CN117371201A - Knowledge-graph-based construction method for digital twin bodies of household paper folding equipment - Google Patents
Knowledge-graph-based construction method for digital twin bodies of household paper folding equipment Download PDFInfo
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Abstract
The invention provides a method for constructing a digital twin body of household paper folding equipment based on a knowledge graph, and relates to the technical field of equipment fault detection. The method for constructing the digital twin body of the household paper folding equipment based on the knowledge graph specifically comprises the following steps: s1, unifying formats; s2, constructing a knowledge graph body structure; s3, constructing a knowledge graph data structure; s4, modeling and demonstration; s5, fault monitoring. The method can carry out digital twin modeling on the large-scale equipment, realize 3D visual display of the large-scale equipment, construct a complex relation network between each part of the equipment and equipment faults by utilizing a knowledge graph technology, finally reflect the state and the behavior of the actual large-scale equipment based on the digital twin of the knowledge graph, monitor and pre-judge the performance of the equipment, realize the function of quickly and accurately detecting and monitoring the equipment faults, and have simple operation and easy operation.
Description
Technical Field
The invention relates to the technical field of equipment fault detection, in particular to a method for constructing a digital twin body of a household paper folding equipment based on a knowledge graph.
Background
The paper for daily use is an essential consumable in the life of people, the paper making industry in China is in the period of capacity removal and structure adjustment transformation, and the modernization transformation and upgrading of the equipment production process by paper making enterprises are key to not be eliminated. The household paper production process comprises pulp preparation, forming and drying, calendaring and processing, packaging and quality inspection, the folding equipment is necessary large equipment in calendaring and processing process flow, the folding equipment is complex in mechanical structure, complex coupling exists among all parts of the equipment, the difficulty of enterprises in personnel training is increased, the number of sensors of the equipment is large, and when equipment faults occur, maintenance personnel are difficult to accurately judge the equipment faults rapidly, and the maintenance cost of the enterprises is increased. Therefore, intelligent management of equipment is one of important means for effectively improving the production efficiency of paper enterprises, saving the cost and improving the competitiveness.
Because of actual production activities, enterprise personnel do not have enough time to know the composition of large-scale equipment, cannot form detailed and comprehensive understanding on the large-scale equipment at multiple angles, and certain difficulty exists in training the enterprise personnel. Meanwhile, the current enterprise equipment fault diagnosis method mainly depends on a signal processing method and an expert experience diagnosis method. The signal-based processing method is insufficient in early potential fault diagnosis capability and poor in timeliness. The fault diagnosis method based on expert experience can utilize the long-term accumulation of the expert to rapidly locate faults, but enterprises do not precipitate and convert expert experience knowledge, and a professional fault knowledge base and a fault diagnosis standard flow are not formed.
Aiming at the problems, the invention provides a method for constructing a digital twin body of a household paper folding device based on a knowledge graph.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a method for constructing a digital twin body of a household paper folding device based on a knowledge graph, which solves the problem of low efficiency of the existing method for judging the device faults.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: the method for constructing the digital twin body of the household paper folding equipment based on the knowledge graph specifically comprises the following steps:
s1, unifying formats
Unifying formats of equipment product document data, expert experience knowledge data, equipment operation data and maintenance record data;
s2, constructing a knowledge graph body structure
Constructing a knowledge graph body structure in the equipment body structure, the fault body structure and the diagnosis analysis body structure, and then forming a global body structure through the equipment components;
s3, constructing a knowledge graph data structure
Setting a triplet of an ontology basic storage structure, wherein the triplet mainly comprises two types of nodes, attributes, attribute values and nodes, relations and nodes, node data are defined in the equipment ontology structure, and node data are defined in the fault ontology structure;
s4, modeling and demonstration
Carrying out digital twin modeling on mechanical relations among folding machine equipment, parts and components, realizing 3D visual display of the running state of large equipment, training service scenes aiming at personnel mechanisms, realizing equipment explosion functions, carrying out split dynamic demonstration on folding equipment, and providing enterprise personnel with multiple-angle understanding of equipment mechanisms;
s5, fault monitoring
The real-time data and knowledge of the knowledge graph are fused through digital twinning, equipment real-time monitoring and early warning are carried out, when the equipment alarms, equipment components pointed by alarm signal entities are rapidly positioned, fault reasons are sequentially searched according to the fault reasons pointed by the equipment component entities, operation and maintenance countermeasures are given according to the associated operation and maintenance bodies, and if the associated fault reasons do not exist, the knowledge graph is updated in a complementary mode.
Preferably, the step S2 includes that in the device body structure, the device is divided into three layers according to the mechanical structure of the folding machine device, and the device is a folding machine, and the components are a trimming part, a cutting part, a folding part and a pre-pressing part.
Preferably, the step S2 is included in the fault body structure, and is constructed by using equipment components, signals, fault description phenomena, fault types, fault reasons and solutions from top to bottom.
Preferably, the step S2 includes constructing in the diagnostic analysis body structure with the device component, the component signal, and the signal state; in the operation and maintenance body structure, equipment components, fault problems and operation and maintenance schemes are taken as the body structure.
Preferably, the node attributes in the triplet in the step S3 include static attributes and dynamic attributes, the static attributes are ID, name and type, the dynamic attributes are voltage, current and temperature, the relationships among the nodes include a same-level relationship and a cross-level relationship, and the cross-level relationship generally represents the inclusion relationship among different level entities in the knowledge graph.
Preferably, in the step S3, the device body structure defines node data as a folder, a component, and a part, the node attribute is ID, name, and type, and the node relationship is set to be a cross-level device inclusion relationship and a device operation transmission relationship of the same level.
Preferably, in the step S3, the node data is defined as a device component, a device parameter and a fault description in the fault ontology structure, the node attribute is set with an ID, a name, a unit and a data type, and the node relationship is set as a cross-level inclusion relationship.
(III) beneficial effects
The invention provides a method for constructing a digital twin body of a household paper folding device based on a knowledge graph. The beneficial effects are as follows:
the invention provides a method for constructing a digital twin body of a household paper folding device based on a knowledge graph, which can realize 3D visual display of the device and construct a device explosion application scene by carrying out digital twin modeling on a large-scale device folding machine, split and demonstrate the device, and support enterprises to carry out device mechanism training on personnel.
Drawings
FIG. 1 is a schematic flow chart of a method for constructing a digital twin body of a household paper folding device based on a knowledge graph;
fig. 2 is a schematic diagram of the knowledge graph structure of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples:
as shown in fig. 1-2, the embodiment of the invention provides a method for constructing a digital twin body of a household paper folding device based on a knowledge graph, which specifically comprises the following steps:
s1, unifying formats
Collecting data such as equipment product documents, expert experience knowledge, equipment operation data, maintenance records and the like, and unifying data formats;
s2, constructing a knowledge graph body structure
And constructing a knowledge graph body structure. In the equipment body structure, according to the mechanical structure of the folding machine equipment, the equipment is divided into three layers of equipment (folding machine), parts (trimming part, cutting part, folding part, pre-pressing part and other parts) and parts for construction; in the fault body structure, constructing by using equipment components, signals, fault description phenomena, fault types, fault reasons and solution countermeasures from top to bottom; in the diagnosis analysis body structure, constructing by using equipment components, component signals and signal states; in the operation and maintenance body structure, equipment components, fault problems and operation and maintenance schemes are taken as the body structure. Finally, forming a global body structure through the equipment assembly;
s3, constructing a knowledge graph data structure
And constructing a knowledge graph data structure, wherein the basic storage structure of the ontology is a triplet and mainly comprises two types of (nodes, attributes, attribute values) and (nodes, relations and nodes). The node attributes comprise static attributes (ID, name, type) and dynamic attributes (voltage, current and temperature), and the relationships between the nodes comprise same-level relationships and cross-level relationships, and the cross-level relationships generally represent inclusion relationships between different level entities in the knowledge graph. In the device body structure, defining node data (folding machine, component and part), wherein the node attribute has ID, name, type and the like, and the node relation has a cross-level device containing relation and a same-level device operation transmission relation; in the fault body structure, defining node data (equipment components, equipment parameters, fault description and the like), wherein node attributes comprise IDs, names, units, data types and the like, and node relations are cross-level inclusion relations;
s4, modeling and demonstration
Carrying out digital twin modeling on mechanical relations among folding machine equipment, parts and components, realizing 3D visual display of the running state of large equipment, aiming at personnel mechanism training service scenes, realizing equipment explosion function, carrying out split dynamic demonstration on folding equipment, and providing enterprise personnel with multi-angle understanding of equipment mechanism;
s5, fault monitoring
The digital twinning carries out real-time monitoring and early warning on equipment by fusing real-time data with knowledge graph knowledge, when the equipment gives an alarm, equipment components pointed by alarm signal entities are rapidly positioned, fault reasons are sequentially searched according to the fault reasons pointed by the equipment component entities, the fault reasons are rapidly analyzed, and operation and maintenance countermeasures are given according to the associated operation and maintenance bodies; if no related fault reasons exist, carrying out supplementary updating on the knowledge graph;
based on the above method, further supplementary explanation is as follows:
structured, semi-structured and unstructured data in the production process of large-scale equipment are collected, multi-source heterogeneous data are fused, and support is provided for the construction of subsequent knowledge maps;
constructing a large-scale equipment knowledge graph body structure, a fault body structure, a diagnosis analysis body structure and an operation and maintenance body structure, and forming a global body structure through equipment components;
constructing a knowledge graph data structure according to different application scenes, and defining entity data, entity attributes and entity relations;
carrying out digital twin modeling on the mechanism running state of the large-scale equipment, and reducing the motion relation of each part of the large-scale equipment in the production process;
the digital twinning is to fuse the real-time data with the knowledge graph, monitor the equipment in real time, pre-warn and analyze faults.
Through the method, the large-scale equipment can be subjected to digital twin modeling, and the equipment can be visually displayed at multiple angles. Meanwhile, by constructing a knowledge graph of the complex relationship between the mechanism structure and the equipment faults of the large-scale equipment of the folding machine, the digital twin can realize the real-time monitoring and analysis of the running state of the equipment by fusing the knowledge graph knowledge
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. The method for constructing the digital twin body of the household paper folding equipment based on the knowledge graph is characterized by comprising the following steps of:
s1, unifying formats
Unifying formats of equipment product document data, expert experience knowledge data, equipment operation data and maintenance record data;
s2, constructing a knowledge graph body structure
Constructing a knowledge graph body structure in the equipment body structure, the fault body structure and the diagnosis analysis body structure, and then forming a global body structure through the equipment components;
s3, constructing a knowledge graph data structure
Setting a triplet of an ontology basic storage structure, wherein the triplet mainly comprises two types of nodes, attributes, attribute values and nodes, relations and nodes, node data are defined in the equipment ontology structure, and node data are defined in the fault ontology structure;
s4, modeling and demonstration
Carrying out digital twin modeling on mechanical relations among folding machine equipment, parts and components, realizing 3D visual display of the running state of large equipment, training service scenes aiming at personnel mechanisms, realizing equipment explosion functions, carrying out split dynamic demonstration on folding equipment, and providing enterprise personnel with multiple-angle understanding of equipment mechanisms;
s5, fault monitoring
The real-time data and knowledge of the knowledge graph are fused through digital twinning, equipment real-time monitoring and early warning are carried out, when the equipment alarms, equipment components pointed by alarm signal entities are rapidly positioned, fault reasons are sequentially searched according to the fault reasons pointed by the equipment component entities, operation and maintenance countermeasures are given according to the associated operation and maintenance bodies, and if the associated fault reasons do not exist, the knowledge graph is updated in a complementary mode.
2. The knowledge-graph-based method for constructing the digital twin body of the household paper folding equipment is characterized by comprising the following steps of: the step S2 comprises the step of constructing equipment, a part and a part in three layers according to the mechanical structure of equipment of the folding machine in the equipment body structure, wherein the equipment is a folding machine, and the part comprises a trimming part, a cutting part, a folding part and a pre-pressing part.
3. The knowledge-graph-based method for constructing the digital twin body of the household paper folding equipment is characterized by comprising the following steps of: the step S2 includes constructing the fault body structure from top to bottom with the device components, signals, fault description phenomena, fault types, fault causes, and solutions.
4. The knowledge-graph-based method for constructing the digital twin body of the household paper folding equipment is characterized by comprising the following steps of: the step S2 comprises the steps of constructing equipment components, component signals and signal states in a diagnosis analysis body structure; in the operation and maintenance body structure, equipment components, fault problems and operation and maintenance schemes are taken as the body structure.
5. The knowledge-graph-based method for constructing the digital twin body of the household paper folding equipment is characterized by comprising the following steps of: the node attributes in the step S3 triplet comprise static attributes and dynamic attributes, the static attributes are ID, name and type, the dynamic attributes are voltage, current and temperature, the relationship among the nodes comprises a same-level relationship and a cross-level relationship, and the cross-level relationship generally represents the inclusion relationship among different level entities in the knowledge graph.
6. The knowledge-graph-based method for constructing the digital twin body of the household paper folding equipment is characterized by comprising the following steps of: and in the step S3, the equipment body structure defines node data as a folder, a part and a part, the node attribute of the node data is ID, name and type, and the node relation is set as a cross-level equipment inclusion relation and a same-level equipment operation transmission relation.
7. The knowledge-graph-based method for constructing the digital twin body of the household paper folding equipment is characterized by comprising the following steps of: and step S3, defining node data in the fault ontology structure as equipment components, equipment parameters and fault descriptions, wherein the node attributes are provided with IDs, names, units and data types, and the node relations are set as cross-level inclusion relations.
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