received through some communication methods between the production digital twin model and the knowledge graph module.
  • the production digital twin model receives the information obtained from the retrieval semantic model, some functional modules will perform the functions of the standard model.
  • Fig. 3 is a schematic diagram of a software interface of a simulation platform of a digital twin modeling mechanism according to a specific embodiment of the present invention.
  • the simulation platform 200 includes a simulation template SF.
  • the left side of the simulation template SF includes various element resource libraries for simulation, including device library L 1 , transmission library L 2 , material space S 3 and source space S 4 .
  • simulation template SF includes various functional modules that perform simulation functions cooperatively, including a semantic retrieval module 210, a device generation module 220, a material preparation module 230, an order management module 240, a process management module 250, a logic management module 260 and Key performance indicator module 270.
  • the simulation template SF is used for the visualization and simulation of the production of digital twins.
  • L 1 comprises a device library of different mounting point (assembly station) and detaching point (dismantle station) and the like. All operating points have the ability to download process routes based on the operation definition and processing part.
  • Means an element in the library means L 1 generates commands waiting means, upon receiving a command, i.e., the device is named elements presented in the simulation and the SF template.
  • the transmission library L 2 includes all the physical connections between the elements of the work point. Materials used for the production of the space S 3 twins digital receiving element material, which includes a default entity starting material (original material entity in default).
  • the material required for a task automatically generated for a specific production is a copy of the default initial material entity in the material space.
  • the material space includes a table, a table top, and a table leg.
  • the transfer library L 2 exemplarily includes a conveyor belt, an automatic mobile trolley transportation and a loading robot.
  • the simulation platform 200 is used to receive the digital twin automatic generation and simulation request, and reset the simulation template after the simulation task is completed.
  • the semantic retrieval module 210 is used to perform a series of query retrieval operations on the semantic model example, and the retrieval results are used to automatically generate digital twins.
  • the device generation module 220 is used to generate devices on the simulation template ST on the simulation platform 200 based on the device type and location.
  • the order management module 240 is used to download the order and its attribute parameters from the semantic model instance.
  • the material preparation module 230 is used to use the physical copy of the original material based on the order to set the product, raw material and the middle part of the material space on the simulation template ST.
  • the material preparation module 230 is also used for downloading raw materials to the source container in the simulation template ST based on the order sequence predefined by the order management module 240 in the semantic model instance.
  • the process management module 250 detects the parts executed at each work point and sends a command to the work point to establish the execution time, set time, and other process information of the work point before a part reaches the work point.
  • the logic management module 260 is used to define a part flow path from one work point to another work point, which has been defined in the process route.
  • the key performance indicator module 270 is used to record all the key performance indicators in the simulation process.
  • the process management module 250 also assigns the operation duration to the simulation template ST.
  • Fig. 4 is a schematic flowchart of a digital twin modeling mechanism according to a specific embodiment of the present invention. After performing the modeling step S1 and the simulation step S2 provided by the present invention, the production digital twin can be automatically generated.
  • the simulation platform 200 completes the initialization trigger after obtaining the production digital twin model generation and simulation request, and specifies the semantic retrieval module 210 to perform semantic retrieval and obtain retrieval results.
  • the process route of the semantic retrieval module 210 is obtained from the process management module 250.
  • the simulation SF template from the library apparatus is set in advance in a setting device L, and instructs the copy material preparation module 230 to prepare the starting material by the material S 3 solid material space and materials to provide simulation template SF.
  • the order management module 240 downloads orders using a predefined order sequence and provides them to the simulation template SF. Then, based on the order and material information, the raw materials are downloaded to the raw material container in the simulation template SF.
  • Means generating device type module 220 sends to the simulation apparatus attribute template SF, and select from the library apparatus L 1.
  • the semantic retrieval module 210 acquires materials from the material preparation module 230
  • the semantic retrieval module 210 acquires the order list from the order management module 240
  • the semantic retrieval module 210 acquires the device list from the device generation module 220
  • the semantic retrieval module 210 acquires the device list from the logic management module.
  • the simulation starts to trigger, and the process management module 250 specifies the device and automatically selects the process time before placing the product on the device in the simulation.
  • the logic management module 260 detects the manufacturing status in each device. After a device completes an operation based on a component, the logic management module 260 designates the production component to be transported to the downstream workstation based on the product-based process route.
  • the key performance indicator module 270 records key performance indicators for products, devices, and lines, including device utilization, line balance rate, productivity, and throughput.
  • the logic management module 260 selects the type of transmission mode from the transmission library L 2 , and the transmission library L 2 provides a physical connection between the workstations in the simulation template SF.
  • the process management module 250 assigns a route to the logic management module 260, and the logic management module 260 assigns connection attributes to the simulation template SF.
  • the simulation template SF reports the simulation status to the key performance indicator module 270, and sends the KPI output required by the customer.
  • the second aspect of the present invention provides a digital twin modeling and simulation system, which includes:
  • a processor and a memory coupled to the processor, the memory having instructions stored therein, which when executed by the processor cause the electronic device to perform actions, the actions including: S1, generating a manufacturing Model ontology, collecting field data, and generating semantic model instances based on the manufacturing model ontology and the field data; S2, retrieving the attributes of the field data according to the type of field data, and extracting the data from the semantic model instances according to the retrieval results Out, and simulate the semantic model instance on the simulation platform.
  • the simulation platform includes: a semantic retrieval module; a device generation module; a material preparation module; an order management module; a process management module; a logic management module; and a key performance indicator module.
  • the resource library of the simulation platform includes a device library, a transmission library, and a material space.
  • the step S2 also includes the following steps: the simulation platform completes the initialization trigger after obtaining the production digital twin model generation and simulation request, specifies the semantic retrieval module to perform semantic retrieval and obtains the retrieval result; the process route of the semantic retrieval module is from the process management Module acquisition, set the device from the preset device library in the simulation template, and instruct the material preparation module to prepare materials with a copy of the original material entity in the material space, and provide raw materials to the simulation template, and the order management module uses predefined The order sequence downloads the order and provides it to the simulation template; the simulation is triggered, the process management module specifies the device and automatically selects the process time before placing the product on the device in the simulation. The logic management module detects the manufacturing status in each device.
  • the logic management module specifies the product-based process route of the production component to be transported to the downstream workstation; when the simulation is triggered, the simulation template will simulate The status is reported to the key performance indicator module, and the key performance output required by the customer is sent.
  • the manufacturing model ontology includes individual, type, target attribute and data attribute.
  • the manufacturing model ontology is divided into sales orders, resources, tasks, equipment, materials, parts, operation steps, equipment collections, raw materials, products, process routes, factory areas, purchase orders, Production orders, companies and factories.
  • a third aspect of the present invention provides a digital twin modeling and simulation device, which includes: a modeling device that generates a manufacturing model body, collects field data, and generates a semantic model based on the manufacturing model body and the field data Example; a simulation device that retrieves the attributes of the field data according to the type of the field data, extracts the data from the semantic model instance according to the retrieval result, and simulates the semantic model instance on the simulation platform.
  • the fourth aspect of the present invention provides a computer program product that is tangibly stored on a computer-readable medium and includes computer-executable instructions that, when executed, cause at least one processor to Perform the method described in the first aspect of the present invention.
  • the fifth aspect of the present invention provides a computer-readable medium on which computer-executable instructions are stored, and when executed, the computer-executable instructions cause at least one processor to perform the method according to the first aspect of the present invention.
  • the digital twin modeling simulation mechanism provided by the present invention has the ability to be applied to all types of production and manufacturing, and it is based on a unified standard production and manufacturing ontology, which fully demonstrates the flexibility of the present invention.
  • the invention can reduce the dependence of senior experts in the field on the production modeling work, and can reduce the manpower input in the complex production of digital twins.

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Abstract

本发明提供了数字双胞胎建模仿真方法,其中,包括如下步骤:产生一个制造模型本体,采集现场数据,并基于所述制造模型本体和所述现场数据产生语义模型实例;根据现场数据的类型检索所述现场数据的属性,根据检索结果把数据从语义模型实例中提取出来,并在仿真平台上对所述语义模型实例进行仿真。本发明提供的数字双胞胎建模仿真机制具有广泛应用的灵活性,并降低了对本领域专家的依赖性。

Description

数字双胞胎建模仿真方法、装置和系统 技术领域
本发明涉及工业数字化,尤其涉及数字双胞胎建模仿真方法、装置和系统。
背景技术
随着工业4.0时代的到来,我们现在站在一个能够从根本上改变制造方式的技术革命开端上。工业4.0的核心技术是数字双胞胎(digital twin)。制造数字化帮助企业缩短生产设计周期,节省生产成本,提供数字服务。基于应用的不同,数字双胞胎能够在概念上分成三类,包括整个生产制造过程中的产品数字双胞胎(product digital twin),生产数字双胞胎(production digital twin)和设备数字双胞胎(equipment digital twin)。
现在,由于生产数字双胞胎在生产中和性能评估(performance evaluation)和操作改进(operation improvement)密切相关,生产数字双胞胎在制造数字化中扮演着越来越重要的角色。基于生产数字双胞胎,工厂的生产活动能够进行充分仿真,实际生产能力(actual production capacity)能够被评估,并且在生产过程中的动态瓶颈(dynamic bottleneck)也能确定。同时,生产过程能够给予生产数字双胞胎进行全局优化,包括时序安排(scheduling)和执行时间调整(operation time adjustment)等。然而,当制造者试图利用生产数字双胞胎,开发人员需要从工厂的不同层级反复人力收集数据,并且还需要在商业生产建模和软件环境仿真中基于收集的数据人力执行数字双胞胎的策划。这非常花时间,浪费了人力,提高了数据双胞胎开发的费用,并且对于数据双胞胎技术的发展和应用不利。
现有技术并没有关于生产数字双胞胎自动生成的解决方案。仅仅有一些商业软件开发者或者整合者尝试通过整个生产周期建立数据流主干,其中,所述生产周期包括:产品设计、过程设计和生产仿真。数据 流能够从一个平台到另一个平台导入和导出。例如,过程设计阶段数据能够导出到生产仿真软件执行模型自动生成,例如布局信息等。然而,上述解决方案具有数个缺点,其一是费用问题。上述方案需要购买整个工业过程的商业软件,一些小制造商无法承担软件购买费用。另一缺点是一些过时的生产线通过几轮升级导致最初过程设计数据并不再适合于生产数字化。因此,现有技术还没有建立数字双胞胎的建模和仿真机制,而不依赖于资深人员和领域专家。
发明内容
本发明第一方面提供了一种数字双胞胎建模仿真方法,其中,包括如下步骤:S1,产生一个制造模型本体,采集现场数据,并基于所述制造模型本体和所述现场数据产生语义模型实例;S2,根据现场数据的类型检索所述现场数据的属性,根据检索结果把数据从语义模型实例中提取出来,并在仿真平台上对所述语义模型实例进行仿真。
进一步地,所述仿真平台包括:语义检索模块;装置产生模块;材料准备模块;订单管理模块;工艺管理模块;逻辑管理模块;关键性能指标模块。
进一步地,所述仿真平台的资源库包括装置库、传输库、材料空间。
进一步地,所述步骤S2还包括如下步骤:仿真平台获得生产数字双胞胎模型产生和仿真请求后则完成初始化触发,指定语义检索模块执行语义检索并获得检索结果;语义检索模块的工艺路线从工艺管理模块获取,在仿真模板中从预先设定的装置库中设定装置,并指示材料准备模块在材料空间中用初始材料实体的副本准备材料,并提供原材料给仿真模板,订单管理模块用预先定义的订单序列下载订单,并提供给仿真模板;仿真开始触发,工艺管理模块指定装置并在仿真中将产品放在装置之前自动选择工艺时间。逻辑管理模块在每个装置中检测生产制造状态,当一个装置完成了基于一个部件的操作以后,逻辑管理模块指定生产部件基于产品的工艺路线运输到下流工作站;当仿真结束触发,仿真模板把仿真状态报告给关键性能指标模块,并发送客户所需关键性能输出。
进一步地,所述制造模型本体包括个体、类型、目标属性和数据属性。
进一步地,所述制造模型本体根据工厂现场数据的分类和属性分为销售订单、资源、任务、设备、材料、部件、操作步骤、设备集合、原材料、产品、工艺路线、工厂区域、采购订单、生产订单、企业和工厂。
本发明第二方面提供了一种数字双胞胎建模仿真系统,其包括:
处理器;以及与所述处理器耦合的存储器,所述存储器具有存储于其中的指令,所述指令在被处理器执行时使所述电子设备执行动作,所述动作包括:S1,产生一个制造模型本体,采集现场数据,并基于所述制造模型本体和所述现场数据产生语义模型实例;S2,根据现场数据的类型检索所述现场数据的属性,根据检索结果把数据从语义模型实例中提取出来,并在仿真平台上对所述语义模型实例进行仿真。
进一步地,所述仿真平台包括:语义检索模块;装置产生模块;材料准备模块;订单管理模块;工艺管理模块;逻辑管理模块;关键性能指标模块。
进一步地,所述仿真平台的资源库包括装置库、传输库、材料空间。
进一步地,所述步骤S2还包括如下步骤:仿真平台获得生产数字双胞胎模型产生和仿真请求后则完成初始化触发,指定语义检索模块执行语义检索并获得检索结果;语义检索模块的工艺路线从工艺管理模块获取,在仿真模板中从预先设定的装置库中设定装置,并指示材料准备模块在材料空间中用初始材料实体的副本准备材料,并提供原材料给仿真模板,订单管理模块用预先定义的订单序列下载订单,并提供给仿真模板;仿真开始触发,工艺管理模块指定装置并在仿真中将产品放在装置之前自动选择工艺时间。逻辑管理模块在每个装置中检测生产制造状态,当一个装置完成了基于一个部件的操作以后,逻辑管理模块指定生产部件基于产品的工艺路线运输到下流工作站;当仿真结束触发,仿真模板把仿真状态报告给关键性能指标模块,并发送客户所需关键性能输出。
进一步地,所述制造模型本体包括个体、类型、目标属性和数据属性。
进一步地,所述制造模型本体根据工厂现场数据的分类和属性分为销售订单、资源、任务、设备、材料、部件、操作步骤、设备集合、原材料、产品、工艺路线、工厂区域、采购订单、生产订单、企业和工厂。
本发明第三方面提供了一种数字双胞胎建模仿真装置,其中,包括: 建模装置,其产生一个制造模型本体,采集现场数据,并基于所述制造模型本体和所述现场数据产生语义模型实例;仿真装置,根据现场数据的类型检索所述现场数据的属性,根据检索结果把数据从语义模型实例中提取出来,并在仿真平台上对所述语义模型实例进行仿真。
本发明第四方面提供了一种计算机程序产品,所述计算机程序产品被有形地存储在计算机可读介质上并且包括计算机可执行指令,所述计算机可执行指令在被执行时使至少一个处理器执行本发明第一方面所述的方法。
本发明第五方面提供了计算机可读介质,其上存储有计算机可执行指令,所述计算机可执行指令在被执行时使至少一个处理器执行根据本发明第一方面所述的方法。
本发明提供的数字双胞胎建模仿真机制具有应用于所有类型的生产制造的能力,并且其是基于一个统一标准的生产制造本体,这充分说明了本发明的灵活性。本发明能够降低本领域资深专家在生产建模工作的依赖性,能够在复杂的生产数字双胞胎中降低人力投入。
附图说明
图1是根据本发明一个具体实施例的数字双胞胎建模机制的制造模型本体示意图;
图2是根据本发明一个具体实施例的数字双胞胎建模机制的语义模型实例示意图;
图3是根据本发明一个具体实施例的数字双胞胎建模机制的仿真平台的软件界面示意图;
图4是根据本发明一个具体实施例的数字双胞胎建模机制的流程示意图。
具体实施方式
以下结合附图,对本发明的具体实施方式进行说明。
本发明提供的数字双胞胎建模仿真机制分为建模和仿真两部分,其中,在建模部分,普通的制造模型本体用于描述生产制造所有方面的关系,并整合从工厂来的现场数据来建立基于制造模型本体的语义模型实 例。在仿真部分,语义检索功能用于提取有用的信息和结果,并将所述信息和结果传输至生产数字双胞胎仿真平台进行仿真。
其中,语义模型用于利用本体描述语言描述生产制造过程,被称为知识图(knowledge graph)。制造模型本体是预先定义的标准,例如ISA95标准等,其是广泛应用在生产制造领域的统一标准。具体地,大部分标准用于描述模型(models)、目标(objects)、活动(activities)以及生产制造中的整合(integeration in manufacturing)。每个标准基于特定的描述元素。
本发明提供的数字双胞胎建模仿真方法,其中,包括如下步骤:
首先执行步骤S1,根据本领域不同的协议产生一个制造模型本体,采集工厂现场数据,并基于所述制造模型本体和所述现场数据产生语义模型模板。
在本实施例中,假设制造模型本体是基于ISA95标准的。基于ISA95标准,制造模型本体由图1所示的语义模型示出。在图1所示的制造模型本体具有四个基本元素,包括个体(individuals)、类型(class)、目标属性(object property)和数据属性(data property)。其中,类型为基于ISA95标准的制造模型本体的核心元素,其是个体的抽象。目标属性用于描述不同类型之间的关系。数据属性用于描述类型特性。生产制造中的生产、操作、拓扑学用本体论网络语言(ontology web language)来规范,并整合到如图1所示的普通的制造模型本体。
如图1所示,基于所述ISA95标准的制造模型本体根据工厂现场数据的分类和属性分为销售订单(purchase order)、资源(resources)、任务(job)、设备(equipment)、材料(raw material)、部件(part)、操作步骤(operation)、设备集合(work unit)、原材料、产品(product)、工艺路线(routing)、工厂区域(area)、采购订单(sales order)、生产订单(order)、企业(enterprise)和工厂(site)。
具体地,如图1所示,销售订单1的属性包括采购订单编号、到达时间、到期时间、状态、产品编号和数量,生产订单2的属性包括订单编号、开始时间、结束时间、状态、产品编号和数量,产品3的属性包括产品编号和产品名称,原材料4的属性包括材料编号和材料数量,材料5的属性包括材料编号和材料名称,采购订单6的属性包括采购订单 号、采购日期、到达日、材料编号和数量部分,部件7的属性包括部分编号和部分名称,操作步骤8的属性包括操作类型、操作编号和操作名称,工艺路线9的属性包括路线编号、路线名称和操作步骤,任务10的属性包括任务类型、任务编号、任务名称、开始时间和结束时间,资源11的属性包括资源类型、资源编号和资源名称,装置12的属性包括装置类型、装置编号和装置名称,设备集合13的属性包括集合类型、集合编号和集合名称,工厂区域14的属性包括区域编号和区域名称,工厂15的属性包括工厂编号和工厂名称。企业16的属性包括企业编号和企业名称。
上述分类贯穿了整个生产制造的过程,例如,生产制造由收到一个销售订单1开始,然后将销售订单1转化为生产订单2,生产订单2用于生产产品3,产品3需要原材料4。其中,根据产品3的不同组成部分,材料5详细描述了整个产品的不同组成部分需要的具体材料。而具体材料通过采购订单6来采购。其中,部件7是整个产品的不同组成部分的中间生成部分,其由操作步骤8提供的单个生产步骤来制造。进一步地,操作步骤8被分解为多个的任务10。其中,产品3按照工艺路线9来生产制造。其中,任务10利用了不同的装置12,装置12属于设备集合13,设备集合13是同一个功能属性设备的集合。并且,设备结合13属于不同的工厂区域14,工厂区域14属于特定的工厂15,工厂15属于不同的企业16。
基于普通的制造模型本体,一个特定的工厂数据包括拓扑数据、装置数据、产品和生产数据,其能产生一个工厂实例。例如,工厂具有两台机器,一台机器用于钻孔作业(Drilling operation),另一台机器用于装配作业(Assembling operation)。该工厂涉及为制造桌子和椅子。桌子的工艺路线包括两个步骤:首先执行钻孔步骤然后执行装配步骤。工厂如果接到了100个桌子的销售订单。
因此,基于如图1所示制造模型本体和本实施例的现场数据产生语义模型实例,现场数据包括具体的销售订单、产品、工艺路线、原材料、材料、操作步骤、装置和工厂等,以及上述实体的属性。
图2所示出根据本发明一个具体实施例的语义模型实例,在本实施例的语义模型实例中包括多个实体以及实体属性,其中,实体包括:销 售订单101、产品102、工艺路线103、原材料104、材料105、材料106、操作步骤107、操作步骤108、第一装置109、第二装置110、第一工厂111。上述实体分别具有一个或多个实体属性。
如图2所示,工厂接到了100个桌子的销售订单101,销售订单101的属性包括订单编号、到达时间、到期时间、产品编号和数量,其中,订单编号为“80010111”,到达时间为“2019年1月1日上午11点”,到期时间为“2019年1月2日上午12点”,状态为“未开始”,产品编号为“10011111”,数量为“100”。这表示工厂接到了生产100个桌子的销售订单,制造还未开始,需要在“2019年1月1日上午11点到2019年1月2日上午12点”之间完成销售订单。销售订单101生产的是产品102,其中,产品102的属性包括产品编号和产品名称,其中,产品编号为“1001111”,产品名称为“桌子”。其中产品102的原材料104的属性包括材料名称和材料数量。原材料104又分为桌面的材料105和桌腿的材料106,其中,桌面的材料105的属性包括材料编号和材料名称,桌腿的材料106的属性包括材料编号和材料名称。进一步地,桌面的材料105的材料编号为“1000001”,桌面的材料105的名称为“桌面”,桌腿的材料106的材料编号为“1000002”,桌腿的材料106的材料名称为“桌腿”。
此外,产品102是基于工艺路线103来制造的,工艺路线103的属性包括路线编号、操作编号和操作名称。其中,路线编号为“30001001”,第一操作编号为“OP10”,第二操作编号为“OP20”,第一操作的操作名称为“打钻”,第二操作的操作名称为“装配”。工艺路线103包括第一操作步骤107和第二操作步骤108。进一步地,第一操作步骤107的属性包括操作名称和持续时间,其中,操作名称为“打钻”,持续时间为“5分钟”。进一步地,第二操作步骤108的属性包括操作名称和持续时间,其中,操作名称为“装配”,持续时间为“4.5分钟”。并且,第一操作步骤107是在第一装置109中执行,其中,第一装置109的装置编号为“2000100”,第一装置109的装置名称为“打钻站”。第二操作步骤108是在第一装置110中执行,其中,第二装置110的装置编号为“2000101”,第二装置110的装置名称为“装配站”。第一装置109和第二装置110都属于第一工厂111,第一工厂111的属性包括工厂名称,其中工厂名称为 “Star manufacturing Ltd.”。
上述语义模型实例的多个实体之间的关系如图2所示。具体地,销售订单101生产的是产品102,经过工艺路线查询获得生产产品102的工艺路线103。产品102具有材料需求如原材料104,其中,原材料104执行材料查询后获知桌面的材料105和桌腿的材料106。工艺路线103包括第一操作步骤107和第二操作步骤108。其中,第一操作步骤107由第一装置109来做装备支持,第二操作步骤108由第二装置110来做装备支持。所述第一装置109和第二装置110属于第一工厂111。
然后,执行步骤S2,根据现场数据的类型检索所述现场数据的属性,根据检索结果把数据从语义模型实例中提取出来,并在仿真平台上对所述语义模型实例进行仿真。
其中,为了自动产生语义模型实例,本发明需要一些来自工厂端的关键信息。本发明需要执行语义检索在语义模型实例中提取这些信息。为了,其中为了执行语义检索,本发明发展了查询函数库(query function library),查询函数库包括:机器检索,订单检索,材料检索和工艺路线检索等。其中,在执行语义检索时,查询函数包括多个参数。
其中,首先在如图2所示的语义模型实例中检索第一工厂111中所有的装置,也就是将所有连接到Star manufacturing Ltd.的装置检索并列举出来。具体地,连接到Star manufacturing Ltd.的装置包括第一装置109和第二装置110。
然后,在语义模型实例中按照日期检索销售订单及其所有属性,将“1月1日”输入语义模型检索函数,能够查询到销售订单销售订单101及其属性信息,包括订单编号、到达时间、到期时间、产品编号和数量。
接着,在语义模型实例中按照产品检索原材料及其所有属性,将“桌子”输入语义模型检索函数,能够查询到产品102的原材料104,原材料104的属性包括材料名称和材料数量。
最后,在语义模型实例中按照产品检索工艺路线,将“桌子”输入语义模型检索函数,能够查询到产品102是基于工艺路线103来制造的,工艺路线103的属性包括路线编号、操作编号和操作名称。其中,路线编号为“30001001”,第一操作编号为“OP10”,第二操作编号为“OP20”,第一操作的操作名称为“打钻”,第二操作的操作名称为“装配”。
本发明检索语义模型得到的信息会用于自动产生生产数字双胞胎。当生产数字双胞胎模块接收到一个模型产生要求,即会触发在语义模型实例中的语义检索。检索结果会在生产数字双胞胎模型和知识图谱模块之间通过一些通信方式接收。一旦生产数字双胞胎模型接收到了检索语义模型得到的信息,一些功能模块会执行规范模型的功能。
本发明在仿真平台上对语义模型实例执行仿真,其中,所述仿真平台可选地为二次开发以后的商业事件驱动仿真软件。图3是根据本发明一个具体实施例的数字双胞胎建模机制的仿真平台的软件界面示意图。如图2所示,仿真平台200包括一个仿真模板SF。在仿真模板SF的左边包括各种用于仿真的元素资源库,其中,包括装置库L 1、传输库L 2、材料空间S 3和源空间S 4。在仿真模板SF的右边包括各种协同执行仿真功能的功能模块,其中,包括语义检索模块210、装置产生模块220、材料准备模块230、订单管理模块240、工艺管理模块250、逻辑管理模块260和关键性能指标模块270。
其中,仿真模板SF用于生产数字双胞胎的可视化(visualization)和仿真。装置库L 1包括不同装配点(assembly station)和拆卸点(dismantle station)等。所有工作点具有基于计算定义(operation definition)和处理部分(processing part)的下载工艺路线的能力。装置元素在装置库L 1中等候装置产生命令,一旦接收到命令,装置元素即会被命名并在仿真模板SF中呈现。传输库L 2包括所有在工作点元素之间的物理连接。材料空间S 3用于为生产数字双胞胎容纳材料元素,其包括一个默认初始材料实体(original material entity in default)。在一个模型中,一个特定生产自动生成的任务所需材料是材料空间中的默认初始材料实体中的副本。例如,在本实施例中,包括桌子、桌面和桌腿等。其中,传输库L 2示例性地包括传送带、自动移动小车运输和装载机器人。
如图3所示,仿真平台200用于接收数字双胞胎自动生成和仿真请求,并再仿真任务完成以后重置仿真模板。其中,语义检索模块210用于执行再语义模型示例上执行一系列查询检索操作,检索结果用于生产数字双胞胎自动生成。装置产生模块220用于基于装置类型和位置在仿真平台200上的仿真模板ST上产生装置。订单管理模块240用于从语义模型实例中下载订单及其属性参数。材料准备模块230用于利用基于订 单的原始材料实体副本在仿真模板ST上设置产品、原材料和材料空间中部分。此外,材料准备模块230还用于在语义模型实例中基于订单管理模块240预先定义的订单序列下载原材料给仿真模板ST中的源容纳器(source container)。工艺管理模块250检测在每个工作点执行的部件并发送一个命令给工作点,以在一个部件到达工作点之前建立工作点的执行时间、设定时间以及其他工艺信息。逻辑管理模块260用于定义一个工作点到另一个工作点之间的部件流程通道(part flow path),其在工艺路线中已经定义好了。关键性能指标模块270用于记录在仿真过程的所有关键性能指标。工艺管理模块250把操作持续时间也指定给仿真模板ST。
图4是根据本发明一个具体实施例的数字双胞胎建模机制的流程示意图。在执行了本发明提供的建模步骤S1和仿真步骤S2以后则可以自动产生生产数字双胞胎。
如图4所示,仿真平台200获得生产数字双胞胎模型产生和仿真请求后则完成初始化触发,指定语义检索模块210执行语义检索并获得检索结果。语义检索模块210的工艺路线从工艺管理模块250获取。然后,在仿真模板SF中从预先设定的装置库L 1中设定装置,并指示材料准备模块230在材料空间S 3中用初始材料实体的副本准备材料,并提供原材料给仿真模板SF。此外,订单管理模块240用预先定义的订单序列下载订单,并提供给仿真模板SF。接着,基于订单和材料信息在仿真模板SF中下载原材料给原材料容器。装置产生模块220将装置属性发送给仿真模板SF,并从装置库L 1中选择装置类型。此外,语义检索模块210从材料准备模块230获取材料,语义检索模块210从订单管理模块240中获取订单列表,语义检索模块210从装置产生模块220中获取装置列表,语义检索模块210从逻辑管理模块260中获取装置采集表。
然后,仿真开始触发,工艺管理模块250指定装置并在仿真中将产品放在装置之前自动选择工艺时间。逻辑管理模块260在每个装置中检测生产制造状态,当一个装置完成了基于一个部件的操作以后,逻辑管理模块260指定生产部件基于产品的工艺路线运输到下流工作站。在仿真执行过程中,关键性能指标模块270为产品、装置和线路记录关键性能指标,包括装置利用,线路平衡速率,生产力和吞吐量。逻辑管理模 块260从传输库L 2为传输方式选取类型,传输库L 2在仿真模板SF中的工作站之间提供物理连接。工艺管理模块250指定路线给逻辑管理模块260,逻辑管理模块260将连接属性指定给仿真模板SF。
最后,当仿真结束触发,所有的关键性能指标都被发送回去做进一步分析,即仿真模板SF把仿真状态报告给关键性能指标模块270,并发送客户所需KPI输出。
本发明第二方面提供了一种数字双胞胎建模仿真系统,其包括:
处理器;以及与所述处理器耦合的存储器,所述存储器具有存储于其中的指令,所述指令在被处理器执行时使所述电子设备执行动作,所述动作包括:S1,产生一个制造模型本体,采集现场数据,并基于所述制造模型本体和所述现场数据产生语义模型实例;S2,根据现场数据的类型检索所述现场数据的属性,根据检索结果把数据从语义模型实例中提取出来,并在仿真平台上对所述语义模型实例进行仿真。
进一步地,所述仿真平台包括:语义检索模块;装置产生模块;材料准备模块;订单管理模块;工艺管理模块;逻辑管理模块;关键性能指标模块。
进一步地,所述仿真平台的资源库包括装置库、传输库、材料空间。
进一步地,所述步骤S2还包括如下步骤:仿真平台获得生产数字双胞胎模型产生和仿真请求后则完成初始化触发,指定语义检索模块执行语义检索并获得检索结果;语义检索模块的工艺路线从工艺管理模块获取,在仿真模板中从预先设定的装置库中设定装置,并指示材料准备模块在材料空间中用初始材料实体的副本准备材料,并提供原材料给仿真模板,订单管理模块用预先定义的订单序列下载订单,并提供给仿真模板;仿真开始触发,工艺管理模块指定装置并在仿真中将产品放在装置之前自动选择工艺时间。逻辑管理模块在每个装置中检测生产制造状态,当一个装置完成了基于一个部件的操作以后,逻辑管理模块指定生产部件基于产品的工艺路线运输到下流工作站;当仿真结束触发,仿真模板把仿真状态报告给关键性能指标模块,并发送客户所需关键性能输出。
进一步地,所述制造模型本体包括个体、类型、目标属性和数据属性。
进一步地,所述制造模型本体根据工厂现场数据的分类和属性分为 销售订单、资源、任务、设备、材料、部件、操作步骤、设备集合、原材料、产品、工艺路线、工厂区域、采购订单、生产订单、企业和工厂。
本发明第三方面提供了一种数字双胞胎建模仿真装置,其中,包括:建模装置,其产生一个制造模型本体,采集现场数据,并基于所述制造模型本体和所述现场数据产生语义模型实例;仿真装置,根据现场数据的类型检索所述现场数据的属性,根据检索结果把数据从语义模型实例中提取出来,并在仿真平台上对所述语义模型实例进行仿真。
本发明第四方面提供了一种计算机程序产品,所述计算机程序产品被有形地存储在计算机可读介质上并且包括计算机可执行指令,所述计算机可执行指令在被执行时使至少一个处理器执行本发明第一方面所述的方法。
本发明第五方面提供了计算机可读介质,其上存储有计算机可执行指令,所述计算机可执行指令在被执行时使至少一个处理器执行根据本发明第一方面所述的方法。
本发明提供的数字双胞胎建模仿真机制具有应用于所有类型的生产制造的能力,并且其是基于一个统一标准的生产制造本体,这充分说明了本发明的灵活性。本发明能够降低本领域资深专家在生产建模工作的依赖性,能够在复杂的生产数字双胞胎中降低人力投入。
尽管本发明的内容已经通过上述优选实施例作了详细介绍,但应当认识到上述的描述不应被认为是对本发明的限制。在本领域技术人员阅读了上述内容后,对于本发明的多种修改和替代都将是显而易见的。因此,本发明的保护范围应由所附的权利要求来限定。此外,不应将权利要求中的任何附图标记视为限制所涉及的权利要求;“包括”一词不排除其它权利要求或说明书中未列出的装置或步骤;“第一”、“第二”等词语仅用来表示名称,而并不表示任何特定的顺序。

Claims (15)

  1. 数字双胞胎建模仿真方法,其中,包括如下步骤:
    S1,产生一个制造模型本体,采集现场数据,并基于所述制造模型本体和所述现场数据产生语义模型实例;
    S2,根据现场数据的类型检索所述现场数据的属性,根据检索结果把数据从语义模型实例中提取出来,并在仿真平台上对所述语义模型实例进行仿真。
  2. 根据权利要求1所述的数字双胞胎建模仿真方法,其特征在于,所述仿真平台包括:
    -语义检索模块;
    -装置产生模块;
    -材料准备模块;
    -订单管理模块;
    -工艺管理模块;
    -逻辑管理模块;
    -关键性能指标模块。
  3. 根据权利要求2所述的数字双胞胎建模仿真方法,其特征在于,所述仿真平台的资源库包括装置库、传输库、材料空间。
  4. 根据权利要求1所述的数字双胞胎建模仿真方法,其特征在于,所述步骤S2还包括如下步骤:
    仿真平台获得生产数字双胞胎模型产生和仿真请求后则完成初始化触发,指定语义检索模块执行语义检索并获得检索结果;
    语义检索模块的工艺路线从工艺管理模块获取,在仿真模板中从预先设定的装置库中设定装置,并指示材料准备模块在材料空间中用初始材料实体的副本准备材料,并提供原材料给仿真模板,订单管理模块用预先定义的订单序列下载订单,并提供给仿真模板;
    仿真开始触发,工艺管理模块指定装置并在仿真中将产品放在装置之前自动选择工艺时间。逻辑管理模块在每个装置中检测生产制造状态,当一个装置完成了基于一个部件的操作以后,逻辑管理模块指定生产部件基于产品的工艺路线运输到下流工作站;
    当仿真结束触发,仿真模板把仿真状态报告给关键性能指标模块,并发送客户所需关键性能输出。
  5. 根据权利要求1所述的数字双胞胎建模仿真方法,其特征在于,所述制造模型本体包括个体、类型、目标属性和数据属性。
  6. 根据权利要求5所述的数字双胞胎建模仿真方法,其特征在于,所述制造模型本体根据工厂现场数据的分类和属性分为销售订单、资源、任务、设备、材料、部件、操作步骤、设备集合、原材料、产品、工艺路线、工厂区域、采购订单、生产订单、企业和工厂。
  7. 数字双胞胎建模仿真系统,其包括:
    处理器;以及
    与所述处理器耦合的存储器,所述存储器具有存储于其中的指令,所述指令在被处理器执行时使所述电子设备执行动作,所述动作包括:
    S1,产生一个制造模型本体,采集现场数据,并基于所述制造模型本体和所述现场数据产生语义模型实例;
    S2,根据现场数据的类型检索所述现场数据的属性,根据检索结果把数据从语义模型实例中提取出来,并在仿真平台上对所述语义模型实例进行仿真。
  8. 根据权利要求7所述的数字双胞胎建模仿真系统,其特征在于,所述仿真平台包括:
    -语义检索模块;
    -装置产生模块;
    -材料准备模块;
    -订单管理模块;
    -工艺管理模块;
    -逻辑管理模块;
    -关键性能指标模块。
  9. 根据权利要求8所述的数字双胞胎建模仿真系统,其特征在于,所述仿真平台的资源库包括装置库、传输库、材料空间。
  10. 根据权利要求7所述的数字双胞胎建模仿真系统,其特征在于,所述动作S2还包括如下步骤:
    仿真平台获得生产数字双胞胎模型产生和仿真请求后则完成初始化触发,指定语义检索模块执行语义检索并获得检索结果;
    语义检索模块的工艺路线从工艺管理模块获取,在仿真模板中从预先设定的装置库中设定装置,并指示材料准备模块在材料空间中用初始材料实体的副本准备材料,并提供原材料给仿真模板,订单管理模块用预先定义的订单序列下载订单,并提供给仿真模板;
    仿真开始触发,工艺管理模块指定装置并在仿真中将产品放在装置之前自动选择工艺时间。逻辑管理模块在每个装置中检测生产制造状态,当一个装置完成了基于一个部件的操作以后,逻辑管理模块指定生产部件基于产品的工艺路线运输到下流工作站;
    当仿真结束触发,仿真模板把仿真状态报告给关键性能指标模块,并发送客户所需关键性能输出。
  11. 根据权利要求7所述的数字双胞胎建模仿真系统,其特征在于,所述制造模型本体包括个体、类型、目标属性和数据属性。
  12. 根据权利要求11所述的数字双胞胎建模仿真系统,其特征在于,所述制造模型本体根据工厂现场数据的分类和属性分为销售订单、资源、任务、设备、材料、部件、操作步骤、设备集合、原材料、产品、工艺路线、工厂区域、采购订单、生产订单、企业和工厂。
  13. 数字双胞胎建模仿真装置,其中,包括:
    建模装置,其产生一个制造模型本体,采集现场数据,并基于所述制造模型本体和所述现场数据产生语义模型实例;
    仿真装置,其根据现场数据的类型检索所述现场数据的属性,根据检索结果把数据从语义模型实例中提取出来,并在仿真平台上对所述语义模型实例进行仿真。
  14. 计算机程序产品,所述计算机程序产品被有形地存储在计算机可读介质上并且包括计算机可执行指令,所述计算机可执行指令在被执行时使至少一个处理器执行根据权利要求1至6中任一项所述的方法。
  15. 计算机可读介质,其上存储有计算机可执行指令,所述计算机可执行指令在被执行时使至少一个处理器执行根据权利要求1至6中任一项所述的方法。
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