WO2022061518A1 - Method and apparatus for generating and utilizing knowledge graph of manufacturing simulation model - Google Patents

Method and apparatus for generating and utilizing knowledge graph of manufacturing simulation model Download PDF

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Publication number
WO2022061518A1
WO2022061518A1 PCT/CN2020/116840 CN2020116840W WO2022061518A1 WO 2022061518 A1 WO2022061518 A1 WO 2022061518A1 CN 2020116840 W CN2020116840 W CN 2020116840W WO 2022061518 A1 WO2022061518 A1 WO 2022061518A1
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data
graph
model
manufacturing
simulation model
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PCT/CN2020/116840
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French (fr)
Chinese (zh)
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曹佃松
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西门子股份公司
西门子(中国)有限公司
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Priority to CN202080104959.3A priority Critical patent/CN116171453A/en
Priority to PCT/CN2020/116840 priority patent/WO2022061518A1/en
Publication of WO2022061518A1 publication Critical patent/WO2022061518A1/en

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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04Manufacturing
    • 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
    • Y02P90/30Computing systems specially adapted for manufacturing

Definitions

  • the present disclosure relates to the technical field of industrial manufacturing, and more particularly, to methods, apparatuses, computing devices, computer-readable storage media, and program products for generating and utilizing knowledge graphs for manufacturing simulation models.
  • simulation models play a very important role in industry.
  • simulation models can simulate the warehousing, transportation, and production processes in the plant to help manage the storage and transportation of materials, identify production bottlenecks, and increase capacity.
  • the job of creating or maintaining a manufacturing simulation model (such as changing the model structure or parameters) is usually done by simulation engineers who are familiar with simulation software and model formats. Collaboration with simulation engineers is required when other plant personnel unfamiliar with simulation software want to obtain simulation results from a manufacturing simulation model.
  • a first embodiment of the present disclosure proposes a method for generating and utilizing a knowledge graph of a manufacturing simulation model, including: obtaining a manufacturing simulation model in a factory and generating model representation data based on the manufacturing simulation model; obtaining a manufacturing simulation model for The preset graph semantics of the Graph semantics provides the semantic description of the knowledge graph of the manufacturing simulation model; and the data corresponding to the graph semantics is extracted from the model representation data as the graph data of the knowledge graph to form the knowledge graph.
  • the manufacturing simulation model is converted into a knowledge graph through the preset graph semantics, and the knowledge of the manufacturing simulation model can be saved and managed in a unified format, which is helpful for the digitization of discrete manufacturing plants.
  • the knowledge graph of the manufacturing simulation model also provides a unified way of knowledge acquisition for machines and humans, and improves the efficiency of knowledge acquisition.
  • converting manufacturing simulation models in different formats into model representation data in a unified format, and then converting them into knowledge graph graph data can easily realize the conversion between manufacturing simulation models and knowledge graphs.
  • a second embodiment of the present disclosure proposes an apparatus for generating and utilizing a knowledge graph of a manufacturing simulation model, including: a model data generating unit configured to obtain a manufacturing simulation model in a factory and generate a model based on the manufacturing simulation model Representation data; a graph semantic obtaining unit configured to obtain a preset graph semantic for manufacturing the simulation model, the graph semantic providing a semantic description of the knowledge graph of the manufacturing simulation model; and a graph data extracting unit configured to extract from the graph The data corresponding to the semantics of the graph is extracted from the model representation data as the graph data of the knowledge graph to form the knowledge graph.
  • a third embodiment of the present disclosure proposes a computing device comprising: a processor; and a memory for storing computer-executable instructions that, when executed, cause the processor to execute the first embodiment method in .
  • a fourth embodiment of the present disclosure proposes a computer-readable storage medium having computer-executable instructions stored thereon for performing the method of the first embodiment.
  • a fifth embodiment of the present disclosure proposes a computer program product tangibly stored on a computer-readable storage medium and comprising computer-executable instructions that, when executed, cause at least one process The controller executes the method of the first embodiment.
  • FIG. 1 illustrates a method for generating and utilizing a knowledge graph of a manufacturing simulation model according to some embodiments of the present disclosure
  • FIG. 2 shows a system architecture diagram for implementing the method in FIG. 1 according to an embodiment of the present disclosure
  • FIG. 3 shows a flowchart of a method for generating a knowledge graph of a production line simulation model in the embodiment of FIG. 2;
  • Figure 4 shows a flowchart of a method for improving a production line simulation model in the embodiment of Figure 2;
  • FIG. 5 shows a flowchart of a method for querying a knowledge graph of a production line simulation model in the embodiment of FIG. 2;
  • FIG. 6 shows a flowchart of a method for modifying a manufacturing simulation model using the knowledge graph of the production line simulation model in the embodiment of FIG. 2;
  • Fig. 7 shows the schematic diagram of a production line simulation model in the embodiment of Fig. 2;
  • Fig. 8 shows the schematic diagram of the knowledge graph of the production line simulation model in Fig. 7;
  • FIG. 9 illustrates an apparatus for generating and utilizing a knowledge graph of a manufacturing simulation model according to an embodiment of the present disclosure.
  • FIG. 10 illustrates a block diagram of a computing device for generating and utilizing a knowledge graph of a manufacturing simulation model according to one embodiment of the present disclosure.
  • the terms “including”, “comprising” and similar terms are open-ended terms, ie, “including/including but not limited to,” meaning that other content may also be included.
  • the term “based on” is “based at least in part on.”
  • the term “one embodiment” means “at least one embodiment”; the term “another embodiment” means “at least one additional embodiment” and so on.
  • step 101 a manufacturing simulation model in the factory is obtained and model representation data is generated based on the manufacturing simulation model.
  • the manufacturing simulation model may be any type of simulation model involved in the manufacturing activities of the factory, including but not limited to a production line model, a warehousing model, an in-shop logistics model, and the like. These manufacturing simulation models can be created by different simulation software and in different formats, and they can be stored at any storage device (eg, a server) within the factory.
  • the manufacturing simulation model is used to simulate the actual manufacturing process corresponding to it.
  • the production line model is used to simulate the production process of the actual production line
  • the warehousing model is used to simulate the warehousing, storage and delivery processes of goods (such as materials, workpieces, etc.)
  • the in-shop logistics model is used to transport the goods. process is simulated.
  • the internal data of the manufacturing simulation model usually includes the corresponding data used in the actual manufacturing process, including but not limited to the type, name, attribute value, connection sequence between the components, etc. of each component of the manufacturing simulation model.
  • its internal data includes production workstations (such as assembly stations, testing stations, packaging stations, processing stations, etc.), transfer workstations (such as conveyor belts, AGVs, robots, labor, etc.), temporary storage areas, Types and names of components such as supply stations, recycling stations, etc., their attribute values (such as spatial location, preparation time, processing time, processing capacity, additional tools or materials, personnel requirements, etc.), and the sequence of connections between them.
  • the model representation data also includes the contents of the above-mentioned internal data.
  • the low-level format can be, for example, JSON format or XML format.
  • a preset graph semantics for manufacturing the simulation model is obtained, and the graph semantics provides a semantic description of the knowledge graph of the manufacturing simulation model.
  • a unified graph database can be established for one or more factories to centralize all knowledge within the factory. Define common graph semantics for this graph database.
  • Graph Semantics provides a semantic description of the domain's knowledge graph.
  • Graph semantics includes all entity categories, subcategories and attributes related to the domain, and relationship categories and attributes between entities.
  • a realm can be a collection of several similar objects, for example, a collection of several similar production lines can be called a realm.
  • Entities serve as vertices of the knowledge graph, and relationships between entities serve as edges connecting vertices.
  • the entities are associated through relationships to form a hierarchical structure of the knowledge graph. It should be noted that entity categories, subcategories and attributes, and relationship categories and attributes between entities may be different for different domains.
  • the entity categories in the knowledge graph can include production workstations, transfer workstations, staging areas, supply stations, recycling stations, etc. in the production line. Attributes of an entity can vary depending on the entity class.
  • the properties of a production workstation can include name, spatial location, preparation time, and processing time
  • the properties of a transfer station can include name, spatial location, and transfer speed
  • the properties of a staging area can include name, spatial location, and storage capacity
  • the properties of the recycle bin can include the name, space location and material supply interval
  • the properties of the recycle bin can include the name, space location and processing time.
  • a relationship category between entities may include connection relationships between entities, such as connected previous and subsequent entities. At the same time, the connection relationship can represent the production sequence of the products to be produced.
  • step 103 data corresponding to the semantics of the graph is extracted from the model representation data as graph data of the knowledge graph to form the knowledge graph.
  • graph semantics includes all entity categories, subcategories and attributes related to the domain, and relationship categories and attributes between entities.
  • corresponding data can be extracted from the model representation data according to the preset graph semantics used to manufacture the simulation model to generate graph data.
  • Graph semantics and model representation data have different definitions for data categories, that is, for entities or relationships of a certain category in graph semantics, they are identified by another category in model representation data.
  • the mapping relationship between the entity category and relationship category of the graph semantics and the data category in the model representation data can be established in advance, and then the corresponding data can be extracted from the model representation data according to the mapping relationship as the graph data used to generate the knowledge graph. .
  • the graph data for forming the knowledge graph can be easily searched and extracted from the model representation data.
  • the knowledge graph can be formed and saved using the generated graph data through any existing graph database.
  • the graph database may also hold other knowledge graphs related to one or more factories, eg, knowledge graphs of production records, knowledge graphs related to production line maintenance, etc. .
  • knowledge graphs of production records e.g., knowledge graphs related to production line maintenance, etc.
  • production line maintenance e.g., production line maintenance, etc.
  • the method 100 further includes (not shown in FIG. 1 ): obtaining an updated knowledge graph of the manufacturing simulation model; reading graph data of the updated knowledge graph, and generating an updated knowledge graph based on the read graph data model representation data; and updating the manufacturing simulation model with the updated model representation data.
  • the manufacturing simulation model in addition to being able to generate a knowledge graph of the manufacturing simulation model, can also be updated according to the updated knowledge graph.
  • the graph data in the knowledge graph can be directly modified, for example, changing the attribute value of the entity and the relationship between the entities in the knowledge graph, adding an entity or relationship, deleting an entity or relationship, and so on.
  • the graph data of the updated knowledge graph is read and the updated model representation data is generated. Similar to generating the graph data according to the model representation data, the required data can be extracted from the updated graph data as the updated graph data according to the mapping relationship between the entity categories and relation categories of the pre-established graph semantics and the data categories in the model representation data. Models represent data. Thereafter, the manufacturing simulation model is updated with the updated model representation data. The manufacturing simulation model can be regenerated from the updated model representation data, or only the changed data can be modified in the manufacturing simulation model.
  • updating the manufacturing simulation model with the updated model representation data further comprises: comparing the updated model representation data with the previous model representation data; determining a difference between the updated model representation data and the previous model representation data based on the comparison results difference data between; and modify the manufacturing simulation model based on the difference data.
  • the previous model representation data is the graph data before the knowledge graph is updated.
  • the difference data between the updated model representation data and the previous model representation data may be entities, relationships between entities, and/or attribute values of entities or relationships.
  • the difference data can be that one or more attribute values (such as preparation time and processing time, etc.) of the production workstation have changed, the transfer workstation and its connection to other workstations have been added, and the two production workstations have changed.
  • connection relationship between them has changed (meaning that the processing steps of the product to be produced have changed) and so on.
  • the corresponding internal data in the manufacturing simulation model can be modified to update the manufacturing simulation model. Modifying only the changed internal data in the manufacturing simulation model avoids potential errors in regenerating the manufacturing simulation model, increases the accuracy of the updated model, and increases the speed of model update without the need for cross-departmental collaboration.
  • the method 100 before obtaining the updated knowledge graph of the manufacturing simulation model, the method 100 further includes (not shown in FIG. 1 ): according to the graph data of the knowledge graph, determining whether there is a model update associated with the manufacturing simulation model data; and when there is model update data, modify the graph data with the model update data to form an updated knowledge graph.
  • Entities and relationships between entities in the graph data can be read and model update data, ie new entities, relationships and/or their new attribute values, can be looked up in other knowledge sources based on the entities and relationships.
  • the model update data After the model update data is found, the corresponding entities, relationships and/or their attribute values can be modified in the graph data to update the graph data, so as to form an updated knowledge graph.
  • the model update data may be data from other external data sources stored in any storage device in the factory, or may be the graph data of other knowledge graphs stored in the same graph database as the knowledge graph of the manufacturing simulation model.
  • Model update data can be any data that needs to be updated in the manufacturing simulation model, for example, actual manufacturing data corresponding to the manufacturing simulation model, expected modification data in the manufacturing simulation model (such as adding, deleting, and/or modifying components in the manufacturing simulation model). parts, connection order and/or attribute values, etc.), etc.
  • the model update data is actual manufacturing data
  • modifying the map data using the model update data further includes: judging whether the actual manufacturing data is the same as the corresponding data in the map data; and when the actual manufacturing data is the same as the map data When the corresponding data is different, replace the corresponding data in the map data with the actual manufacturing data.
  • the actual manufacturing data is the data generated in the actual manufacturing process corresponding to the manufacturing simulation model.
  • the actual manufacturing data may be one or more actual attribute values (eg, actual setup time, processing time, equipment failure rate, etc.) of the production workstations of the production line.
  • Actual manufacturing data may, for example, come from external data sources such as production records.
  • the actual manufacturing data can be automatically read and compared with the corresponding data in the map data to determine whether they are the same. If the actual manufacturing data is different from the corresponding data in the graph data, the corresponding data in the graph data is replaced with the actual manufacturing data, ie entities, relationships and/or their attribute values are replaced with actual values.
  • Using actual manufacturing data to automatically update the manufacturing simulation model at the knowledge graph level keeps the manufacturing simulation model up-to-date, thereby increasing the model update speed and enabling more accurate simulation of the actual manufacturing process.
  • the model update data is expected modification data
  • the method 100 further includes (not shown in FIG. 1 ): performing a simulation using the updated manufacturing simulation model; and determining whether to apply the expected modification in the factory based on the simulation results data.
  • the expected modification data may be the addition, deletion and/or modification of components, connection sequences and/or property values in the manufacturing simulation model.
  • the expected modification data may be the addition of production workstations to the actual production line, the modified connection sequence, and/or the value of one or more attributes of the production workstations (eg, setup time, processing time, equipment failure rate, etc.).
  • the expected modification data may be obtained automatically by the AI program from external data sources such as the Internet or other documents, for example.
  • the simulation is performed again using the updated manufacturing simulation model, thereby obtaining a new simulation result of the manufacturing simulation model after applying the expected modification data.
  • Whether to apply the expected modification data in the factory can be judged based on the new simulation results and other conditions (such as how easy it is to apply the expected modification data to the factory, the number of products that may affect production, the time and economic costs required, etc.) . For example, for a production line simulation model, it is expected that a certain production workstation in the production line is replaced with another production workstation with a shorter processing time, and the expected modification data is the processing time of the other production workstation.
  • the method 100 further includes (not shown in FIG. 1 ): obtaining query conditions for the manufacturing simulation model; and reading graph data matching the query conditions from the knowledge graph.
  • any knowledge related to the manufacturing simulation model can also be queried from the knowledge graph. For example, for a production line simulation model, the number of production workstations in the production line, the number of process steps required to produce a product, whether a failure of a production workstation will affect product production, and so on can be queried from its knowledge graph.
  • the graph database storing the knowledge graph of the manufacturing simulation model also includes other knowledge graphs related to the factory (such as product-related knowledge graphs, knowledge graphs of production records, etc.), it is also possible to use multiple knowledge graphs to query more knowledge .
  • the product-related knowledge graph and the knowledge graph of the production line simulation model can be used to query the products and their quantities that will be affected during the maintenance period of a production workstation, and the knowledge graph and production line of production records can be used.
  • the knowledge graph of the simulation model queries the products that have been affected during the failure period of a production station, their quantities, and so on.
  • the content to be queried is decomposed into query conditions associated with the graph data in the knowledge graph, and then the graph data matching the query conditions is read from the knowledge graph.
  • the query conditions can be the entity category (workstation) of the test station and the entity name (containing the word "test").
  • the number of test stations can be obtained by counting the queried entities.
  • method 100 may be performed by a server device in communication with a client device used by an engineer. A request is made by the client device, and a knowledge graph of the manufacturing simulation model is generated, the knowledge graph is queried, or the manufacturing simulation model is updated at the server device in response to the request. In other embodiments, the method 100 may also be performed directly by the client device.
  • the manufacturing simulation model is converted into a knowledge graph through the preset graph semantics, so that the knowledge of the manufacturing simulation model can be saved and managed in a unified format, which is helpful for the digitization of discrete manufacturing plants.
  • the knowledge graph of the manufacturing simulation model also provides a unified way of knowledge acquisition for machines and humans, and improves the efficiency of knowledge acquisition.
  • converting manufacturing simulation models in different formats into model representation data in a unified format, and then converting them into knowledge graph graph data can easily realize the conversion between manufacturing simulation models and knowledge graphs.
  • FIG. 2 shows a system architecture diagram for implementing the method in FIG. 1 according to an embodiment of the present disclosure.
  • FIG. 3 shows a flowchart of a method for generating a knowledge graph of a production line simulation model in the embodiment of FIG. 2 .
  • FIG. 4 shows a flowchart of a method for improving a production line simulation model in the embodiment of FIG. 2 .
  • FIG. 5 shows a flowchart of a method for querying a knowledge graph of a production line simulation model in the embodiment of FIG. 2 .
  • FIG. 6 shows a flowchart of a method for modifying a production line simulation model using the knowledge graph of the production line simulation model in the embodiment of FIG. 2 . This embodiment will be described with reference to FIGS. 2 to 6 at the same time.
  • step 301 includes defining a common graph semantics 202 for the graph database 203 by the graph semantics definition unit 201 .
  • the graph database 203 is used to save all knowledge graphs of the entire factory or enterprise, for example, knowledge graphs of production records, knowledge graphs related to products, knowledge graphs of production line simulation models, and so on.
  • the common graph semantics 202 provides a semantic description of the knowledge graph for the production line simulation model.
  • Common graph semantics 202 include entity categories, subcategories and attributes, relationship categories and attributes between entities. Tables 1 and 2 below show an example of a portion of the graph semantics for a production line simulation model.
  • the entity categories for the graph semantics of the production line simulation model include production stations, supply stations, and recycle stations, and the relation categories include connections.
  • the properties of the production station include name, x-coordinate, y-coordinate, preparation time and processing time
  • the properties of the supply station include the name, x-coordinate, y-coordinate and material supply interval
  • the properties of the recycling station include the name, x-coordinate, y-coordinate and processing time.
  • step 302 the production line simulation model 204 is obtained by the model connector 205 and model representation data 206 is generated based on the production line simulation model 204 .
  • Model connector 205 may be implemented as a plug-in in simulation software.
  • Figure 7 shows a schematic diagram of a production line simulation model.
  • the supply station 701 and the recycle station 707 go through five production stations in the order of product processing: assembly station 702, pre-test station 703, first test station 704, second test station 705 and packaging Stop 706.
  • the first test station 704 and the second test station 705 are test stations for simultaneous testing, indicating that the product to be produced can pass through one of them.
  • model representation data 206 is read from the production line simulation model 204 and converted into model representation data 206 in JSON file format.
  • the model representation data 206 may also be stored in other formats, such as XML format. Table 3 below lists some examples of model representation data 206 .
  • step 303 common graph semantics 202 for the production line simulation model 204 are obtained.
  • the common graph semantics 202 provides a semantic description of the knowledge graph of the production line simulation model 204 .
  • the graph connector 207 extracts data corresponding to the graph semantics 202 from the model representation data 206 as graph data of the knowledge graph 208 .
  • the mapping relationship between the entity categories of the graph semantics 202 and the data categories in the model representation data 206 is pre-established in the graph connector 207 .
  • the entity categories "supply station” and “recycling station” defined in the graph semantics 202 correspond to “supply station” and “recycling station”, respectively, in the model representation data 206
  • the entity category "production station” Corresponding to "single processing station” in the model representation data 206
  • the relationship class "connection” corresponds to "connection” in the model representation data 206
  • the graph connector 207 extracts corresponding data from the model representation data 206 as graph data according to the mapping relationship.
  • TinkerPop is used as the graph database, and the graph data may be a script file for execution by TinkerPop.
  • the graph database 203 uses the graph data to form and store the knowledge graph 208 of the production line simulation model 204 .
  • the knowledge graph 800 includes a supply station 801 , an assembly station 802 , a pre-test station 803 , a first test station 804 , a second test station 805 , a packaging station 806 and a recycling station 807 .
  • the arrows between these workstations indicate the connection between them, and also indicate the production sequence of the products.
  • the method 400 may also be performed to improve the production line simulation model 204 .
  • the model improvement module 209 judges whether there is actual manufacturing data related to the production line simulation model 204 according to the map data of the knowledge map 208, for example, the production line simulation model 204 stored in the graph database 203 The latest production record of the corresponding production line.
  • Production records usually include actual attribute values of each workstation, such as actual preparation time, actual processing time, actual failure rate, and so on.
  • step 402 when there is actual manufacturing data, the model improvement module 209 judges whether the actual manufacturing data is the same as the corresponding data in the map data, for example, the actual preparation time of a certain processing workstation and the corresponding preparation time in the knowledge map 208 Compare.
  • step 403 when the actual manufacturing data is different from the corresponding data in the graph data, the model improvement module 209 replaces the corresponding data in the graph data with the actual manufacturing data, thereby updating the knowledge graph 208.
  • step 404 the updated knowledge graph 208 of the production line simulation model 204 is obtained by the graph connector 207, the graph data is read, and the model representation data 206 is updated based on the graph data. Similar to generating the graph data from the model representation data 206 , the required data is extracted from the updated graph data according to the mapping relationship between the entity categories of the graph semantics 202 and the data categories in the model representation data 206 .
  • step 405 the updated model representation data 206 is compared with the previous model representation data 206 by the model connector 205 .
  • step 406 discrepancy data between the updated model representation data 206 and the previous model representation data 206 is determined by the model connector 205 based on the comparison results, eg, different attribute values of a certain production workstation.
  • step 407 the model connector 205 modifies the production line simulation model 204 according to the difference data, that is, modifies the internal data of the model.
  • step 501 includes obtaining query conditions for the production line simulation model 204 by the graph query module 210 .
  • the query condition can be obtained by decomposing the query content.
  • the content to be queried may include the number of production workstations in the actual production line, the number of process steps required to produce products, and the like.
  • the query conditions vary according to the content to be queried.
  • the graph query module 210 reads graph data matching the query condition from the knowledge graph. After that, in step 503, the graph query module 210 performs post-processing on the read graph data to generate a query result. Post-processing may be, for example, merging the read atlas data, further processing the read atlas data, and the like.
  • step 601 includes determining, by the model modification module 211 , whether there is expected modification data related to the production line simulation model 204 according to the graph data of the knowledge graph 208 .
  • the expected modification data may be an added production station in the production line, a modified connection sequence, and/or one or more attribute values of a production station. Expected modification data can be obtained from external data sources such as the Internet or documents.
  • the model modification module 211 updates the knowledge graph 208 with the expected modification data, that is, replaces the corresponding data in the knowledge graph 208 with the expected modification data.
  • the model modification module 211 determines that a new test station with a processing time of 120 seconds can be used to replace the second test station in the production line, then the new processing time (120 seconds) can be used. ) in place of the original processing time (140 seconds) of the second test station in the knowledge graph 208.
  • Steps 603 - 606 in method 600 are the same as steps 404 - 407 in method 400 .
  • the updated knowledge graph 208 of the production line simulation model 204 is obtained by the graph connector 207, the graph data is read, and the model representation data 206 is updated based on the graph data.
  • the updated model representation data 206 is compared with the previous model representation data 206 by the model connector 205 .
  • discrepancy data between the updated model representation data 206 and the previous model representation data 206 is determined by the model connector 205 based on the comparison results.
  • the production line simulation model 204 is modified by the model connector 205 according to the difference data.
  • step 607 a simulation is performed by simulation software (not shown in FIG. 2) using the updated production line simulation model 204.
  • the application judgment module (not shown in FIG. 2 ) judges whether to apply the expected modification data in the actual production line according to the simulation result. Still taking the production line simulation model 700 in FIG. 7 as an example, after the second test station in the production line simulation model 204 is replaced with a new test station, the production capacity simulation result of the production line simulation model 204 increases from 1149 products/day to 1231 product/day.
  • the application judgment module judges whether to replace the second test station in the actual production line with a new test station according to changes in the simulation results and other preset conditions (such as the difficulty of replacement, the output that will be affected during the replacement process, etc.).
  • the manufacturing simulation model is converted into a knowledge graph through the preset graph semantics, so that the knowledge of the manufacturing simulation model can be saved and managed in a unified format, which is helpful for the digitization of discrete manufacturing plants.
  • the knowledge graph of the manufacturing simulation model also provides a unified way of knowledge acquisition for machines and humans, and improves the efficiency of knowledge acquisition.
  • converting manufacturing simulation models in different formats into model representation data in a unified format, and then converting them into knowledge graph graph data can easily realize the conversion between manufacturing simulation models and knowledge graphs.
  • FIG. 9 illustrates an apparatus for generating and utilizing a knowledge graph of a manufacturing simulation model according to one embodiment of the present disclosure.
  • the apparatus 900 includes a model data generating unit 901 , a graph semantics obtaining unit 902 and a graph data extracting unit 903 .
  • the model data generating unit 901 is configured to obtain a manufacturing simulation model in a factory and generate model representation data based on the manufacturing simulation model.
  • the graph semantics obtaining unit 902 is configured to obtain preset graph semantics for manufacturing the simulation model, the graph semantics providing a semantic description of the knowledge graph of the manufacturing simulation model.
  • the graph data extraction unit 903 is configured to extract data corresponding to the graph semantics from the model representation data as graph data of the knowledge graph to form the knowledge graph.
  • Each unit in FIG. 9 may be implemented by software, hardware (eg, integrated circuit, FPGA, etc.), or a combination of software and hardware.
  • the apparatus 900 further includes a graph obtaining unit, a model data updating unit, and a model updating unit (not shown in FIG. 9 ).
  • the graph obtaining unit is configured to obtain an updated knowledge graph of the manufacturing simulation model.
  • the model data updating unit is configured to read graph data of the updated knowledge graph, and generate updated model representation data based on the read graph data.
  • the model update unit is configured to update the manufacturing simulation model with the updated model representation data.
  • the model update unit is further configured to: compare the updated model representation data with the previous model representation data; determine difference data between the updated model representation data and the previous model representation data based on the comparison result; As well as modifying the manufacturing simulation model based on the variance data.
  • the apparatus 900 further includes an update data determination unit and a map data modification unit (not shown in FIG. 9 ).
  • the update data judgment unit is configured to judge whether there is model update data associated with the manufacturing simulation model according to the graph data of the knowledge graph.
  • the graph data modification unit is configured to modify the graph data using the model update data to form an updated knowledge graph.
  • the map data modification unit is further configured to: determine whether the actual manufacturing data is the same as the corresponding data in the map data; When the corresponding data is different, replace the corresponding data in the map data with the actual manufacturing data.
  • the model update data is expected modification data
  • the apparatus 900 further includes a model simulation unit and an application judgment unit (not shown in FIG. 9 ).
  • the model simulation unit is configured to simulate with the updated manufacturing simulation model.
  • the application judgment unit is configured to judge whether to apply the expected modification data in the factory based on the simulation result.
  • the apparatus 900 further includes a query condition obtaining unit and a graph data reading unit (not shown in FIG. 9 ).
  • the query condition obtaining unit is configured to obtain query conditions for the manufacturing simulation model.
  • the graph data reading unit is configured to read graph data matching the query condition from the knowledge graph.
  • the knowledge graph of the manufacturing simulation model is maintained in a graph database, and the graph database is also used to maintain other knowledge graphs related to the plant.
  • a computing device 1000 for generating and utilizing a knowledge graph of a manufacturing simulation model includes a processor 1001 and a memory 1002 coupled to the processor 1001 .
  • the memory 1002 is used for storing computer-executable instructions, and when the computer-executable instructions are executed, causes the processor 1001 to perform the methods in the above embodiments.
  • the above-described method can be implemented by a computer-readable storage medium.
  • the computer-readable storage medium carries computer-readable program instructions for carrying out various embodiments of the present disclosure.
  • a computer-readable storage medium may be a tangible device that can hold and store instructions for use by the instruction execution device.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Non-exhaustive list of computer readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM) or flash memory), static random access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically coded devices, such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above.
  • RAM random access memory
  • ROM read only memory
  • EPROM erasable programmable read only memory
  • flash memory static random access memory
  • SRAM static random access memory
  • CD-ROM compact disk read only memory
  • DVD digital versatile disk
  • memory sticks floppy disks
  • mechanically coded devices such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above.
  • Computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (eg, light pulses through fiber optic cables), or through electrical wires transmitted electrical signals.
  • the present disclosure presents a computer-readable storage medium having computer-executable instructions stored thereon for performing various implementations of the present disclosure method in the example.
  • the present disclosure presents a computer program product tangibly stored on a computer-readable storage medium and comprising computer-executable instructions that, when executed, cause At least one processor executes the methods in various embodiments of the present disclosure.
  • the various example embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor or other computing device. While aspects of the embodiments of the present disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it is to be understood that the blocks, apparatus, systems, techniques, or methods described herein may be taken as non-limiting Examples are implemented in hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controllers or other computing devices, or some combination thereof.
  • Computer-readable program instructions or computer program products for executing various embodiments of the present disclosure can also be stored in the cloud, and when invoked, the user can access the data stored in the cloud for execution through the mobile Internet, fixed network or other network.
  • the computer-readable program instructions of an embodiment of the present disclosure implement the technical solutions disclosed in accordance with various embodiments of the present disclosure.

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Abstract

A method for generating and utilizing a knowledge graph of a manufacturing simulation model, comprising: obtaining a manufacturing simulation model in a factory and generating model representation data on the basis of the manufacturing simulation model; obtaining preset graph semantics for the manufacturing simulation model, the graph semantics providing semantic description of the knowledge graph of the manufacturing simulation model; and extracting, from the model representation data, data corresponding to the graph semantics as graph data of the knowledge graph to form the knowledge graph. The knowledge of manufacturing simulation models is stored and managed in a unified format, thereby facilitating digitization of discrete manufacturing factories. The knowledge graph also provides a unified knowledge acquisition mode for machines and human beings, thereby improving the knowledge acquisition efficiency. Manufacturing simulation models in different formats are converted into model representation data in a unified format, and the model representation data is then converted into graph data of the knowledge graph, so that conversion between the manufacturing simulation models and the knowledge graph can be easily realized.

Description

用于生成和利用制造仿真模型的知识图谱的方法和装置Method and apparatus for generating and utilizing knowledge graphs of manufacturing simulation models 技术领域technical field
本公开涉及工业制造的技术领域,更具体地说,涉及用于生成和利用制造仿真模型的知识图谱的方法、装置、计算设备、计算机可读存储介质和程序产品。The present disclosure relates to the technical field of industrial manufacturing, and more particularly, to methods, apparatuses, computing devices, computer-readable storage media, and program products for generating and utilizing knowledge graphs for manufacturing simulation models.
背景技术Background technique
随着仿真技术在工业数字化中的广泛使用,仿真模型在工业中扮演着非常重要的角色。对于离散制造工厂而言,仿真模型可模拟工厂中的仓储、运输和生产过程,从而辅助管理物料的存储和运输、挖掘生产瓶颈和提高产能。With the widespread use of simulation technology in industrial digitization, simulation models play a very important role in industry. For discrete manufacturing plants, simulation models can simulate the warehousing, transportation, and production processes in the plant to help manage the storage and transportation of materials, identify production bottlenecks, and increase capacity.
建立或维护制造仿真模型(如更改模型结构或参数)的工作通常由熟悉仿真软件和模型格式的仿真工程师完成。当不熟悉仿真软件的其它工厂人员希望得到某个制造仿真模型的仿真结果时,需要与仿真工程师进行协作。The job of creating or maintaining a manufacturing simulation model (such as changing the model structure or parameters) is usually done by simulation engineers who are familiar with simulation software and model formats. Collaboration with simulation engineers is required when other plant personnel unfamiliar with simulation software want to obtain simulation results from a manufacturing simulation model.
发明内容SUMMARY OF THE INVENTION
不同的制造仿真模型通常具有不同格式或者经由不同的仿真软件建立,因此难以对它们进行统一的管理。另外,机器(如AI程序)和不熟悉仿真软件的其它工厂人员无法直接从制造仿真模型获得需要的模型知识。并且,模型的开发和维护都只能由仿真工程师完成,这不仅耗费仿真工程师的大量时间和精力,而且还使得其它工厂人员无法及时修改仿真模型并获得仿真结果,从而降低了依赖于仿真结果的一些工作的工作效率。Different manufacturing simulation models usually have different formats or are created by different simulation software, so it is difficult to manage them uniformly. In addition, machines (such as AI programs) and other plant personnel unfamiliar with simulation software cannot obtain the required model knowledge directly from the manufacturing simulation model. In addition, the development and maintenance of the model can only be completed by the simulation engineer, which not only consumes a lot of time and energy of the simulation engineer, but also makes it impossible for other factory personnel to modify the simulation model and obtain the simulation results in time, thus reducing the number of factors that depend on the simulation results. Some work productivity.
本公开的第一实施例提出了一种用于生成和利用制造仿真模型的知识图谱的方法,包括:获得工厂中的制造仿真模型并基于制造仿真模型生成模型表示数据;获得用于制造仿真模型的预设的图谱语义,图谱语义提供对制造仿真模型的知识图谱的语义描述;以及从模型表示数据中提取与图谱语义对应的数据作为知识图谱的图谱数据,以形成知识图谱。A first embodiment of the present disclosure proposes a method for generating and utilizing a knowledge graph of a manufacturing simulation model, including: obtaining a manufacturing simulation model in a factory and generating model representation data based on the manufacturing simulation model; obtaining a manufacturing simulation model for The preset graph semantics of the Graph semantics provides the semantic description of the knowledge graph of the manufacturing simulation model; and the data corresponding to the graph semantics is extracted from the model representation data as the graph data of the knowledge graph to form the knowledge graph.
在该实施例中,通过预设的图谱语义,将制造仿真模型转换成知识图谱,能够将制造仿真模型的知识以统一的格式进行保存和管理,有助于离散制造工厂的数字化。制造仿真模型的知识图谱还为机器和人类提供了统一的知识获取方式,提高了知识获取的效率。另外,将不同格式的制造仿真模型转换为统一格式的模型表示数据,再转换成知识图谱的图谱数据,能够容易地实现制造仿真模型与知识图谱之间的转换。In this embodiment, the manufacturing simulation model is converted into a knowledge graph through the preset graph semantics, and the knowledge of the manufacturing simulation model can be saved and managed in a unified format, which is helpful for the digitization of discrete manufacturing plants. The knowledge graph of the manufacturing simulation model also provides a unified way of knowledge acquisition for machines and humans, and improves the efficiency of knowledge acquisition. In addition, converting manufacturing simulation models in different formats into model representation data in a unified format, and then converting them into knowledge graph graph data, can easily realize the conversion between manufacturing simulation models and knowledge graphs.
本公开的第二实施例提出了一种用于生成和利用制造仿真模型的知识图谱的装置,包括:模型数据生成单元,其被配置为获得工厂中的制造仿真模型并基于制造仿真模型生成模型表示数据;图谱语义获得单元,其被配置为获得用于制造仿真模型的预设的图谱语义,图谱语义提供对制造仿真模型的知识图谱的语义描述;以及图谱数据提取单元,其被配置为从模型表示数据中提取与图谱语义对应的数据作为知识图谱的图谱数据,以形成知识图谱。A second embodiment of the present disclosure proposes an apparatus for generating and utilizing a knowledge graph of a manufacturing simulation model, including: a model data generating unit configured to obtain a manufacturing simulation model in a factory and generate a model based on the manufacturing simulation model Representation data; a graph semantic obtaining unit configured to obtain a preset graph semantic for manufacturing the simulation model, the graph semantic providing a semantic description of the knowledge graph of the manufacturing simulation model; and a graph data extracting unit configured to extract from the graph The data corresponding to the semantics of the graph is extracted from the model representation data as the graph data of the knowledge graph to form the knowledge graph.
本公开的第三实施例提出了一种计算设备,该计算设备包括:处理器;以及存储器,其用于存储计算机可执行指令,当计算机可执行指令被执行时使得处理器执行第一实施例中的方法。A third embodiment of the present disclosure proposes a computing device comprising: a processor; and a memory for storing computer-executable instructions that, when executed, cause the processor to execute the first embodiment method in .
本公开的第四实施例提出了一种计算机可读存储介质,该计算机可读存储介质具有存储在其上的计算机可执行指令,计算机可执行指令用于执行第一实施例的方法。A fourth embodiment of the present disclosure proposes a computer-readable storage medium having computer-executable instructions stored thereon for performing the method of the first embodiment.
本公开的第五实施例提出了一种计算机程序产品,该计算机程序产品被有形地存储在计算机可读存储介质上,并且包括计算机可执行指令,计算机可执行指令在被执行时使至少一个处理器执行第一实施例的方法。A fifth embodiment of the present disclosure proposes a computer program product tangibly stored on a computer-readable storage medium and comprising computer-executable instructions that, when executed, cause at least one process The controller executes the method of the first embodiment.
附图说明Description of drawings
结合附图并参考以下详细说明,本公开的各实施例的特征、优点及其他方面将变得更加明显,在此以示例性而非限制性的方式示出了本公开的若干实施例,在附图中:The features, advantages and other aspects of various embodiments of the present disclosure will become more apparent when taken in conjunction with the accompanying drawings and with reference to the following detailed description, In the attached picture:
图1示出了根据本公开的一些实施例的用于生成和利用制造仿真模型的知识图谱的方法;1 illustrates a method for generating and utilizing a knowledge graph of a manufacturing simulation model according to some embodiments of the present disclosure;
图2示出了根据本公开的一个实施例的用于实现图1中的方法的系统架 构图;FIG. 2 shows a system architecture diagram for implementing the method in FIG. 1 according to an embodiment of the present disclosure;
图3示出了在图2的实施例中用于生成生产线仿真模型的知识图谱的方法流程图;3 shows a flowchart of a method for generating a knowledge graph of a production line simulation model in the embodiment of FIG. 2;
图4示出了在图2的实施例中用于改进生产线仿真模型的方法流程图;Figure 4 shows a flowchart of a method for improving a production line simulation model in the embodiment of Figure 2;
图5示出了在图2的实施例中用于查询生产线仿真模型的知识图谱的方法流程图;5 shows a flowchart of a method for querying a knowledge graph of a production line simulation model in the embodiment of FIG. 2;
图6示出了在图2的实施例中利用生产线仿真模型的知识图谱修改制造仿真模型的方法流程图;6 shows a flowchart of a method for modifying a manufacturing simulation model using the knowledge graph of the production line simulation model in the embodiment of FIG. 2;
图7示出了图2的实施例中的一个生产线仿真模型的示意图;Fig. 7 shows the schematic diagram of a production line simulation model in the embodiment of Fig. 2;
图8示出了图7中的生产线仿真模型的知识图谱的示意图;Fig. 8 shows the schematic diagram of the knowledge graph of the production line simulation model in Fig. 7;
图9示出了根据本公开的一个实施例的用于生成和利用制造仿真模型的知识图谱的装置;以及FIG. 9 illustrates an apparatus for generating and utilizing a knowledge graph of a manufacturing simulation model according to an embodiment of the present disclosure; and
图10示出了根据本公开的一个实施例的用于生成和利用制造仿真模型的知识图谱的计算设备的框图。10 illustrates a block diagram of a computing device for generating and utilizing a knowledge graph of a manufacturing simulation model according to one embodiment of the present disclosure.
具体实施方式detailed description
以下参考附图详细描述本公开的各个示例性实施例。虽然以下所描述的示例性方法、装置包括在其它组件当中的硬件上执行的软件和/或固件,但是应当注意,这些示例仅仅是说明性的,而不应看作是限制性的。例如,考虑在硬件中独占地、在软件中独占地、或在硬件和软件的任何组合中可以实施任何或所有硬件、软件和固件组件。因此,虽然以下已经描述了示例性的方法和装置,但是本领域的技术人员应容易理解,所提供的示例并不用于限制用于实现这些方法和装置的方式。Various exemplary embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. Although the example methods, apparatuses described below include software and/or firmware executing on hardware among other components, it should be noted that these examples are merely illustrative and should not be regarded as limiting. For example, it is contemplated that any or all hardware, software and firmware components may be implemented exclusively in hardware, exclusively in software, or in any combination of hardware and software. Accordingly, while exemplary methods and apparatus have been described below, those skilled in the art will readily appreciate that the examples provided are not intended to limit the manner in which these methods and apparatus may be implemented.
此外,附图中的流程图和框图示出了根据本公开的各个实施例的方法和系统的可能实现的体系架构、功能和操作。应当注意,方框中所标注的功能也可以按照不同于附图中所标注的顺序发生。例如,两个接连地表示的方框实际上可以基本并行地执行,或者它们有时也可以按照相反的顺序执行,这取决于所涉及的功能。同样应当注意的是,流程图和/或框图中的每个方框、以及流程图和/或框图中的方框的组合,可以使用执行规定的功能或操作的专用的基于硬件的系统来实现,或者可以使用专用硬件与计算机指令的组合来 实现。Additionally, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems in accordance with various embodiments of the present disclosure. It should be noted that the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may in fact be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented using dedicated hardware-based systems that perform the specified functions or operations , or can be implemented using a combination of dedicated hardware and computer instructions.
本文所使用的术语“包括”、“包含”及类似术语是开放性的术语,即“包括/包含但不限于”,表示还可以包括其他内容。术语“基于”是“至少部分地基于”。术语“一个实施例”表示“至少一个实施例”;术语“另一实施例”表示“至少一个另外的实施例”等等。As used herein, the terms "including", "comprising" and similar terms are open-ended terms, ie, "including/including but not limited to," meaning that other content may also be included. The term "based on" is "based at least in part on." The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment" and so on.
图1示出了根据本公开的一些实施例的用于生成和利用制造仿真模型的知识图谱的方法。参考图1,方法100从步骤101开始。在步骤101中,获得工厂中的制造仿真模型并基于制造仿真模型生成模型表示数据。制造仿真模型可以是工厂的生产制造活动中所涉及的任意类型的仿真模型,包括但不限于生产线模型、仓储模型、车间内物流模型等。这些制造仿真模型可以由不同的仿真软件建立并具有不同的格式,并且,它们可以被存储在工厂内的任意存储设备(如服务器)处。制造仿真模型用于对与其对应的实际制造过程进行仿真。例如,生产线模型用于对实际生产线的生产过程进行仿真,仓储模型用于对货物(如物料、工件等)的入库、存放和出库过程进行仿真,车间内物流模型用于对货物的运输过程进行仿真。1 illustrates a method for generating and utilizing a knowledge graph of a manufacturing simulation model in accordance with some embodiments of the present disclosure. Referring to FIG. 1 , method 100 begins at step 101 . In step 101, a manufacturing simulation model in the factory is obtained and model representation data is generated based on the manufacturing simulation model. The manufacturing simulation model may be any type of simulation model involved in the manufacturing activities of the factory, including but not limited to a production line model, a warehousing model, an in-shop logistics model, and the like. These manufacturing simulation models can be created by different simulation software and in different formats, and they can be stored at any storage device (eg, a server) within the factory. The manufacturing simulation model is used to simulate the actual manufacturing process corresponding to it. For example, the production line model is used to simulate the production process of the actual production line, the warehousing model is used to simulate the warehousing, storage and delivery processes of goods (such as materials, workpieces, etc.), and the in-shop logistics model is used to transport the goods. process is simulated.
制造仿真模型的内部数据通常包括对应的实际制造过程中使用的数据,包括但不限于制造仿真模型的各组成部分的类型、名称、属性值、各组成部分之间的连接顺序等等。例如,对于生产线仿真模型,其内部数据包括实际生产线的生产工作站(如组装站、测试站、包装站、加工站等)、传送工作站(如传送带、AGV、机器人、人工等)、暂存区、供应站、回收站等各组成部分的类型和名称、它们的属性值(如空间位置、准备时间、处理时间、加工能力、额外工具或材料、人员要求等)以及它们之间的连接顺序。将这些生产线仿真模型的内部数据转换为低级别格式的模型表示数据。模型表示数据也同样包括上述内部数据的内容。低级别格式例如可以是JSON格式或XML格式。The internal data of the manufacturing simulation model usually includes the corresponding data used in the actual manufacturing process, including but not limited to the type, name, attribute value, connection sequence between the components, etc. of each component of the manufacturing simulation model. For example, for a production line simulation model, its internal data includes production workstations (such as assembly stations, testing stations, packaging stations, processing stations, etc.), transfer workstations (such as conveyor belts, AGVs, robots, labor, etc.), temporary storage areas, Types and names of components such as supply stations, recycling stations, etc., their attribute values (such as spatial location, preparation time, processing time, processing capacity, additional tools or materials, personnel requirements, etc.), and the sequence of connections between them. Convert the internal data of these production line simulation models into model representation data in a low-level format. The model representation data also includes the contents of the above-mentioned internal data. The low-level format can be, for example, JSON format or XML format.
在步骤102中,获得用于制造仿真模型的预设的图谱语义,图谱语义提供对制造仿真模型的知识图谱的语义描述。可以为一个或多个工厂建立统一的图数据库以集中工厂内的所有知识。为该图数据库定义共同的图谱语义。对于每个不同领域,图谱语义提供该领域的知识图谱的语义描述。图谱语义包括与该领域有关的所有实体类别、子类别和属性、实体之间的关系类别和 属性。领域可以是若干个类似对象的集合,例如,多条类似的生产线的集合可以被称为一个领域。实体作为知识图谱的顶点,实体之间的关系作为连接顶点的边。通过关系将实体关联起来,形成知识图谱的层次结构。应当指出,对于不同领域,实体类别、子类别和属性、实体之间的关系类别和属性可能都不相同。In step 102, a preset graph semantics for manufacturing the simulation model is obtained, and the graph semantics provides a semantic description of the knowledge graph of the manufacturing simulation model. A unified graph database can be established for one or more factories to centralize all knowledge within the factory. Define common graph semantics for this graph database. For each distinct domain, Graph Semantics provides a semantic description of the domain's knowledge graph. Graph semantics includes all entity categories, subcategories and attributes related to the domain, and relationship categories and attributes between entities. A realm can be a collection of several similar objects, for example, a collection of several similar production lines can be called a realm. Entities serve as vertices of the knowledge graph, and relationships between entities serve as edges connecting vertices. The entities are associated through relationships to form a hierarchical structure of the knowledge graph. It should be noted that entity categories, subcategories and attributes, and relationship categories and attributes between entities may be different for different domains.
在生成制造仿真模型的知识图谱时,需要首先获得用于该制造仿真模型的预设的图谱语义。对于生产线仿真模型,知识图谱中的实体类别可以包括生产线中的生产工作站、传送工作站、暂存区、供应站、回收站等。实体的属性可以根据实体类别的不同而不同。例如,生产工作站的属性可以包括名称、空间位置、准备时间和处理时间,传送工作站的属性可以包括名称、空间位置和传送速度,暂存区的属性可以包括名称、空间位置和存储容量,供应站的属性可以包括名称、空间位置和物料供应间隔时间,回收站的属性可以包括名称、空间位置和处理时间。实体之间的关系类别可以包括实体之间的连接关系,例如连接的先前实体和后续实体。同时,连接关系能够表示待生产产品的生产顺序。When generating the knowledge graph of the manufacturing simulation model, it is necessary to first obtain the preset graph semantics for the manufacturing simulation model. For the production line simulation model, the entity categories in the knowledge graph can include production workstations, transfer workstations, staging areas, supply stations, recycling stations, etc. in the production line. Attributes of an entity can vary depending on the entity class. For example, the properties of a production workstation can include name, spatial location, preparation time, and processing time, the properties of a transfer station can include name, spatial location, and transfer speed, the properties of a staging area can include name, spatial location, and storage capacity, and the properties of a supply station The properties of the recycle bin can include the name, space location and material supply interval, and the properties of the recycle bin can include the name, space location and processing time. A relationship category between entities may include connection relationships between entities, such as connected previous and subsequent entities. At the same time, the connection relationship can represent the production sequence of the products to be produced.
在步骤103中,从模型表示数据中提取与图谱语义对应的数据作为知识图谱的图谱数据,以形成知识图谱。如上所述,图谱语义包括与领域有关的所有实体类别、子类别和属性、实体之间的关系类别和属性。在形成知识图谱之前,可以根据用于制造仿真模型的预设的图谱语义从模型表示数据中提取对应数据,以生成图谱数据。图谱语义和模型表示数据对数据类别具有不同的定义,也就是说,对于图谱语义中某个类别的实体或关系,在模型表示数据中用另一种类别标识。因此,可以预先建立图谱语义的实体类别和关系类别与模型表示数据中的数据类别之间的映射关系,再按照映射关系从模型表示数据中提取对应的数据,作为用于生成知识图谱的图谱数据。通过建立图谱语义的实体类别和关系类别与模型表示数据中的数据类别之间的映射关系,能够容易地从模型表示数据中查找并提取用于形成知识图谱的图谱数据。可以通过任何现有的图数据库使用生成的图谱数据来形成并保存知识图谱。In step 103, data corresponding to the semantics of the graph is extracted from the model representation data as graph data of the knowledge graph to form the knowledge graph. As mentioned above, graph semantics includes all entity categories, subcategories and attributes related to the domain, and relationship categories and attributes between entities. Before the knowledge graph is formed, corresponding data can be extracted from the model representation data according to the preset graph semantics used to manufacture the simulation model to generate graph data. Graph semantics and model representation data have different definitions for data categories, that is, for entities or relationships of a certain category in graph semantics, they are identified by another category in model representation data. Therefore, the mapping relationship between the entity category and relationship category of the graph semantics and the data category in the model representation data can be established in advance, and then the corresponding data can be extracted from the model representation data according to the mapping relationship as the graph data used to generate the knowledge graph. . By establishing the mapping relationship between the entity categories and relation categories of the graph semantics and the data categories in the model representation data, the graph data for forming the knowledge graph can be easily searched and extracted from the model representation data. The knowledge graph can be formed and saved using the generated graph data through any existing graph database.
在一些实施例中,除了制造仿真模型的知识图谱之外,图数据库还可以保存与一个或多个工厂有关的其它知识图谱,例如,生产记录的知识图谱、 与生产线维修有关的知识图谱等等。通过建立工厂级或企业级的图数据库,能够将整个工厂或企业中不同来源的分散数据集中起来,并以知识图谱的形式统一保存,从而更容易地实现数据获取和利用。In some embodiments, in addition to the knowledge graph of the manufacturing simulation model, the graph database may also hold other knowledge graphs related to one or more factories, eg, knowledge graphs of production records, knowledge graphs related to production line maintenance, etc. . By establishing a factory-level or enterprise-level graph database, scattered data from different sources in the entire factory or enterprise can be centralized and stored uniformly in the form of a knowledge graph, so that data acquisition and utilization can be more easily achieved.
在一些实施例中,方法100进一步包括(图1中未示出):获得制造仿真模型的更新的知识图谱;读取更新的知识图谱的图谱数据,并基于所读取的图谱数据生成更新的模型表示数据;以及利用更新的模型表示数据更新制造仿真模型。在这些实施例中,除了能够生成制造仿真模型的知识图谱以外,还能根据更新的知识图谱更新制造仿真模型。当需要更新制造仿真模型时,可以直接修改知识图谱中的图谱数据,例如,改变知识图谱中的实体和实体之间的关系的属性值、增加实体或关系、删除实体或关系等等。在更新知识图谱之后,读取更新的知识图谱的图谱数据并生成更新的模型表示数据。与根据模型表示数据生成图谱数据类似,可以按照预先建立的图谱语义的实体类别和关系类别与模型表示数据中的数据类别之间的映射关系来从更新的图谱数据中提取所需数据作为更新的模型表示数据。之后,利用更新的模型表示数据更新制造仿真模型。可以根据更新的模型表示数据重新生成制造仿真模型,也可以仅在制造仿真模型中修改发生变化的数据。通过在制造仿真模型的知识图谱的层级更新或维护制造仿真模型,无需再由专门的仿真工程师进行模型的更新和维护,不仅节省了仿真工程师的时间和精力,而且提高了模型更新和维护的效率,使其他工厂人员能够根据需要方便地修改模型及执行仿真。In some embodiments, the method 100 further includes (not shown in FIG. 1 ): obtaining an updated knowledge graph of the manufacturing simulation model; reading graph data of the updated knowledge graph, and generating an updated knowledge graph based on the read graph data model representation data; and updating the manufacturing simulation model with the updated model representation data. In these embodiments, in addition to being able to generate a knowledge graph of the manufacturing simulation model, the manufacturing simulation model can also be updated according to the updated knowledge graph. When the manufacturing simulation model needs to be updated, the graph data in the knowledge graph can be directly modified, for example, changing the attribute value of the entity and the relationship between the entities in the knowledge graph, adding an entity or relationship, deleting an entity or relationship, and so on. After updating the knowledge graph, the graph data of the updated knowledge graph is read and the updated model representation data is generated. Similar to generating the graph data according to the model representation data, the required data can be extracted from the updated graph data as the updated graph data according to the mapping relationship between the entity categories and relation categories of the pre-established graph semantics and the data categories in the model representation data. Models represent data. Thereafter, the manufacturing simulation model is updated with the updated model representation data. The manufacturing simulation model can be regenerated from the updated model representation data, or only the changed data can be modified in the manufacturing simulation model. By updating or maintaining the manufacturing simulation model at the level of the knowledge graph of the manufacturing simulation model, it is no longer necessary to update and maintain the model by a dedicated simulation engineer, which not only saves the simulation engineer's time and effort, but also improves the efficiency of model update and maintenance. , enabling other plant personnel to easily modify the model and perform simulations as needed.
在一些实施例中,利用更新的模型表示数据更新制造仿真模型进一步包括:将更新的模型表示数据与先前的模型表示数据进行比较;基于比较结果确定更新的模型表示数据与先前的模型表示数据之间的差异数据;以及根据差异数据修改制造仿真模型。先前的模型表示数据即为知识图谱更新之前的图谱数据。更新的模型表示数据与先前的模型表示数据之间的差异数据可以是实体、实体之间的关系和/或实体或关系的属性值。例如,对于生产线仿真模型,差异数据可以是生产工作站的一个或多个属性值(如准备时间和处理时间等)发生了变化、增加了传送工作站及其与其它工作站的连接关系、两个生产工作站之间的连接关系发生了变化(意味着待生产产品的加工工序有所改变)等等。在确定差异数据之后,可以修改制造仿真模型中的对应的内 部数据,从而更新制造仿真模型。仅修改制造仿真模型中发生变化的内部数据能够避免重新生成制造仿真模型时潜在发生的错误,增加更新模型的准确性,也提高了模型更新速度,无需跨部门协作。In some embodiments, updating the manufacturing simulation model with the updated model representation data further comprises: comparing the updated model representation data with the previous model representation data; determining a difference between the updated model representation data and the previous model representation data based on the comparison results difference data between; and modify the manufacturing simulation model based on the difference data. The previous model representation data is the graph data before the knowledge graph is updated. The difference data between the updated model representation data and the previous model representation data may be entities, relationships between entities, and/or attribute values of entities or relationships. For example, for a production line simulation model, the difference data can be that one or more attribute values (such as preparation time and processing time, etc.) of the production workstation have changed, the transfer workstation and its connection to other workstations have been added, and the two production workstations have changed. The connection relationship between them has changed (meaning that the processing steps of the product to be produced have changed) and so on. After the discrepancy data is determined, the corresponding internal data in the manufacturing simulation model can be modified to update the manufacturing simulation model. Modifying only the changed internal data in the manufacturing simulation model avoids potential errors in regenerating the manufacturing simulation model, increases the accuracy of the updated model, and increases the speed of model update without the need for cross-departmental collaboration.
在一些实施例中,在获得制造仿真模型的更新的知识图谱之前,方法100进一步包括(图1中未示出):根据知识图谱的图谱数据,判断是否存在与制造仿真模型相关联的模型更新数据;以及当存在模型更新数据时,利用模型更新数据修改图谱数据,以形成更新的知识图谱。可以读取图谱数据中的实体和实体之间的关系,并根据实体和关系在其它知识源中查找模型更新数据,即新的实体、关系和/或它们新的属性值。在查找到模型更新数据之后,可以在图谱数据中修改对应的实体、关系和/或它们的属性值来更新图谱数据,用于形成更新的知识图谱。模型更新数据可以是来自其它外部数据源的保存在工厂内的任意存储设备中的数据,也可以是与制造仿真模型的知识图谱保存在同一图数据库中的其它知识图谱的图谱数据。模型更新数据可以是制造仿真模型中需要更新的任意数据,例如,与制造仿真模型对应的实际制造数据、制造仿真模型中的预期修改数据(如增加、删除和/或修改制造仿真模型中的组成部分、连接顺序和/或属性值等)等等。In some embodiments, before obtaining the updated knowledge graph of the manufacturing simulation model, the method 100 further includes (not shown in FIG. 1 ): according to the graph data of the knowledge graph, determining whether there is a model update associated with the manufacturing simulation model data; and when there is model update data, modify the graph data with the model update data to form an updated knowledge graph. Entities and relationships between entities in the graph data can be read and model update data, ie new entities, relationships and/or their new attribute values, can be looked up in other knowledge sources based on the entities and relationships. After the model update data is found, the corresponding entities, relationships and/or their attribute values can be modified in the graph data to update the graph data, so as to form an updated knowledge graph. The model update data may be data from other external data sources stored in any storage device in the factory, or may be the graph data of other knowledge graphs stored in the same graph database as the knowledge graph of the manufacturing simulation model. Model update data can be any data that needs to be updated in the manufacturing simulation model, for example, actual manufacturing data corresponding to the manufacturing simulation model, expected modification data in the manufacturing simulation model (such as adding, deleting, and/or modifying components in the manufacturing simulation model). parts, connection order and/or attribute values, etc.), etc.
在一些实施例中,模型更新数据为实际制造数据,并且,利用模型更新数据修改图谱数据进一步包括:判断实际制造数据是否与图谱数据中的对应数据相同;以及当实际制造数据与图谱数据中的对应数据不同时,将图谱数据中的对应数据替换为实际制造数据。实际制造数据是与制造仿真模型对应的实际制造过程中所产生的数据。例如,对于生产线仿真模型,实际制造数据可以是生产线的生产工作站的一个或多个实际属性值(如实际准备时间、处理时间、设备故障率等)。实际制造数据例如可以来自生产记录之类的外部数据源。可以自动读取实际制造数据,并将实际制造数据与图谱数据中的对应数据进行比较,以判断是否相同。如果实际制造数据与图谱数据中的对应数据不同,则将图谱数据中的对应数据替换为该实际制造数据,即将实体、关系和/或它们的属性值替换为实际值。利用实际制造数据在知识图谱的层次上自动更新制造仿真模型能够使制造仿真模型保持在最新状态,从而提高了模型更新速度并能够更准确地对实际制造过程进行仿真。In some embodiments, the model update data is actual manufacturing data, and modifying the map data using the model update data further includes: judging whether the actual manufacturing data is the same as the corresponding data in the map data; and when the actual manufacturing data is the same as the map data When the corresponding data is different, replace the corresponding data in the map data with the actual manufacturing data. The actual manufacturing data is the data generated in the actual manufacturing process corresponding to the manufacturing simulation model. For example, for a production line simulation model, the actual manufacturing data may be one or more actual attribute values (eg, actual setup time, processing time, equipment failure rate, etc.) of the production workstations of the production line. Actual manufacturing data may, for example, come from external data sources such as production records. The actual manufacturing data can be automatically read and compared with the corresponding data in the map data to determine whether they are the same. If the actual manufacturing data is different from the corresponding data in the graph data, the corresponding data in the graph data is replaced with the actual manufacturing data, ie entities, relationships and/or their attribute values are replaced with actual values. Using actual manufacturing data to automatically update the manufacturing simulation model at the knowledge graph level keeps the manufacturing simulation model up-to-date, thereby increasing the model update speed and enabling more accurate simulation of the actual manufacturing process.
在一些实施例中,模型更新数据为预期修改数据,并且,方法100进一 步包括(图1中未示出):利用更新的制造仿真模型进行仿真;以及基于仿真结果判断是否在工厂中应用预期修改数据。预期修改数据可以是在制造仿真模型中增加、删除和/或修改的组成部分、连接顺序和/或属性值。例如,对于生产线仿真模型,预期修改数据可以是实际生产线中增加的生产工作站、修改的连接顺序和/或生产工作站的一个或多个属性值(如准备时间、处理时间、设备故障率等)。预期修改数据例如可以由AI程序自动地从互联网或其它文档之类的外部数据源获取。在通过更新知识图谱来更新制造仿真模型后,利用更新的制造仿真模型重新进行仿真,从而获得应用了预期修改数据后的制造仿真模型的新的仿真结果。可以根据新的仿真结果和其它一些条件(如预期修改数据应用在工厂中的难易程度、可能影响生产的产品数量、需要花费的时间和经济成本等)来判断是否在工厂中应用预期修改数据。例如,对于生产线仿真模型,预期生产线中的某一个生产工作站被更换为具有更短的处理时间的另一个生产工作站,则预期修改数据为该另一个生产工作站的处理时间。在对更新的制造仿真模型进行仿真后,根据仿真结果判断是否在实际生产线中用该另一个生产工作站更换原生产工作站。通过利用预期修改数据更新制造仿真模型的知识图谱并对更新的制造仿真模型重新仿真,能够为工厂提供更新设备的决策,从而能够节约成本并提高整个工厂的效率。In some embodiments, the model update data is expected modification data, and the method 100 further includes (not shown in FIG. 1 ): performing a simulation using the updated manufacturing simulation model; and determining whether to apply the expected modification in the factory based on the simulation results data. The expected modification data may be the addition, deletion and/or modification of components, connection sequences and/or property values in the manufacturing simulation model. For example, for a production line simulation model, the expected modification data may be the addition of production workstations to the actual production line, the modified connection sequence, and/or the value of one or more attributes of the production workstations (eg, setup time, processing time, equipment failure rate, etc.). The expected modification data may be obtained automatically by the AI program from external data sources such as the Internet or other documents, for example. After the manufacturing simulation model is updated by updating the knowledge graph, the simulation is performed again using the updated manufacturing simulation model, thereby obtaining a new simulation result of the manufacturing simulation model after applying the expected modification data. Whether to apply the expected modification data in the factory can be judged based on the new simulation results and other conditions (such as how easy it is to apply the expected modification data to the factory, the number of products that may affect production, the time and economic costs required, etc.) . For example, for a production line simulation model, it is expected that a certain production workstation in the production line is replaced with another production workstation with a shorter processing time, and the expected modification data is the processing time of the other production workstation. After simulating the updated manufacturing simulation model, it is judged whether to replace the original production workstation with the other production workstation in the actual production line according to the simulation result. By updating the knowledge graph of the manufacturing simulation model with expected modification data and re-simulating the updated manufacturing simulation model, the decision to update equipment can be provided to the factory, resulting in cost savings and improved overall factory efficiency.
在一些实施例中,方法100进一步包括(图1中未示出):获得针对制造仿真模型的查询条件;以及从知识图谱中读取与查询条件匹配的图谱数据。在生成制造仿真模型的知识图谱之后,还可以从知识图谱查询与制造仿真模型有关的任何知识。例如,对于生产线仿真模型,可以从其知识图谱中查询生产线中的生产工作站的数量、生产产品所需的工艺步骤数量、当某个生产工作站发生故障时是否会影响产品生产等等。在保存制造仿真模型的知识图谱的图数据库中还包括与工厂相关的其它知识图谱(如产品相关的知识图谱、生产记录的知识图谱等等)时,还能够利用多个知识图谱查询更多知识。例如,对于生产线仿真模型,可以利用产品相关的知识图谱和生产线仿真模型的知识图谱查询在某个生产工作站的维修期内将受到影响的产品及其数量,还可以利用生产记录的知识图谱和生产线仿真模型的知识图谱查询在某个生产工作站的故障期内已受到影响的产品及其数量等等。将待查询内 容分解为与知识图谱中的图谱数据相关联的查询条件,再从知识图谱中读取与查询条件匹配的图谱数据。例如,从生产线仿真模型的知识图谱中查询生产线中的测试站的数量时,查询条件可以是测试站的实体类别(工作站)和实体名称(包含“测试”一词)。通过对查询到的实体进行计数,便能得到测试站的数量。通过生成制造仿真模型的知识图谱,能够将模型知识转换为机器理解的形式,从而在知识图谱的层级进行模型相关知识的查询,更好地挖掘和利用了模型的价值。In some embodiments, the method 100 further includes (not shown in FIG. 1 ): obtaining query conditions for the manufacturing simulation model; and reading graph data matching the query conditions from the knowledge graph. After the knowledge graph of the manufacturing simulation model is generated, any knowledge related to the manufacturing simulation model can also be queried from the knowledge graph. For example, for a production line simulation model, the number of production workstations in the production line, the number of process steps required to produce a product, whether a failure of a production workstation will affect product production, and so on can be queried from its knowledge graph. When the graph database storing the knowledge graph of the manufacturing simulation model also includes other knowledge graphs related to the factory (such as product-related knowledge graphs, knowledge graphs of production records, etc.), it is also possible to use multiple knowledge graphs to query more knowledge . For example, for the production line simulation model, the product-related knowledge graph and the knowledge graph of the production line simulation model can be used to query the products and their quantities that will be affected during the maintenance period of a production workstation, and the knowledge graph and production line of production records can be used. The knowledge graph of the simulation model queries the products that have been affected during the failure period of a production station, their quantities, and so on. The content to be queried is decomposed into query conditions associated with the graph data in the knowledge graph, and then the graph data matching the query conditions is read from the knowledge graph. For example, when querying the number of test stations in the production line from the knowledge graph of the production line simulation model, the query conditions can be the entity category (workstation) of the test station and the entity name (containing the word "test"). The number of test stations can be obtained by counting the queried entities. By generating the knowledge graph of the manufacturing simulation model, the model knowledge can be converted into the form of machine understanding, so that the model-related knowledge can be queried at the level of the knowledge graph, and the value of the model can be better explored and utilized.
在一些实施例中,方法100可以通过与工程师使用的客户端设备通信的服务器设备来执行。由客户端设备发出请求,在服务器设备处响应于该请求生成制造仿真模型的知识图谱、查询知识图谱或更新制造仿真模型。在其它实施例中,方法100也可以直接由客户端设备执行。In some embodiments, method 100 may be performed by a server device in communication with a client device used by an engineer. A request is made by the client device, and a knowledge graph of the manufacturing simulation model is generated, the knowledge graph is queried, or the manufacturing simulation model is updated at the server device in response to the request. In other embodiments, the method 100 may also be performed directly by the client device.
在上述实施例中,通过预设的图谱语义,将制造仿真模型转换成知识图谱,能够将制造仿真模型的知识以统一的格式进行保存和管理,有助于离散制造工厂的数字化。制造仿真模型的知识图谱还为机器和人类提供了统一的知识获取方式,提高了知识获取的效率。另外,将不同格式的制造仿真模型转换为统一格式的模型表示数据,再转换成知识图谱的图谱数据,能够容易地实现制造仿真模型与知识图谱之间的转换。In the above embodiment, the manufacturing simulation model is converted into a knowledge graph through the preset graph semantics, so that the knowledge of the manufacturing simulation model can be saved and managed in a unified format, which is helpful for the digitization of discrete manufacturing plants. The knowledge graph of the manufacturing simulation model also provides a unified way of knowledge acquisition for machines and humans, and improves the efficiency of knowledge acquisition. In addition, converting manufacturing simulation models in different formats into model representation data in a unified format, and then converting them into knowledge graph graph data, can easily realize the conversion between manufacturing simulation models and knowledge graphs.
下面参照一个具体的实施例来说明本公开的用于生成和利用制造仿真模型的知识图谱的方法。在本实施例中,制造仿真模型为生产线仿真模型。图2示出了根据本公开的一个实施例的用于实现图1中的方法的系统架构图。图3示出了在图2的实施例中用于生成生产线仿真模型的知识图谱的方法流程图。图4示出了在图2的实施例中用于改进生产线仿真模型的方法流程图。图5示出了在图2的实施例中用于查询生产线仿真模型的知识图谱的方法流程图。图6示出了在图2的实施例中用于利用生产线仿真模型的知识图谱修改生产线仿真模型的方法流程图。同时参考图2-图6说明本实施例。The method for generating and utilizing the knowledge graph of the manufacturing simulation model of the present disclosure will be described below with reference to a specific embodiment. In this embodiment, the manufacturing simulation model is a production line simulation model. FIG. 2 shows a system architecture diagram for implementing the method in FIG. 1 according to an embodiment of the present disclosure. FIG. 3 shows a flowchart of a method for generating a knowledge graph of a production line simulation model in the embodiment of FIG. 2 . FIG. 4 shows a flowchart of a method for improving a production line simulation model in the embodiment of FIG. 2 . FIG. 5 shows a flowchart of a method for querying a knowledge graph of a production line simulation model in the embodiment of FIG. 2 . FIG. 6 shows a flowchart of a method for modifying a production line simulation model using the knowledge graph of the production line simulation model in the embodiment of FIG. 2 . This embodiment will be described with reference to FIGS. 2 to 6 at the same time.
首先参考图2和图3。在图3的方法300中,步骤301包括由图谱语义定义单元201为图数据库203定义共同的图谱语义202。图数据库203用于保存整个工厂或企业的所有知识图谱,例如,生产记录的知识图谱、与产品有关的知识图谱、生产线仿真模型的知识图谱等等。共同的图谱语义202为生产线仿真模型提供知识图谱的语义描述。共同的图谱语义202包括实体类 别、子类别和属性、实体之间的关系类别和属性。下面的表1和表2示出了用于生产线仿真模型的一部分图谱语义的示例。Reference is made first to FIGS. 2 and 3 . In the method 300 of FIG. 3 , step 301 includes defining a common graph semantics 202 for the graph database 203 by the graph semantics definition unit 201 . The graph database 203 is used to save all knowledge graphs of the entire factory or enterprise, for example, knowledge graphs of production records, knowledge graphs related to products, knowledge graphs of production line simulation models, and so on. The common graph semantics 202 provides a semantic description of the knowledge graph for the production line simulation model. Common graph semantics 202 include entity categories, subcategories and attributes, relationship categories and attributes between entities. Tables 1 and 2 below show an example of a portion of the graph semantics for a production line simulation model.
表1Table 1
实体:entity:
Figure PCTCN2020116840-appb-000001
Figure PCTCN2020116840-appb-000001
表2Table 2
关系:relation:
类别category 属性Attributes 描述describe
连接connect    生产路径production path
如表1和表2中示出的,用于生产线仿真模型的图谱语义的实体类别包括生产工作站、供应站和回收站,关系类别包括连接。生产工作站的属性包括名称、x坐标、y坐标、准备时间和处理时间,供应站的属性包括名称、x坐标、y坐标和物料供应间隔时间,回收站的属性包括名称、x坐标、y坐标和处理时间。As shown in Tables 1 and 2, the entity categories for the graph semantics of the production line simulation model include production stations, supply stations, and recycle stations, and the relation categories include connections. The properties of the production station include name, x-coordinate, y-coordinate, preparation time and processing time, the properties of the supply station include the name, x-coordinate, y-coordinate and material supply interval, and the properties of the recycling station include the name, x-coordinate, y-coordinate and processing time.
接下来,在步骤302中,由模型连接器205获得生产线仿真模型204并基于该生产线仿真模型204生成模型表示数据206。模型连接器205可以被实现为仿真软件中的插件。图7示出了一个生产线仿真模型的示意图。在生产线仿真模型700中,在供应站701与回收站707之间按产品加工顺序分别经历5个生产工作站:组装站702、预测试站703、第一测试站704、第二测试站705和包装站706。第一测试站704和第二测试站705为同时进行测试的测试站,表示待生产产品可以从其中之一通过。在本实施例中,从生产线仿真模型204中读取内部数据并转换为JSON文件格式的模型表示数据206。在其它实施例中,也可以采用其它格式来保存模型表示数据206,例如XML格式。下面的表3列出了模型表示数据206的一部分示例。Next, in step 302 , the production line simulation model 204 is obtained by the model connector 205 and model representation data 206 is generated based on the production line simulation model 204 . Model connector 205 may be implemented as a plug-in in simulation software. Figure 7 shows a schematic diagram of a production line simulation model. In the production line simulation model 700, the supply station 701 and the recycle station 707 go through five production stations in the order of product processing: assembly station 702, pre-test station 703, first test station 704, second test station 705 and packaging Stop 706. The first test station 704 and the second test station 705 are test stations for simultaneous testing, indicating that the product to be produced can pass through one of them. In this embodiment, internal data is read from the production line simulation model 204 and converted into model representation data 206 in JSON file format. In other embodiments, the model representation data 206 may also be stored in other formats, such as XML format. Table 3 below lists some examples of model representation data 206 .
表3table 3
Figure PCTCN2020116840-appb-000002
Figure PCTCN2020116840-appb-000002
在步骤303中,获得用于生产线仿真模型204的共同的图谱语义202。如上所述,共同的图谱语义202提供对生产线仿真模型204的知识图谱的语义描述。继续步骤304,由图谱连接器207从模型表示数据206中提取与图谱语义202对应的数据作为知识图谱208的图谱数据。在图谱连接器207中预先建立图谱语义202的实体类别与模型表示数据206中的数据类别之间的映射关系。对于示例的生产线仿真模型700,图谱语义202中定义的实体类别“供应站”和“回收站”分别对应于模型表示数据206中的“供应站”和“回收站”,实体类别“生产工作站”对应于模型表示数据206中的“单处理站”,关系类别“连接”对应于模型表示数据206中的“连接”。图谱连接 器207按照映射关系从模型表示数据206中提取对应的数据作为图谱数据。在本实施例中,使用TinkerPop作为图数据库,图谱数据可以是用于由TinkerPop执行的脚本文件。图数据库203使用图谱数据形成并保存生产线仿真模型204的知识图谱208。图8示出了生产线仿真模型700的知识图谱800的示意图。在知识图谱800中包括供应站801、组装站802、预测试站803、第一测试站804、第二测试站805、包装站806和回收站807。这些工作站之间的箭头表示它们之间的连接关系,同时也表示产品的生产顺序。In step 303, common graph semantics 202 for the production line simulation model 204 are obtained. As described above, the common graph semantics 202 provides a semantic description of the knowledge graph of the production line simulation model 204 . Continuing with step 304 , the graph connector 207 extracts data corresponding to the graph semantics 202 from the model representation data 206 as graph data of the knowledge graph 208 . The mapping relationship between the entity categories of the graph semantics 202 and the data categories in the model representation data 206 is pre-established in the graph connector 207 . For the example production line simulation model 700, the entity categories "supply station" and "recycling station" defined in the graph semantics 202 correspond to "supply station" and "recycling station", respectively, in the model representation data 206, and the entity category "production station" Corresponding to "single processing station" in the model representation data 206 , the relationship class "connection" corresponds to "connection" in the model representation data 206 . The graph connector 207 extracts corresponding data from the model representation data 206 as graph data according to the mapping relationship. In this embodiment, TinkerPop is used as the graph database, and the graph data may be a script file for execution by TinkerPop. The graph database 203 uses the graph data to form and store the knowledge graph 208 of the production line simulation model 204 . FIG. 8 shows a schematic diagram of a knowledge graph 800 of a production line simulation model 700 . The knowledge graph 800 includes a supply station 801 , an assembly station 802 , a pre-test station 803 , a first test station 804 , a second test station 805 , a packaging station 806 and a recycling station 807 . The arrows between these workstations indicate the connection between them, and also indicate the production sequence of the products.
在生成生产线仿真模型204的知识图谱208之后,可选地,还可以执行方法400来对生产线仿真模型204进行改进。参照图4,在步骤401中,由模型改进模块209根据知识图谱208的图谱数据,判断是否存在与生产线仿真模型204有关的实际制造数据,例如,保存在图数据库203中的与生产线仿真模型204对应的生产线的最新生产记录。生产记录中通常包括各工作站的实际属性值,例如,实际准备时间、实际处理时间、实际故障率等等。在步骤402中,当存在实际制造数据时,模型改进模块209判断实际制造数据是否与图谱数据中的对应数据相同,例如,将某个加工工作站的实际准备时间与知识图谱208中的对应准备时间进行比较。在步骤403中,当实际制造数据与图谱数据中的对应数据不同时,模型改进模块209使用实际制造数据替换图谱数据中的对应数据,从而对知识图谱208进行更新。After the knowledge graph 208 of the production line simulation model 204 is generated, optionally, the method 400 may also be performed to improve the production line simulation model 204 . 4, in step 401, the model improvement module 209 judges whether there is actual manufacturing data related to the production line simulation model 204 according to the map data of the knowledge map 208, for example, the production line simulation model 204 stored in the graph database 203 The latest production record of the corresponding production line. Production records usually include actual attribute values of each workstation, such as actual preparation time, actual processing time, actual failure rate, and so on. In step 402, when there is actual manufacturing data, the model improvement module 209 judges whether the actual manufacturing data is the same as the corresponding data in the map data, for example, the actual preparation time of a certain processing workstation and the corresponding preparation time in the knowledge map 208 Compare. In step 403, when the actual manufacturing data is different from the corresponding data in the graph data, the model improvement module 209 replaces the corresponding data in the graph data with the actual manufacturing data, thereby updating the knowledge graph 208.
在步骤404中,由图谱连接器207获得生产线仿真模型204的更新的知识图谱208,读取图谱数据,并基于图谱数据对模型表示数据206进行更新。与根据模型表示数据206生成图谱数据类似,按照图谱语义202的实体类别与模型表示数据206中的数据类别之间的映射关系,从更新的图谱数据中提取所需数据。接下来,在步骤405中,由模型连接器205将更新的模型表示数据206与先前的模型表示数据206进行比较。在步骤406中,由模型连接器205基于比较结果确定更新的模型表示数据206与先前的模型表示数据206之间的差异数据,例如,某个生产工作站的不同属性值。在步骤407中,由模型连接器205根据差异数据修改生产线仿真模型204,即修改模型的内部数据。In step 404, the updated knowledge graph 208 of the production line simulation model 204 is obtained by the graph connector 207, the graph data is read, and the model representation data 206 is updated based on the graph data. Similar to generating the graph data from the model representation data 206 , the required data is extracted from the updated graph data according to the mapping relationship between the entity categories of the graph semantics 202 and the data categories in the model representation data 206 . Next, in step 405 , the updated model representation data 206 is compared with the previous model representation data 206 by the model connector 205 . In step 406, discrepancy data between the updated model representation data 206 and the previous model representation data 206 is determined by the model connector 205 based on the comparison results, eg, different attribute values of a certain production workstation. In step 407, the model connector 205 modifies the production line simulation model 204 according to the difference data, that is, modifies the internal data of the model.
在系统200中,还可以通过图谱查询模块210对知识图谱208进行查询。图5示出了用于查询生产线仿真模型的知识图谱的方法流程图。同时参考图 2和图5。在图5的方法500中,步骤501包括由图谱查询模块210获得针对生产线仿真模型204的查询条件。查询条件可以通过对待查询内容进行分解而获得。以图7中的生产线仿真模型700为例,待查询内容可以包括实际生产线中的生产工作站的数量、生产产品所需的工艺步骤数量等等。查询条件根据待查询内容不同而不同。在步骤502中,由图谱查询模块210从知识图谱中读取与查询条件匹配的图谱数据。之后,在步骤503中,由图谱查询模块210将所读取的图谱数据进行后处理,生成查询结果。后处理例如可以是合并所读取的图谱数据、对所读取的图谱数据进行进一步处理等等。In the system 200 , the knowledge graph 208 can also be queried through the graph query module 210 . Figure 5 shows a flow chart of a method for querying a knowledge graph of a production line simulation model. Refer to Figure 2 and Figure 5 simultaneously. In the method 500 of FIG. 5 , step 501 includes obtaining query conditions for the production line simulation model 204 by the graph query module 210 . The query condition can be obtained by decomposing the query content. Taking the production line simulation model 700 in FIG. 7 as an example, the content to be queried may include the number of production workstations in the actual production line, the number of process steps required to produce products, and the like. The query conditions vary according to the content to be queried. In step 502, the graph query module 210 reads graph data matching the query condition from the knowledge graph. After that, in step 503, the graph query module 210 performs post-processing on the read graph data to generate a query result. Post-processing may be, for example, merging the read atlas data, further processing the read atlas data, and the like.
如图2所示,系统200还包括模型修改模块211。图6示出了利用生产线仿真模型的知识图谱修改生产线仿真模型的方法流程图。同时参考图2和图6。在方法600中,步骤601包括由模型修改模块211根据知识图谱208的图谱数据,判断是否存在与生产线仿真模型204有关的预期修改数据。预期修改数据可以是生产线中增加的生产工作站、修改的连接顺序和/或生产工作站的一个或多个属性值。可以从互联网或文档之类的外部数据源获取预期修改数据。在步骤602中,当存在预期修改数据时,模型修改模块211利用预期修改数据对知识图谱208进行更新,即使用预期修改数据替换知识图谱208中的对应数据。以图7中的生产线仿真模型700为例,模型修改模块211判断一个处理时间为120秒的新的测试站可用于更换生产线中的第二测试站,则可以用该新的处理时间(120秒)代替知识图谱208中的第二测试站的原处理时间(140秒)。As shown in FIG. 2 , the system 200 also includes a model modification module 211 . FIG. 6 shows a flowchart of a method for modifying a production line simulation model by using the knowledge graph of the production line simulation model. 2 and 6 are also referred to. In the method 600 , step 601 includes determining, by the model modification module 211 , whether there is expected modification data related to the production line simulation model 204 according to the graph data of the knowledge graph 208 . The expected modification data may be an added production station in the production line, a modified connection sequence, and/or one or more attribute values of a production station. Expected modification data can be obtained from external data sources such as the Internet or documents. In step 602, when the expected modification data exists, the model modification module 211 updates the knowledge graph 208 with the expected modification data, that is, replaces the corresponding data in the knowledge graph 208 with the expected modification data. Taking the production line simulation model 700 in FIG. 7 as an example, the model modification module 211 determines that a new test station with a processing time of 120 seconds can be used to replace the second test station in the production line, then the new processing time (120 seconds) can be used. ) in place of the original processing time (140 seconds) of the second test station in the knowledge graph 208.
方法600中的步骤603-606与方法400中的步骤404-407相同。在步骤603中,由图谱连接器207获得生产线仿真模型204的更新的知识图谱208,读取图谱数据,并基于图谱数据对模型表示数据206进行更新。在步骤604中,由模型连接器205将更新的模型表示数据206与先前的模型表示数据206进行比较。在步骤605中,由模型连接器205基于比较结果确定更新的模型表示数据206与先前的模型表示数据206之间的差异数据。在步骤606中,由模型连接器205根据差异数据修改生产线仿真模型204。Steps 603 - 606 in method 600 are the same as steps 404 - 407 in method 400 . In step 603, the updated knowledge graph 208 of the production line simulation model 204 is obtained by the graph connector 207, the graph data is read, and the model representation data 206 is updated based on the graph data. In step 604 , the updated model representation data 206 is compared with the previous model representation data 206 by the model connector 205 . In step 605, discrepancy data between the updated model representation data 206 and the previous model representation data 206 is determined by the model connector 205 based on the comparison results. In step 606, the production line simulation model 204 is modified by the model connector 205 according to the difference data.
接下来,在步骤607中,由仿真软件(图2中未示出)利用更新的生产线仿真模型204进行仿真。在步骤608中,由应用判断模块(图2中未示出)根据仿真结果判断是否在实际生产线中应用预期修改数据。仍以图7中的生 产线仿真模型700为例,生产线仿真模型204中的第二测试站被更换为新的测试站后,生产线仿真模型204的产能仿真结果从1149个产品/天增加到1231个产品/天。应用判断模块根据仿真结果的变化和其它预设条件(如更换的难易程度、更换过程中将受影响的产量等)来判断是否将实际生产线中的第二测试站更换为新的测试站。Next, in step 607, a simulation is performed by simulation software (not shown in FIG. 2) using the updated production line simulation model 204. In step 608 , the application judgment module (not shown in FIG. 2 ) judges whether to apply the expected modification data in the actual production line according to the simulation result. Still taking the production line simulation model 700 in FIG. 7 as an example, after the second test station in the production line simulation model 204 is replaced with a new test station, the production capacity simulation result of the production line simulation model 204 increases from 1149 products/day to 1231 product/day. The application judgment module judges whether to replace the second test station in the actual production line with a new test station according to changes in the simulation results and other preset conditions (such as the difficulty of replacement, the output that will be affected during the replacement process, etc.).
在上述实施例中,通过预设的图谱语义,将制造仿真模型转换成知识图谱,能够将制造仿真模型的知识以统一的格式进行保存和管理,有助于离散制造工厂的数字化。制造仿真模型的知识图谱还为机器和人类提供了统一的知识获取方式,提高了知识获取的效率。另外,将不同格式的制造仿真模型转换为统一格式的模型表示数据,再转换成知识图谱的图谱数据,能够容易地实现制造仿真模型与知识图谱之间的转换。In the above embodiment, the manufacturing simulation model is converted into a knowledge graph through the preset graph semantics, so that the knowledge of the manufacturing simulation model can be saved and managed in a unified format, which is helpful for the digitization of discrete manufacturing plants. The knowledge graph of the manufacturing simulation model also provides a unified way of knowledge acquisition for machines and humans, and improves the efficiency of knowledge acquisition. In addition, converting manufacturing simulation models in different formats into model representation data in a unified format, and then converting them into knowledge graph graph data, can easily realize the conversion between manufacturing simulation models and knowledge graphs.
图9示出了根据本公开的一个实施例的用于生成和利用制造仿真模型的知识图谱的装置。参照图9,装置900包括模型数据生成单元901、图谱语义获得单元902和图谱数据提取单元903。模型数据生成单元901被配置为获得工厂中的制造仿真模型并基于制造仿真模型生成模型表示数据。图谱语义获得单元902被配置为获得用于制造仿真模型的预设的图谱语义,图谱语义提供对制造仿真模型的知识图谱的语义描述。图谱数据提取单元903被配置为从模型表示数据中提取与图谱语义对应的数据作为知识图谱的图谱数据,以形成知识图谱。图9中的各单元可以利用软件、硬件(例如集成电路、FPGA等)或者软硬件结合的方式来实现。FIG. 9 illustrates an apparatus for generating and utilizing a knowledge graph of a manufacturing simulation model according to one embodiment of the present disclosure. 9 , the apparatus 900 includes a model data generating unit 901 , a graph semantics obtaining unit 902 and a graph data extracting unit 903 . The model data generating unit 901 is configured to obtain a manufacturing simulation model in a factory and generate model representation data based on the manufacturing simulation model. The graph semantics obtaining unit 902 is configured to obtain preset graph semantics for manufacturing the simulation model, the graph semantics providing a semantic description of the knowledge graph of the manufacturing simulation model. The graph data extraction unit 903 is configured to extract data corresponding to the graph semantics from the model representation data as graph data of the knowledge graph to form the knowledge graph. Each unit in FIG. 9 may be implemented by software, hardware (eg, integrated circuit, FPGA, etc.), or a combination of software and hardware.
在一些实施例中,装置900进一步包括图谱获得单元、模型数据更新单元和模型更新单元(图9中未示出)。图谱获得单元被配置为获得制造仿真模型的更新的知识图谱。模型数据更新单元被配置为读取更新的知识图谱的图谱数据,并基于所读取的图谱数据生成更新的模型表示数据。模型更新单元被配置为利用更新的模型表示数据更新制造仿真模型。In some embodiments, the apparatus 900 further includes a graph obtaining unit, a model data updating unit, and a model updating unit (not shown in FIG. 9 ). The graph obtaining unit is configured to obtain an updated knowledge graph of the manufacturing simulation model. The model data updating unit is configured to read graph data of the updated knowledge graph, and generate updated model representation data based on the read graph data. The model update unit is configured to update the manufacturing simulation model with the updated model representation data.
在一些实施例中,模型更新单元被进一步配置为:将更新的模型表示数据与先前的模型表示数据进行比较;基于比较结果确定更新的模型表示数据与先前的模型表示数据之间的差异数据;以及根据差异数据修改制造仿真模型。In some embodiments, the model update unit is further configured to: compare the updated model representation data with the previous model representation data; determine difference data between the updated model representation data and the previous model representation data based on the comparison result; As well as modifying the manufacturing simulation model based on the variance data.
在一些实施例中,装置900进一步包括更新数据判断单元和图谱数据修 改单元(图9中未示出)。更新数据判断单元被配置为根据知识图谱的图谱数据,判断是否存在与制造仿真模型相关联的模型更新数据。图谱数据修改单元被配置为利用模型更新数据修改图谱数据,以形成更新的知识图谱。In some embodiments, the apparatus 900 further includes an update data determination unit and a map data modification unit (not shown in FIG. 9 ). The update data judgment unit is configured to judge whether there is model update data associated with the manufacturing simulation model according to the graph data of the knowledge graph. The graph data modification unit is configured to modify the graph data using the model update data to form an updated knowledge graph.
在一些实施例中,其中,模型更新数据为实际制造数据,并且,图谱数据修改单元被进一步配置为:判断实际制造数据是否与图谱数据中的对应数据相同;以及当实际制造数据与图谱数据中的对应数据不同时,将图谱数据中的对应数据替换为实际制造数据。In some embodiments, wherein the model update data is actual manufacturing data, and the map data modification unit is further configured to: determine whether the actual manufacturing data is the same as the corresponding data in the map data; When the corresponding data is different, replace the corresponding data in the map data with the actual manufacturing data.
在一些实施例中,模型更新数据为预期修改数据,并且,装置900进一步包括模型仿真单元和应用判断单元(图9中未示出)。模型仿真单元被配置为利用更新的制造仿真模型进行仿真。应用判断单元被配置为基于仿真结果判断是否在工厂中应用预期修改数据。In some embodiments, the model update data is expected modification data, and the apparatus 900 further includes a model simulation unit and an application judgment unit (not shown in FIG. 9 ). The model simulation unit is configured to simulate with the updated manufacturing simulation model. The application judgment unit is configured to judge whether to apply the expected modification data in the factory based on the simulation result.
在一些实施例中,装置900进一步包括查询条件获得单元和图谱数据读取单元(图9中未示出)。查询条件获得单元被配置为获得针对制造仿真模型的查询条件。图谱数据读取单元被配置为从知识图谱中读取与查询条件匹配的图谱数据。In some embodiments, the apparatus 900 further includes a query condition obtaining unit and a graph data reading unit (not shown in FIG. 9 ). The query condition obtaining unit is configured to obtain query conditions for the manufacturing simulation model. The graph data reading unit is configured to read graph data matching the query condition from the knowledge graph.
在一些实施例中,制造仿真模型的知识图谱被保存在图数据库中,并且,图数据库还用于保存与工厂有关的其它知识图谱。In some embodiments, the knowledge graph of the manufacturing simulation model is maintained in a graph database, and the graph database is also used to maintain other knowledge graphs related to the plant.
图10示出了根据本公开的一个实施例的用于生成和利用制造仿真模型的知识图谱的计算设备的框图。从图10中可以看出,用于生成和利用制造仿真模型的知识图谱的计算设备1000包括处理器1001以及与处理器1001耦接的存储器1002。存储器1002用于存储计算机可执行指令,当计算机可执行指令被执行时使得处理器1001执行以上实施例中的方法。10 illustrates a block diagram of a computing device for generating and utilizing a knowledge graph of a manufacturing simulation model according to one embodiment of the present disclosure. As can be seen in FIG. 10 , a computing device 1000 for generating and utilizing a knowledge graph of a manufacturing simulation model includes a processor 1001 and a memory 1002 coupled to the processor 1001 . The memory 1002 is used for storing computer-executable instructions, and when the computer-executable instructions are executed, causes the processor 1001 to perform the methods in the above embodiments.
此外,替代地,上述方法能够通过计算机可读存储介质来实现。计算机可读存储介质上载有用于执行本公开的各个实施例的计算机可读程序指令。计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是但不限于电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括:便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携 式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身,诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如,通过光纤电缆的光脉冲)、或者通过电线传输的电信号。Also, alternatively, the above-described method can be implemented by a computer-readable storage medium. The computer-readable storage medium carries computer-readable program instructions for carrying out various embodiments of the present disclosure. A computer-readable storage medium may be a tangible device that can hold and store instructions for use by the instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of computer readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM) or flash memory), static random access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically coded devices, such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above. Computer-readable storage media, as used herein, are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (eg, light pulses through fiber optic cables), or through electrical wires transmitted electrical signals.
因此,在另一个实施例中,本公开提出了一种计算机可读存储介质,该计算机可读存储介质具有存储在其上的计算机可执行指令,计算机可执行指令用于执行本公开的各个实施例中的方法。Accordingly, in another embodiment, the present disclosure presents a computer-readable storage medium having computer-executable instructions stored thereon for performing various implementations of the present disclosure method in the example.
在另一个实施例中,本公开提出了一种计算机程序产品,该计算机程序产品被有形地存储在计算机可读存储介质上,并且包括计算机可执行指令,该计算机可执行指令在被执行时使至少一个处理器执行本公开的各个实施例中的方法。In another embodiment, the present disclosure presents a computer program product tangibly stored on a computer-readable storage medium and comprising computer-executable instructions that, when executed, cause At least one processor executes the methods in various embodiments of the present disclosure.
一般而言,本公开的各个示例实施例可以在硬件或专用电路、软件、固件、逻辑,或其任何组合中实施。某些方面可以在硬件中实施,而其他方面可以在可以由控制器、微处理器或其他计算设备执行的固件或软件中实施。当本公开的实施例的各方面被图示或描述为框图、流程图或使用某些其他图形表示时,将理解此处描述的方框、装置、系统、技术或方法可以作为非限制性的示例在硬件、软件、固件、专用电路或逻辑、通用硬件或控制器或其他计算设备,或其某些组合中实施。In general, the various example embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, firmware, logic, or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor or other computing device. While aspects of the embodiments of the present disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it is to be understood that the blocks, apparatus, systems, techniques, or methods described herein may be taken as non-limiting Examples are implemented in hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controllers or other computing devices, or some combination thereof.
用于执行本公开的各个实施例的计算机可读程序指令或者计算机程序产品也能够存储在云端,在需要调用时,用户能够通过移动互联网、固网或者其他网络访问存储在云端上的用于执行本公开的一个实施例的计算机可读程序指令,从而实施依据本公开的各个实施例所公开的技术方案。Computer-readable program instructions or computer program products for executing various embodiments of the present disclosure can also be stored in the cloud, and when invoked, the user can access the data stored in the cloud for execution through the mobile Internet, fixed network or other network. The computer-readable program instructions of an embodiment of the present disclosure implement the technical solutions disclosed in accordance with various embodiments of the present disclosure.
虽然已经参考若干具体实施例描述了本公开的实施例,但是应当理解,本公开的实施例并不限于所公开的具体实施例。本公开的实施例旨在涵盖在所附权利要求的精神和范围内所包括的各种修改和等同布置。权利要求的范围符合最宽泛的解释,从而包含所有这样的修改及等同结构和功能。Although embodiments of the present disclosure have been described with reference to several specific embodiments, it is to be understood that embodiments of the present disclosure are not limited to the specific embodiments disclosed. The embodiments of the present disclosure are intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. The scope of the claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.

Claims (19)

  1. 用于生成和利用制造仿真模型的知识图谱的方法,包括:Methods for generating and utilizing knowledge graphs of manufacturing simulation models, including:
    获得工厂中的制造仿真模型并基于所述制造仿真模型生成模型表示数据;obtaining a manufacturing simulation model in the factory and generating model representation data based on the manufacturing simulation model;
    获得用于所述制造仿真模型的预设的图谱语义,所述图谱语义提供对所述制造仿真模型的所述知识图谱的语义描述;以及obtaining preset graph semantics for the manufacturing simulation model, the graph semantics providing a semantic description of the knowledge graph for the manufacturing simulation model; and
    从所述模型表示数据中提取与所述图谱语义对应的数据作为所述知识图谱的图谱数据,以形成所述知识图谱。Data corresponding to the graph semantics is extracted from the model representation data as graph data of the knowledge graph to form the knowledge graph.
  2. 根据权利要求1所述的方法,进一步包括:The method of claim 1, further comprising:
    获得所述制造仿真模型的更新的知识图谱;obtaining an updated knowledge graph of the manufacturing simulation model;
    读取所述更新的知识图谱的图谱数据,并基于所读取的图谱数据生成更新的模型表示数据;以及reading the graph data of the updated knowledge graph, and generating updated model representation data based on the read graph data; and
    利用所述更新的模型表示数据更新所述制造仿真模型。The manufacturing simulation model is updated with the updated model representation data.
  3. 根据权利要求2所述的方法,其中,利用所述更新的模型表示数据更新所述制造仿真模型进一步包括:3. The method of claim 2, wherein updating the manufacturing simulation model with the updated model representation data further comprises:
    将所述更新的模型表示数据与先前的所述模型表示数据进行比较;comparing the updated model representation data with the previous model representation data;
    基于比较结果确定所述更新的模型表示数据与先前的所述模型表示数据之间的差异数据;以及determining discrepancy data between the updated model representation data and the previous model representation data based on the comparison; and
    根据所述差异数据修改所述制造仿真模型。The manufacturing simulation model is modified based on the difference data.
  4. 根据权利要求2所述的方法,其中,在获得所述制造仿真模型的更新的知识图谱之前,所述方法进一步包括:The method of claim 2, wherein before obtaining the updated knowledge graph of the manufacturing simulation model, the method further comprises:
    根据所述知识图谱的所述图谱数据,判断是否存在与所述制造仿真模型相关联的模型更新数据;以及judging whether there is model update data associated with the manufacturing simulation model according to the graph data of the knowledge graph; and
    当存在所述模型更新数据时,利用所述模型更新数据修改所述图谱数据,以形成所述更新的知识图谱。When the model update data exists, the graph data is modified using the model update data to form the updated knowledge graph.
  5. 根据权利要求4所述的方法,其中,所述模型更新数据为实际制造数据,并且,利用所述模型更新数据修改所述图谱数据进一步包括:The method of claim 4, wherein the model update data is actual manufacturing data, and modifying the map data using the model update data further comprises:
    判断所述实际制造数据是否与所述图谱数据中的对应数据相同;以及determining whether the actual manufacturing data is the same as the corresponding data in the map data; and
    当所述实际制造数据与所述图谱数据中的对应数据不同时,将所述图谱数据中的对应数据替换为所述实际制造数据。When the actual manufacturing data is different from the corresponding data in the map data, the corresponding data in the map data is replaced with the actual manufacturing data.
  6. 根据权利要求4所述的方法,其中,所述模型更新数据为预期修改数据,并且,所述方法进一步包括:The method of claim 4, wherein the model update data is expected modification data, and the method further comprises:
    利用更新的制造仿真模型进行仿真;以及Simulate with updated manufacturing simulation models; and
    基于仿真结果判断是否在所述工厂中应用所述预期修改数据。Whether to apply the expected modification data in the plant is determined based on the simulation results.
  7. 根据权利要求1所述的方法,进一步包括:The method of claim 1, further comprising:
    获得针对所述制造仿真模型的查询条件;以及obtaining query conditions for the manufacturing simulation model; and
    从所述知识图谱中读取与所述查询条件匹配的图谱数据。The graph data matching the query condition is read from the knowledge graph.
  8. 根据权利要求1所述的方法,其中,所述制造仿真模型的所述知识图谱被保存在图数据库中,并且,所述图数据库还用于保存与所述工厂有关的其它知识图谱。The method of claim 1, wherein the knowledge graph of the manufacturing simulation model is stored in a graph database, and the graph database is also used to store other knowledge graphs related to the factory.
  9. 用于生成和利用制造仿真模型的知识图谱的装置,包括:Apparatus for generating and utilizing knowledge graphs of manufacturing simulation models, including:
    模型数据生成单元,其被配置为获得工厂中的制造仿真模型并基于所述制造仿真模型生成模型表示数据;a model data generation unit configured to obtain a manufacturing simulation model in the factory and generate model representation data based on the manufacturing simulation model;
    图谱语义获得单元,其被配置为获得用于所述制造仿真模型的预设的图谱语义,所述图谱语义提供对所述制造仿真模型的所述知识图谱的语义描述;以及a graph semantics obtaining unit configured to obtain preset graph semantics for the manufacturing simulation model, the graph semantics providing a semantic description of the knowledge graph of the manufacturing simulation model; and
    图谱数据提取单元,其被配置为从所述模型表示数据中提取与所述图谱语义对应的数据作为所述知识图谱的图谱数据,以形成所述知识图谱。A graph data extraction unit configured to extract data corresponding to the graph semantics from the model representation data as graph data of the knowledge graph to form the knowledge graph.
  10. 根据权利要求9所述的装置,进一步包括:The apparatus of claim 9, further comprising:
    图谱获得单元,其被配置为获得所述制造仿真模型的更新的知识图谱;a graph obtaining unit configured to obtain an updated knowledge graph of the manufacturing simulation model;
    模型数据更新单元,其被配置为读取所述更新的知识图谱的图谱数据,并基于所读取的图谱数据生成更新的模型表示数据;以及a model data update unit configured to read graph data of the updated knowledge graph and generate updated model representation data based on the read graph data; and
    模型更新单元,其被配置为利用所述更新的模型表示数据更新所述制造仿真模型。A model update unit configured to update the manufacturing simulation model with the updated model representation data.
  11. 根据权利要求10所述的装置,其中,所述模型更新单元被进一步配置为:The apparatus of claim 10, wherein the model update unit is further configured to:
    将所述更新的模型表示数据与先前的所述模型表示数据进行比较;comparing the updated model representation data with the previous model representation data;
    基于比较结果确定所述更新的模型表示数据与先前的所述模型表示数据之间的差异数据;以及determining discrepancy data between the updated model representation data and the previous model representation data based on the comparison; and
    根据所述差异数据修改所述制造仿真模型。The manufacturing simulation model is modified based on the difference data.
  12. 根据权利要求10所述的装置,进一步包括:The apparatus of claim 10, further comprising:
    更新数据判断单元,其被配置为根据所述知识图谱的所述图谱数据,判断是否存在与所述制造仿真模型相关联的模型更新数据;以及an update data determination unit configured to determine whether there is model update data associated with the manufacturing simulation model according to the graph data of the knowledge graph; and
    图谱数据修改单元,其被配置为利用所述模型更新数据修改所述图谱数据,以形成所述更新的知识图谱。A graph data modification unit configured to modify the graph data using the model update data to form the updated knowledge graph.
  13. 根据权利要求12所述的装置,其中,所述模型更新数据为实际制造数据,并且,所述图谱数据修改单元被进一步配置为:The apparatus of claim 12, wherein the model update data is actual manufacturing data, and the map data modification unit is further configured to:
    判断所述实际制造数据是否与所述图谱数据中的对应数据相同;以及determining whether the actual manufacturing data is the same as the corresponding data in the map data; and
    当所述实际制造数据与所述图谱数据中的对应数据不同时,将所述图谱数据中的对应数据替换为所述实际制造数据。When the actual manufacturing data is different from the corresponding data in the map data, the corresponding data in the map data is replaced with the actual manufacturing data.
  14. 根据权利要求12所述的装置,其中,所述模型更新数据为预期修改数据,并且,所述装置进一步包括:The apparatus of claim 12, wherein the model update data is expected modification data, and the apparatus further comprises:
    模型仿真单元,其被配置为利用更新的制造仿真模型进行仿真;以及a model simulation unit configured to simulate with the updated manufacturing simulation model; and
    应用判断单元,其被配置为基于仿真结果判断是否在所述工厂中应用所述预期修改数据。An application judgment unit configured to judge whether to apply the expected modification data in the plant based on a simulation result.
  15. 根据权利要求9所述的装置,进一步包括:The apparatus of claim 9, further comprising:
    查询条件获得单元,其被配置为获得针对所述制造仿真模型的查询条件;以及a query condition obtaining unit configured to obtain query conditions for the manufacturing simulation model; and
    图谱数据读取单元,其被配置为从所述知识图谱中读取与所述查询条件匹配的图谱数据。A graph data reading unit, which is configured to read graph data matching the query condition from the knowledge graph.
  16. 根据权利要求9所述的装置,其中,所述制造仿真模型的所述知识图谱被保存在图数据库中,并且,所述图数据库还用于保存与所述工厂有关的其它知识图谱。9. The apparatus of claim 9, wherein the knowledge graph of the manufacturing simulation model is stored in a graph database, and the graph database is further used to store other knowledge graphs related to the factory.
  17. 计算设备,包括:Computing equipment, including:
    处理器;以及processor; and
    存储器,其用于存储计算机可执行指令,当所述计算机可执行指令被执行时使得所述处理器执行根据权利要求1-8中任一项所述的方法。a memory for storing computer-executable instructions which, when executed, cause the processor to perform the method of any of claims 1-8.
  18. 计算机可读存储介质,所述计算机可读存储介质具有存储在其上的计算机可执行指令,所述计算机可执行指令用于执行根据权利要求1-8中任一项所述的方法。A computer-readable storage medium having computer-executable instructions stored thereon for performing the method of any of claims 1-8.
  19. 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存储介质上,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1-8中任一项所述的方法。A computer program product tangibly stored on a computer-readable storage medium and comprising computer-executable instructions which, when executed, cause at least one processor to perform the execution according to claims 1-8 The method of any of the above.
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