A knowledge graph-based hole making process parameter recommendation method

Abstract

The application discloses a hole making process parameter recommendation method based on a knowledge graph, uses an optimized seven-step method, provides an ontology evaluation system and an ontology updating method, so that the hole making process ontology can be updated and iterated according to different processing requirements; the application adopts a fine-tuning UIE model to perform knowledge extraction on hole making process text data, and only needs to fine-tune the UIE model with small sample data to obtain good extraction accuracy, reduces the labeling cost of hole making process text data knowledge extraction, and improves the extraction efficiency; the application designs an entity alignment algorithm based on text similarity and a word table mapping and a processing case duplicate filtering algorithm, reduces the repetition rate of knowledge in the knowledge graph, and improves the retrieval efficiency.

Description

Technical Field

[0001] The present invention relates to the field of hole-making process technology, and in particular to a method for recommending hole-making process parameters based on a knowledge graph. Background Art

[0002] Aerospace technology places increasingly stringent demands on aircraft assembly techniques, with assembly quality directly impacting aircraft performance and safety. In aircraft manufacturing, 70% of assembly tasks involve mechanical connections, primarily riveting and bolting, both of which require hole preparation. Therefore, the efficiency and accuracy of recommended hole preparation process parameters significantly impact the speed and quality of aircraft assembly.

[0003] Currently, parameter recommendations for hole-making processes primarily rely on traditional methods such as lookup tables, orthogonal experiments, and expert recommendations. These methods struggle to quickly and accurately recommend process parameters, have poor autonomous reasoning capabilities, and struggle to meet the demands of highly automated hole-making processes. By storing hole-making process data and knowledge in a graph structure, search efficiency can be improved while also enabling the integration of reasoning algorithms to mine hidden relationships within the data. Existing knowledge graph-based parameter recommendation methods in the hole-making process field suffer from shortcomings such as poorly constructed hole-making process ontology and high training and annotation costs and poor performance of the knowledge extraction algorithms employed. Summary of the Invention

[0004] To address the shortcomings of the above-mentioned hole-making process parameter recommendation method, the present invention proposes a hole-making process parameter recommendation method based on a knowledge graph. A hole-making process parameter recommendation method based on a knowledge graph includes:

[0005] Step S1, constructing the body of the hole making process:

[0006] Data and knowledge related to the hole-making process were collected, and a needs analysis was completed for the construction of the hole-making process ontology. Based on this, the target scope of the hole-making process ontology was clarified. Ontologies related to hole-making processes were reused, and core concepts in the hole-making process domain were refined. The classification relationships between concepts were clarified, and the relationships between concepts and their attributes were defined. Finally, an ontology evaluation was conducted to determine whether the ontology needed to be updated. If the ontology passed the evaluation, it was visualized using Protégé software to form the model layer of the knowledge graph.

[0007] Step S2, using a pattern-based knowledge extraction method to complete the knowledge extraction of the hole-making process table data; after a one-to-one correspondence between the table data header and the concepts in the pattern layer, the data in the table is converted into triples based on the relationship between the concepts in the pattern;

[0008] Step S3: Use the UIE model to extract knowledge from the hole-making process text data: Clean the collected text data to reduce interference from irrelevant data. Annotate the collected text data based on the knowledge graph model layer, and use the annotated data to fine-tune the UIE model. Finally, use the trained UIE model to extract triples from the hole-making process text data.

[0009] Step S4: Knowledge fusion and storage of hole-making process knowledge graph based on Neo4j graph database:

[0010] Entity fusion is achieved through similarity and a mapping table between the Chinese material name and its English abbreviation. Relational fusion of the same processing cases is achieved by comparing the data parameters of the processing cases. Finally, the fused hole-making process knowledge graph is stored and visualized in the Neo4j database.

[0011] In step S5, based on the processing requirements, a search is performed in the constructed knowledge graph to obtain the final recommended processing parameters.

[0012] Preferably, the step S1 specifically includes:

[0013] Step S1.1: Consider the construction requirements of the hole-making process ontology and clarify the target scope of the hole-making process ontology:

[0014] First, the domain of the hole-making process itself is determined from the demand analysis of the hole-making process. Second, the quality requirements of the hole-making process itself are determined.

[0015] Step S1.2: before extracting the core concepts of the hole-making process and constructing the ontology of the hole-making process, you can refer to the existing ontology and reuse it;

[0016] In step S1.3, the domain knowledge must be understood first, and then as many domain concepts as possible that need to be expressed to the user are listed; these concepts are mainly formed from the collected hole-making process data;

[0017] Step S1.4: Based on the concepts of the hole-making process refined in step S1.3, these concepts are sorted out to form a conceptual framework of the hole-making process:

[0018] First, the hole-making process is divided into five basic concepts: processing holes, processing plate materials, processing tools, processing quality assessment, and processing process parameters. Then, the basic concepts are further subdivided to form specific process parameter concepts and a clear classification relationship. Finally, the concepts are analyzed to see whether they are correctly classified and whether they meet the principle of minimal ontology. If not, they are further sorted out until a complete hierarchical system of hole-making process concepts is formed.

[0019] Step S1.5, define the relationships and attributes between hole-making process concepts:

[0020] A relationship refers to the possible connection between two concepts. This relationship can be categorized into object relationships and data type relationships. For a newly defined machining case class, the relationship between it and the machining hole, machining tool, machining process parameters, and machining quality is used to describe a specific machining case.

[0021] Step S1.6, ontology evaluation:

[0022] The ontology evaluation method in this paper mainly evaluates the semantic depth, knowledge scope, reliability, and portability and scalability of the ontology. If the ontology evaluation fails, it will be rebuilt and analyzed starting from step S1.1;

[0023] In step S1.7, the hole-making process ontology is visualized using the Protégé ontology building tool.

[0024] Preferably, the step S2 specifically includes:

[0025] The table data comes from an Excel file that records hole-making process cases. The file consists of a table header and cells that express specific hole-making process data. Each row represents a feasible processing case. When extracting table data, a row-by-row extraction method is used: first, each row corresponds to an entity of the processing case class, and its attribute is the row number. Then, the other cells in the row are analyzed in turn. Each cell corresponds to an entity, and the class of the entity is the class corresponding to the cell header in the schema layer. The attribute of the entity is the specific value of the cell. Finally, a triple is formed with the processing case entity as the head entity, the relationship between the cell class and the processing case class in the schema layer as the relationship, and the cell entity as the tail entity. The triple is then stored in a json file. Each line of the json file contains all triples of the same processing case entity.

[0026] Preferably, the step S3 specifically includes:

[0027] Step S3.1, data cleaning of the collected literature;

[0028] Step S3.2: Randomly select a set number of cleaned text data and perform data annotation on the Doccano platform. The annotated labels and relationships are derived from the categories and relationships in the model layer.

[0029] Step S3.3, deployment and fine-tuning of the UIE model, specifically: export the labeled data from Doccano as a JSON file; during training, divide the labeled data into a training set, a validation set, and a test set; and introduce a set proportion of negative examples into the training set, that is, introduce entities or relations that should not be included in the extraction results, so as to learn the ability to avoid errors during fine-tuning;

[0030] In step S3.4, the fine-tuned UIE model is used to extract hole-making process knowledge from the unlabeled text and convert the obtained hole-making process knowledge into easily processable triples. This process requires applying the fine-tuned UIE to extract process knowledge and converting the UIE output into a triple format.

[0031] When using UIE, you first need to set the structured extraction language to specify the entities and relations that need to be extracted, which is in the form of {head entity type: [relation 1, relation 2]}; according to the constructed pattern layer, the structured extraction language for hole-making process knowledge extraction is defined as {processing case: [tool type is, tool material is, aperture is, processing plate material is, rotation speed is, feed speed is, top angle is, recommended back angle is, processing plate thickness is, helix angle is, roughness is, burr height is, aperture error is, tool diameter is, feed per revolution is]}; then call the fine-tuned UIE model to obtain the UIE predictor; the sentence to be predicted is used as the input of the predictor, and its output is the extracted entities and relations; finally, the extraction result is converted into a triple structure in the knowledge graph.

[0032] Preferably, the collected text data is cleaned to reduce the interference of irrelevant data.

[0033] Preferably, in step 1, when the processing target changes and the hole-making process body needs to be updated on a large scale, the construction is restarted from S1.1; when the hole-making processing technology is updated and the hole-making process body needs to be updated on a small scale, the core concepts of the field are revised starting from S1.3.

[0034] Preferably, in step 4, the table data triples and the text data triples are entity aligned and duplicate process cases are filtered, and the triples are stored in the Neo4j graph database to form a hole-making process knowledge graph, which specifically includes:

[0035] Step S4.1: Select Neo4j, a Java-based graph database, as the storage platform for the hole-making process knowledge graph.

[0036] Step S4.2, aligning the entities of the knowledge extraction results according to similarity;

[0037] Step S4.3, filtering the processed cases;

[0038] In step S4.4, the hole process knowledge graph is queried, and the query nodes and relationships are returned through a visual interface.

[0039] Preferably, the step 5 specifically includes:

[0040] First, input the name of the processing plate material to be queried, evaluate the similarity between the material name and the attribute values ​​of all processing plate material nodes in the graph, and output multiple nodes with the highest similarity; then, the user confirms the material name to be queried at last; find the processing plate material node with the material name as the attribute value, then find all the processing case nodes related to it, output the aperture nodes related to these processing cases, and then the user selects the aperture of the hole; finally, output the processing case nodes that have a relationship with the selected aperture node and processing plate material node at the same time, and then find other nodes that have a relationship with these process case nodes. The attribute values ​​of these nodes are the hole-making process parameters recommended based on the query knowledge graph.

[0041] Preferably, in step S3.4, a json file is used to store the triples, and one line of the json file contains all triples of the same processing case entity.

[0042] Preferably, when aligning the entities of the knowledge extraction results in step S4.2, the similarity between the texts is calculated by taking the average value of the Jaccard similarity and the Levenshtein similarity.

[0043] Preferably, in step S4.3, the filtering weight of the processing case specifically includes:

[0044] First, create a two-dimensional list variable and use the processing case entity of the row to create a processing case node; then, query the tail node in all triples of this row. If the tail node is found to already exist, use the existing node to establish a relationship and find the processing case node with the same relationship as the tail node, and store all the found nodes in a row of the list variable. If the tail node does not exist, create a new tail node and relationship, and add a blank row to the list variable; after completing the traversal, if each row of the table variable has a common node, it means that this processing case node already exists and does not need to be created, so this processing case node is deleted.

[0045] In view of the above technical solutions, the present invention has the following beneficial effects:

[0046] 1. The present invention uses an optimized seven-step method to provide an ontology evaluation system and an ontology update method, so that the hole-making process ontology can be updated and iterated according to different processing requirements.

[0047] 2. The present invention uses a fine-tuned UIE model to extract knowledge from the hole-making process text data. Only a small sample data is needed to fine-tune the UIE model to obtain a good extraction accuracy, which reduces the annotation cost of knowledge extraction from the hole-making process text data and improves the extraction efficiency.

[0048] 3. The present invention designs an entity alignment algorithm and a processing case filtering algorithm based on text similarity and vocabulary mapping to reduce the repetition rate of knowledge in the knowledge graph and improve retrieval efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is an overall flow chart of the method for constructing a knowledge graph for hole-making technology according to the present invention;

[0050] Figure 2 This is a flow chart of the optimized seven-step method of the present invention;

[0051] Figure 3 A visualization result diagram of the hole-making process constructed by the present invention;

[0052] Figure 4 This is a flow chart of the entity alignment algorithm of the present invention;

[0053] Figure 5 This is a flowchart of the weight filtering algorithm for processing cases of the present invention;

[0054] Figure 6 This is the visualization result of the knowledge graph of the hole-making process of the present invention;

[0055] Figure 7 This is a recommended flow chart of the hole-making process parameters of the present invention. DETAILED DESCRIPTION

[0056] In order to describe the above steps S1 to S5 in more detail, they are described in detail below with reference to the accompanying drawings and examples. The specific implementation is as follows:

[0057] Step S1, using the optimized seven-step method to construct the ontology of the hole-making process. To construct the hole-making process knowledge graph, the ontology must be constructed first, that is, the model layer must be constructed to obtain the knowledge system of the hole-making process and provide guidance for knowledge extraction. The specific steps for constructing the hole-making process ontology according to the optimized seven-step method are as follows: Steps S1.1 to S1.7, and the flow chart is as follows: Figure 2 shown.

[0058] Step S1.1, clarify the target scope of the hole-making process ontology. The ontology development first needs to comprehensively consider the construction requirements of the hole-making process ontology and determine the target scope of the ontology construction. First, from the demand analysis of the hole-making process, it can be known that the field of its ontology is mainly limited to the field of aviation hole-making processes mainly based on drilling processing. Secondly, the hole-making process ontology is mainly used to recommend hole-making process parameters. The parameter recommendation depends on the correct processing case. What is important in a case is the properties of the hole-making processing plate, the hole-making processing parameters, the processing tools, and the quality requirements for the holes. Finally, the above two points can be updated as the needs of hole-making process parameter recommendations are met.

[0059] Step S1.2: Reuse an existing ontology. Before refining the core concepts of the hole-making process and constructing the hole-making process ontology, you can refer to existing ontologies and reuse them appropriately. Currently, there is no existing ontology for hole-making processes available for reference, so the cutting tool ontology was used as a reference during construction.

[0060] Step S1.3: Extract the core concepts of the hole-making process. Constructing the hole-making process ontology first requires understanding domain knowledge and then listing as many domain concepts as possible that need to be expressed to users. These concepts are primarily derived from the collected hole-making process data.

[0061] Step S1.4, define the conceptual hierarchy of the hole-making process. Based on the extraction of all the concepts of the hole-making process in step S1.3, this step is mainly to sort out these concepts to form a well-defined conceptual framework of the hole-making process. First, the hole-making process is divided into five basic concepts: processing holes, processing plate materials, processing tools, processing quality assessment, and processing process parameters. These basic concepts cover almost all parameters involved in the hole-making process. The basic concepts are then further subdivided to form specific process parameter concepts and a clear classification relationship. For example, the processing process parameters include special processes involved in the processing, the feed speed, rotation speed, and feed rate of the hole-making robot, and the feed rate if tool change or layered processing is required. Finally, analyze whether the concepts are correctly classified and whether the concepts meet the principle of minimum ontology. If not, sort them out again until a complete conceptual hierarchy of the hole-making process is formed.

[0062] In step S1.5, define the relationships and attributes between hole-making process concepts. A relationship is a possible connection between two concepts. These relationships can be categorized as object relationships and data type relationships. Data type relationships are often referred to as attributes, which are broadly defined as a type of relationship. A new machining case class is defined, and its relationships with the machining hole, machining tool, machining process parameters, and machining quality can be used to describe a specific machining case.

[0063] Step S1.6, ontology evaluation. The ontology evaluation method in this paper mainly evaluates the semantic depth, knowledge scope, reliability, portability and scalability of the ontology. If the ontology evaluation fails, it will be rebuilt and analyzed starting from step S1.1.

[0064] Step S1.7, ontology visualization. The constructed hole-making process ontology is visualized using the Protégé ontology construction tool. The visualization results are as follows: Figure 3 shown.

[0065] When the processing target changes and the hole-making process ontology needs to be updated on a large scale, it is rebuilt from S1.1; when the hole-making processing technology is updated and the hole-making process ontology needs to be updated on a small scale, the core concepts of the domain are revised starting from S1.3.

[0066] Step S2, knowledge extraction of pattern-based tabular data. The tabular data comes from an Excel file that records hole-making process cases. The file consists of a header and cells that express specific hole-making process data, and each row represents a feasible processing case. When extracting tabular data, the form of row-by-row extraction is adopted. The following takes the extraction of a row of data as an example. First, each row corresponds to an entity of the processing case class, and its attribute is the row number. Then, the other cells in the row are analyzed in turn. One cell corresponds to an entity. The class of the entity is the class corresponding to the cell header in the pattern layer, and the attribute of the entity is the specific value of the cell. Finally, a triple is formed with the processing case entity as the head entity, the relationship between the cell class and the processing case class in the pattern layer as the relationship, and the cell entity as the tail entity, and the triple is stored in a json file. Each line of the json file contains all triples of the same processing case entity.

[0067] In step S3, the fine-tuned UIE model is used to extract knowledge from the hole-making process text data. The text data comes from relevant literature that records hole-making process knowledge and cases. The UIE model is a pre-trained large language model. Fine-tuned using the hole-making process data, the UIE model can extract entities and relationships from the hole-making process text according to a pre-set structured extraction language. This step completes the fine-tuning of the UIE model for the hole-making process domain and uses the fine-tuned UIE to extract hole-making process text data and convert it into triples.

[0068] Step S3.1: Text data cleaning. First, the collected documents need to be cleaned. The steps include:

[0069] 1) Locate key text positions based on keywords;

[0070] 2) Convert all characters into half-width characters;

[0071] 3) Remove all spaces and line breaks;

[0072] Step S3.2: Training data annotation. Randomly select 50 cleaned text data and annotate them on the Doccano platform. The annotated labels and relationships are derived from the categories and relationships in the model layer. The detailed annotation steps are as follows:

[0073] 1) Create a project on the Doccano platform that supports sequence labeling tasks, and check "Allow entity overlap" and "Use relationship labels".

[0074] 2) Save the data to be uploaded in a txt file, separating the sentences to be annotated with line breaks. Select this file when uploading data to Doccano, and set the upload data type to TextLine.

[0075] 3) Based on the schema layer and the data to be annotated, determine the entities and relationships that need to be annotated. Add the corresponding tags in Doccano.

[0076] 4) Finally, mark the text. Select the corresponding entity by clicking the text with the mouse. Select two entities to select the relationship between them.

[0077] Step S3.3, UIE model deployment and fine-tuning. The UIE model is deployed on the local platform. The specific training environment is shown in Table 1.

[0078] Table 1 Training environment configuration

[0079]

[0080] Export the labeled data from Doccano as a JSON file. During training, split the labeled data into training, validation, and test sets in a 3:1:1 ratio. To adapt the fine-tuned UIE to information extraction tasks in different settings and address the exposure error of autoregressive teacher-forcing, a certain proportion of negative examples is introduced into the training set. This introduces entities or relations that should not be included in the extraction results, allowing the training set to learn to avoid errors during fine-tuning. The ratio of negative to positive examples is called the negative ratio.

[0081] The basic UIE model is selected as the training model, and the hyperparameters selected during training are shown in Table 2.

[0082] Table 2 Training hyperparameters

[0083] Parameter Type Parameter value negative ratio 0.05 Learning rate 5e-6 Batch size 8

[0084] After training, the F1 value in the validation set is 0.775, and the F1 value in the test set is 0.614.

[0085] In step S3.4, the fine-tuned UIE model is used for knowledge extraction. The fine-tuned UIE model is used to extract hole-making process knowledge from the unannotated text and convert the obtained hole-making process knowledge into easily processable triples. This process requires extracting process knowledge using the fine-tuned UIE and converting the UIE output into a triple format.

[0086] When using UIE, you first need to set the structured extraction language to specify the entities and relations to be extracted, which is in the form of {head entity type: [relation 1, relation 2]}. According to the pattern layer designed in Chapter 2, the structured extraction language for hole-making process knowledge extraction can be defined as {processing case: [tool type is, tool material is, aperture is, processing plate material is, rotation speed is, feed speed is, top angle is, recommended back angle is, processing plate thickness is, helix angle is, roughness is, burr height is, aperture error is, tool diameter is, feed per revolution is]}. Then call the fine-tuned UIE model to obtain the UIE predictor. The sentence to be predicted is used as the input of the predictor, and its output is the extracted entities and relations. Finally, the extraction result is converted into a triple structure in the knowledge graph.

[0087] In order to facilitate further processing of triple files, json files are used to store triples. One line of the json file contains all triples of the same processing case entity.

[0088] Step S4: Knowledge fusion and storage of the hole-making process knowledge graph based on the Neo4j graph database. This step aligns the table data triples and text data triples, filters duplicate process cases, and stores the triples in the Neo4j graph database to form the hole-making process knowledge graph.

[0089] Step S4.1: Select a storage platform for the hole-making process knowledge graph. Neo4j is a Java-based graph database. It can store graph-structured data directly in a network structure, rather than the table structure used by relational databases, so its data has more comprehensive transactional characteristics. Neo4j does not rely on schemas when storing data, has good data compatibility, and also has index-free adjacency features. Combined with the Cypher language provided by Neo4j, efficient queries on graph databases can be achieved. Neo4j's property graph structure can be used to store and visualize data in the knowledge graph. Therefore, the Neo4j graph database is used to store the hole-making process knowledge graph, and entities are expressed as nodes and relationships are expressed as edges.

[0090] Step S4.2: Entity alignment of knowledge extraction results. Text data comes from different documents and may have problems such as content duplication, semantic diversity, and uneven quality. Different documents may have large differences when describing the same physical quantity or physical quantity attribute. For example, for CFRP / Al laminate materials, some documents directly use "CFRP / Al" while other documents use "CFRP / Al alloy." Therefore, the same entity may appear in the system in different semantic forms. Organically integrating knowledge from multiple sources is a necessary condition for constructing a high-quality hole-making process knowledge graph.

[0091] When aligning entities, it is necessary to determine the similarity between entity attribute values. Evaluating the similarity between texts using Jaccard similarity and Levenshtein similarity can provide a more reasonable similarity determination.

[0092] If a and b represent two short texts, the Jaccard similarity calculation formula is as shown in Formula 1.

[0093]

[0094] Among them, A and B represent the character sets that make up the short text, |A∩B| represents the number of characters shared by the two sets, and |A∪B| represents the total number of characters in the two sets.

[0095] The Levenshtein similarity coefficient calculation formula is:

[0096]

[0097] Where d(A,B) is the Levenshtein minimum edit distance, and its formula is:

[0098]

[0099] Where |A| and |B| represent the lengths of strings A and B, respectively. A[1:] and B[1:] represent strings A and B after removing the first character. δ(A[1], B[1]) is a function that compares the first characters of A and B and returns 1 if they are the same, otherwise it returns 0.

[0100] The final similarity is the average of Jaccard similarity and Levenshtein similarity, and the formula is:

[0101]

[0102] Professional terms such as material names are often expressed by their English abbreviations. This step also uses the method of constructing a mapping vocabulary to avoid interference between different representation methods on similarity. Before comparing the similarity, it is necessary to construct new matching terms that replace the professional terms with their English abbreviations, and form a group of terms to be matched together with the original text. Then calculate the similarity between the attribute values ​​of the same type of nodes and each term in the group of terms to be matched, and take the maximum similarity as the similarity between the new node and the node. Sort the similarity between the new node and all the nodes in the graph. If there is a node with a similarity of 1, it means that the same node already exists and does not need to be created. Return the node for subsequent operations. If there is no node with a similarity of 1 but there is a node with a higher similarity, manually select whether to replace it. If replaced, return the selected node. If not replaced, return the newly created node. The specific flow chart is as follows Figure 4 shown.

[0103] Step S4.3, filtering duplicate processing cases. In order to prevent the same specific processing cases from being repeatedly established, it is necessary to implement filtering duplicate processing cases. Take reading a line of a json file as an example. First, establish a two-dimensional list variable and use the processing case entity of the line to establish a processing case node. Then, query the tail nodes in all triples of this line. If it is found that the tail node already exists, use the existing node to establish a relationship and find the processing case node with the same relationship as the tail node, and store all the found nodes in a line of the list variable. If the tail node does not exist, create a new tail node and relationship, and add a blank line to the list variable. After the traversal is completed, if each row of the table variable has a common node, it means that this processing case node already exists and does not need to be created, so delete this processing case node. The specific flow chart is as follows. Figure 5 shown.

[0104] Step S4.4: Visualization of the hole-making process knowledge graph. In the Neo4j database, write a Cypher statement to query the knowledge graph, and return the query nodes and relationships through the visualization interface. By entering the statement "MATCH(n)RETURN n" in the Neo4j database, you can get the overall visualization effect of the graph, as shown in the following example: Figure 6 shown.

[0105] Step S5: Parameter recommendation is achieved using the constructed knowledge graph. Other hole making process parameters are recommended based on the processing plate material and hole diameter. The flowchart of the query process is as follows: Figure 7 As shown. First, enter the name of the processing plate material to be queried, use the average Jaccard similarity and Levenshtein similarity to evaluate the similarity between the material name and the attribute values ​​of all processing plate material nodes in the graph, and output the nodes with the highest similarity. Then, the user confirms the name of the material to be queried. Find the processing plate material node with the material name as the attribute value, then find all the processing case nodes related to it, output the aperture nodes related to these processing cases, and then the user selects the aperture for the hole. Finally, output the processing case nodes that have a relationship with the selected aperture node and processing plate material node at the same time, and then find other nodes that have a relationship with these process case nodes. The attribute values ​​of these nodes are the hole-making process parameters recommended based on the query knowledge graph.

Claims

1. A method for recommending hole-making process parameters based on knowledge graph, characterized in that: include: Step S1, constructing the body of the hole making process: Collect data and knowledge related to the hole-making process and complete a demand analysis for the hole-making process ontology. Based on this, clarify the target scope of the hole-making process ontology and reuse ontologies related to the hole-making process. Then, refine the core concepts in the hole-making process field, clarify the classification relationships between concepts, define the relationships between concepts and the attributes of concepts, and finally, determine whether the ontology needs to be updated through ontology evaluation. If the ontology passes, use Protégé software to visualize the ontology and form the model layer of the knowledge graph. Step S2, using a pattern-based knowledge extraction method to complete the knowledge extraction of the hole-making process table data; after a one-to-one correspondence between the table data header and the concepts in the pattern layer, the data in the table is converted into triples based on the relationship between the concepts in the pattern; Step S3, using the UIE model to extract knowledge from the hole-making process text data: performing data cleaning on the collected text data; The collected text data is annotated based on the knowledge graph model layer, and the annotated data is used to fine-tune the UIE model. Finally, the trained UIE model is used to extract triples from the hole-making process text data. Step S4: Knowledge fusion and storage of hole-making process knowledge graph based on Neo4j graph database: Entity fusion is achieved through similarity and a mapping table between the Chinese material name and its English abbreviation. Relational fusion of the same processing cases is achieved by comparing the data parameters of the processing cases. Finally, the fused hole-making process knowledge graph is stored and visualized in the Neo4j database. Step S5: Search the constructed knowledge graph based on the processing requirements to obtain the final recommended processing parameters; In step S4, the table data triples and text data triples are entity aligned and duplicate process cases are filtered, and the triples are stored in the Neo4j graph database to form a hole-making process knowledge graph, which specifically includes: Step S4.1: Select Neo4j, a Java-based graph database, as the storage platform for the hole-making process knowledge graph. Step S4.2, aligning the entities of the knowledge extraction results according to similarity; Step S4.3, filtering the processed cases; Step S4.4, query the hole process knowledge graph and return the query nodes and relationships through the visual interface; In step S4.3, the filtering weight of the processing case specifically includes: First, create a two-dimensional list variable and use the processing case entity of the row to create a processing case node; then, query the tail node in all triples of this row. If the tail node is found to already exist, use the existing node to establish a relationship and find the processing case node with the same relationship as the tail node. Store all the found nodes in a row of the list variable. If the tail node does not exist, create a new tail node and relationship, and add a blank row to the list variable. After the traversal is completed, if there is a common node in each row of the table variable, it means that this processing case node already exists and does not need to be created. Delete this processing case node.

2. The method for recommending hole-making process parameters based on knowledge graph according to claim 1, characterized in that: The step S1 specifically includes: Step S1.1: Consider the construction requirements of the hole-making process ontology and clarify the target scope of the hole-making process ontology: First, the domain of the hole-making process itself is determined from the demand analysis of the hole-making process. Second, the quality requirements of the hole-making process itself are determined. Step S1.2: before extracting the core concepts of the hole-making process and constructing the ontology of the hole-making process, you can refer to the existing ontology and reuse it; In step S1.3, the domain knowledge must be understood first, and then as many domain concepts as possible that need to be expressed to the user are listed; these concepts are mainly formed from the collected hole-making process data; Step S1.4: Based on the concepts of the hole-making process refined in step S1.3, these concepts are sorted out to form a conceptual framework of the hole-making process: First, the hole-making process is divided into five basic concepts: processing holes, processing plate materials, processing tools, processing quality assessment, and processing process parameters. Then, the basic concepts are further subdivided to form specific process parameter concepts and a clear classification relationship. Finally, the concepts are analyzed to see whether they are correctly classified and whether they meet the principle of minimal ontology. If not, they are further sorted out until a complete hierarchical system of hole-making process concepts is formed. Step S1.5, define the relationships and attributes between hole-making process concepts: A relationship refers to the possible connection between two concepts. This relationship can be categorized into object relationships and data type relationships. For a newly defined machining case class, the relationship between it and the machining hole, machining tool, machining process parameters, and machining quality is used to describe a specific machining case. Step S1.6, ontology evaluation: The ontology evaluation method evaluates the semantic depth, knowledge scope, reliability, portability and scalability of the ontology. If the ontology fails the evaluation, it will be rebuilt and analyzed starting from step S1.

1. In step S1.7, the hole-making process ontology is visualized using the Protégé ontology building tool.

3. The method for recommending hole-making process parameters based on knowledge graph according to claim 1, characterized in that: The step S2 specifically includes: The table data comes from an Excel file that records hole-making process cases; the file consists of a header and cells that express specific hole-making process data, and each row represents a feasible processing case; when extracting table data, row-by-row extraction is adopted: first, each row corresponds to an entity of the processing case class, and its attribute is the row number; then, the other cells in the row are analyzed in turn, one cell corresponds to an entity, the class of the entity is the class corresponding to the cell header in the pattern layer, and the attribute of the entity is the specific value of the cell; finally, a triple is constructed with the processing case entity as the head entity, the relationship between the cell class and the processing case class in the pattern layer as the relationship, and the cell entity as the tail entity, and the triple is stored in a json file. Each line of the json file contains all triples of the same processing case entity.

4. The method for recommending hole-making process parameters based on knowledge graph according to claim 1, characterized in that: The step S3 specifically includes: Step S3.1, data cleaning of the collected literature; Step S3.2: Randomly select a set number of cleaned text data and perform data annotation on the Doccano platform. The annotated labels and relationships are derived from the categories and relationships in the model layer. Step S3.3, deployment and fine-tuning of the UIE model, specifically: export the labeled data from Doccano as a JSON file; during training, divide the labeled data into a training set, a validation set, and a test set; and introduce a set proportion of negative examples into the training set, that is, introduce entities or relations that should not be included in the extraction results, so as to learn the ability to avoid errors during fine-tuning; Step S3.4: Use the fine-tuned UIE model to extract hole-making process knowledge from the unlabeled text and convert the obtained hole-making process knowledge into easy-to-process triples. This process requires applying the fine-tuned UIE to extract process knowledge and converting the UIE output into a triple format. When using UIE, you first need to set the structured extraction language to specify the entities and relations that need to be extracted, which is in the form of {head entity type: [relation 1, relation 2]}; according to the constructed pattern layer, the structured extraction language for hole-making process knowledge extraction is defined as {processing case: [tool type is, tool material is, aperture is, processing plate material is, rotation speed is, feed speed is, top angle is, recommended back angle is, processing plate thickness is, helix angle is, roughness is, burr height is, aperture error is, tool diameter is, feed per revolution is]}; then call the fine-tuned UIE model to obtain the UIE predictor; the sentence to be predicted is used as the input of the predictor, and its output is the extracted entities and relations; finally, the extraction result is converted into a triple structure in the knowledge graph.

5. The method for recommending hole-making process parameters based on knowledge graph according to claim 1, characterized in that: In step S1, when the processing target changes and the hole-making process ontology needs to be updated on a large scale, it is rebuilt from S1.1; when the hole-making processing technology is updated and the hole-making process ontology needs to be updated on a small scale, the core concept of the domain is revised starting from S1.

3.

6. The method for recommending hole-making process parameters based on knowledge graph according to claim 1, characterized in that: The step S5 specifically includes: First, input the name of the processing plate material to be queried, evaluate the similarity between the material name and the attribute values ​​of all processing plate material nodes in the graph, and output multiple nodes with the highest similarity; then, the user confirms the material name to be queried at last; find the processing plate material node with the material name as the attribute value, then find all the processing case nodes related to it, output the aperture nodes related to these processing cases, and then the user selects the aperture of the hole; finally, output the processing case nodes that have a relationship with the selected aperture node and processing plate material node at the same time, and then find other nodes that have a relationship with these process case nodes. The attribute values ​​of these nodes are the hole-making process parameters recommended based on the query knowledge graph.

7. The method for recommending hole-making process parameters based on knowledge graph according to claim 4, characterized in that: In step S3.4, a json file is used to store the triples, and one line of the json file contains all triples of the same processing case entity.

8. The method for recommending hole-making process parameters based on knowledge graph according to claim 1, characterized in that: When aligning the entities of the knowledge extraction results in step S4.2, the similarity between the texts is calculated by taking the average of the Jaccard similarity and the Levenshtein similarity.

Images