Process identification method and device based on model characteristics, equipment and medium

Abstract

The invention provides a process identification method based on model features, which comprises the following steps that: a process knowledge graph is constructed by extracting a relation node triple in a standard process description text, and the node types of the graph comprise feature nodes, process nodes, equipment nodes, cutter nodes and clamp nodes; the relation between every two adjacent nodes comprises an available process, a preorder process, available equipment, initial machining, tool assembling and clamp assembling. Obtaining a geometric model of a to-be-machined mechanical part, extracting geometric feature attributes of the to-be-machined mechanical part, and determining a feature topological relation to obtain machining features; and starting from the feature nodes corresponding to the machining features by adopting an inverted sequence search method, screening out nodes meeting the machining requirements of the to-be-machined mechanical part from the process knowledge graph, and obtaining a complete process scheme. The method can deeply excavate the correlation between the geometric features of the part model and the machining process, improves the process identification accuracy, and is wide in applicability.

Description

Technical Field

[0001] The present invention belongs to the technical field of process identification, and specifically relates to a process identification method, device, equipment and medium based on model features and based on a process knowledge graph. Background Art

[0002] The traditional industrial process production process is highly dependent on manual experience, and the design and analysis usually rely on the knowledge reserves and experience accumulation of operators. More importantly, the traditional method lacks the ability to deeply analyze process data, and cannot mine hidden patterns or correlations, which limits the space for process optimization. As modern industry gradually moves towards diversification, customization and complexity, this method has been difficult to meet the needs of efficient and accurate processes, and is prone to errors, affecting the flexibility and timeliness of production. In order to effectively solve the above problems, knowledge graph technology has been introduced into the field of mechanical processing manufacturing to optimize process identification and design processes. The knowledge graph stores the geometric information of parts, processing data and their relationships through a computer system, and can intelligently search and automatically identify the required processes. As a structured semantic knowledge base, the knowledge graph can describe concepts and their relationships in the physical world in a symbolic way. Its core component is the "entity-relationship-entity" triple, which also contains the relationship between entities and their related attribute-value pairs. Through these relationships, a network of knowledge structures is formed between entities.

[0003] In mechanical processing manufacturing, the application of knowledge graph is particularly important. It can not only aggregate and organize complex process data, but also quickly respond to various process queries by analyzing part features, processing requirements and historical data, and perform intelligent reasoning and recommendations. It can effectively improve the efficiency and accuracy of process design, reduce manual dependence, and improve the automation level and decision support capabilities of the processing process.

[0004] At present, some scholars have applied knowledge graph technology to the manufacturing field, but most studies focus on the construction technology of process knowledge graphs and fail to give full play to its advantages for practical application. Invention patent application CN118468004A discloses a method for mechanical processing process identification of micro-roughness parts. Through dimensionality reduction processing, feature extraction and fusion, and combined with neural network technology, a model that can effectively identify mechanical processing processes is established, thereby improving the processing accuracy of identifying micro-roughness parts. However, although this method improves the accuracy of process identification, the application of this method is mainly concentrated in specific processing identification scenarios. Its "black box" characteristics make the decision-making process opaque and cannot clearly explain the reasoning logic behind process identification. At the same time, the model cannot capture the deep semantic relationship between processes, which limits its application in complex process chains. Invention patent application CN117236432A discloses a method and system for constructing a manufacturing process knowledge graph for multimodal data. The invention establishes a solid model of the workpiece and its related features based on a three-dimensional CAD model and drawing data, enriches the data type of the knowledge graph, and enhances the accuracy and operability of the knowledge graph data. Although the invention integrates multimodal data in the knowledge graph and solves the problem of incomplete data coverage in the construction of the previous manufacturing process knowledge graph, it does not apply the manufacturing process knowledge graph to a deeper level. Invention patent application CN116661389A discloses a method and system for identifying the four new things in diesel engine parts technology. "Four new" refers to new processes, new materials, new equipment and new technologies. This method traverses the existing technology database, counts and identifies the "four new" contents involved in the production process, and quickly confirms the application status of the new technology. Although the patent can efficiently identify the "four new" elements in diesel engine manufacturing, it also fails to provide recommendations for subsequent processing plans and lacks support for intelligent decision-making.

[0005] In general, existing technologies have made important progress in the construction of manufacturing process knowledge graphs and process identification. However, these technologies generally have a common shortcoming, that is, the lack of in-depth application of process knowledge graphs, especially in the subsequent process plan recommendation. Although the knowledge graph effectively organizes and associates a large amount of process data, it fails to fully utilize this information to generate intelligent process plans. This defect limits the application potential of knowledge graphs in actual manufacturing. The decision support value of process knowledge graphs should be further explored and utilized to improve their practicality and effectiveness in the actual manufacturing process. Summary of the invention

[0006] In order to solve the deficiencies in the prior art, the purpose of the present invention is to provide a process identification method, device, equipment and medium based on model features, which can flexibly adapt to various process conditions, deeply explore the correlation between the geometric features of the model of the mechanical parts to be processed and the processing technology, and improve the accuracy of process identification.

[0007] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0008] A first aspect of the present invention provides a process identification method based on model features, comprising:

[0009] Obtain process-related data, pre-process it, and obtain standardized process description text;

[0010] A process knowledge graph is constructed by extracting the relationship node triples in the process description text. The node types constituting the process knowledge graph include: feature nodes, process nodes, equipment nodes, tool nodes and fixture nodes; the relationship between two adjacent nodes includes: "available process", "previous process", "available equipment", "initial processing", "equipped tool" and "equipped fixture";

[0011] Acquire the geometric model of the mechanical part to be processed, extract its geometric feature attributes and determine the feature topological relationship, and obtain the mechanical processing features of the mechanical part model to be processed;

[0012] A reverse search method is adopted to start from the feature nodes corresponding to the machining features, and the nodes that meet the machining requirements of the mechanical parts to be machined are screened out from the process knowledge graph to obtain a complete process plan.

[0013] In some embodiments, the process-related data includes part processing characteristics, material information, process requirements and equipment parameters; the part processing characteristics include specific requirements for the geometric shape, dimensional tolerance and surface roughness of the mechanical parts to be processed; the material information involves the physical and chemical properties of the materials used for the parts; the process requirements include the operating conditions during the processing, including the process route, processing steps and the tools used; the equipment parameters include the type, specifications and processing accuracy of the equipment.

[0014] In some embodiments, the process-related data is divided into two categories: structured process data and unstructured process data.

[0015] Preprocessing of structured process data includes: data cleaning, removing redundancy, correcting erroneous information, and filling missing values ​​to ensure data integrity and accuracy; standardizing data from different sources, unifying data formats from different sources, and converting categorical data into analyzable numerical forms; normalizing numerical data to eliminate the impact of different dimensions;

[0016] The preprocessing of unstructured process data includes: word segmentation, part-of-speech tagging, regularization and stop word filtering.

[0017] In some embodiments, in the process knowledge graph, the feature node represents the feature of the mechanical part to be processed; the process node represents the optional process corresponding to the feature node, and the attributes maintained inside each process node include processing accuracy and surface roughness; the equipment node represents the available processing equipment matching the process, and each equipment node maintains the relevant attributes of the respective equipment; the tool node represents the available processing tools matching the equipment, and each tool node stores the relevant attributes of the respective tools; the fixture node represents the available fixture matching the equipment, and each fixture node stores the characteristics and applicable scope of the respective fixture;

[0018] The “available process” indicates that a certain feature of the mechanical parts to be processed can be processed by a certain process; the “previous process” indicates the order of processes; the “available equipment” indicates that a certain process can be processed by a certain equipment; the “initial processing” indicates that a certain process is the first step in a certain feature processing route; the “equipped tool” indicates that a certain equipment can be equipped with a certain tool; the “equipped fixture” indicates that a certain equipment can be equipped with a certain fixture.

[0019] In some embodiments, the machining features of the machine part model to be machined are obtained according to the following steps:

[0020] Step S31, importing the geometric model of the mechanical part to be processed through a supported file format, ensuring that the required geometric data is converted into a processable format;

[0021] Step S32, extracting all geometric features of the mechanical part to be processed from the geometric model, adding entity attributes to each surface in the geometric model, and predefining the processing accuracy and surface roughness;

[0022] Step S33: classify the extracted geometric features according to the machining requirements, and identify the geometric features that meet the machining requirements as machining features;

[0023] Step S34, extracting key attributes of the processing features, including size, accuracy and surface roughness;

[0024] Step S35: Detect adjacent features of all extracted geometric features using normal vectors and boundary points, construct topological relationships, use unique identifiers of processing features as nodes, and connections between adjacent processing features as edges, analyze dependencies between processing features using spatial positions of geometric features, and identify processing sequences between adjacent processing features;

[0025] Step S36: Arrange the extracted machining features and their related attributes and topological relationships into structured information to obtain the machining features of the mechanical part model to be machined.

[0026] In some embodiments, in step S35, the specific steps of identifying the processing order between adjacent processing features include:

[0027] Assuming that the first processing feature and the second processing feature are in a set common plane, a topological analysis is performed on the closed curves of the first processing feature and the second processing feature: if the closed curve of the second processing feature is completely within the closed curve of the first processing feature, the processing order of the first processing feature is superior to that of the second processing feature; if the closed curves of the first processing feature and the second processing feature only partially overlap, the processing features corresponding to the overlapping area or the processing features with a larger depth in three-dimensional space are preferentially processed; if the closed curves of the first processing feature and the second processing feature do not overlap, it indicates that the first processing feature and the second processing feature are independent in terms of geometric features, and the processing order of the two is arranged according to the overall process flow.

[0028] In some embodiments, the method of using reverse search starts from the feature node corresponding to the machining feature, selects nodes that meet the machining requirements of the mechanical parts to be machined from the process knowledge graph, and obtains a complete process plan, specifically including:

[0029] Enter the process knowledge graph from the target feature node, and find the corresponding process nodes in turn through the "available process" relationship. In this process, determine whether each process node meets the process method required for processing. If it does not meet the requirements, directly end the search for the current path. If it meets the requirements, perform the next search based on the "previous process" relationship. When the found process node has an "initial processing" relationship, determine that the process method corresponding to the obtained node is the last process; find the path between the target feature node and the initial node corresponding to the last process, thereby completing the process plan.

[0030] The second aspect of the present invention provides a device based on the process identification method described in any embodiment of the first aspect of the present invention, comprising:

[0031] A first module stores a standardized process description text, wherein the standardized process description text is data obtained by preprocessing the acquired process-related data;

[0032] The second module is used to construct a process knowledge graph by extracting the relationship node triples in the process description text. The node types constituting the process knowledge graph include: feature nodes, process nodes, equipment nodes, tool nodes and fixture nodes; the relationship between two adjacent nodes includes: "available process", "previous process", "available equipment", "initial processing", "equipped tool" and "equipped fixture";

[0033] The third module is used to obtain the geometric model of the mechanical part to be processed, extract its geometric feature attributes and determine the feature topological relationship, and obtain the mechanical processing features of the mechanical part model to be processed;

[0034] The fourth module is used to adopt a reverse search method starting from the feature nodes corresponding to the machining features, screen out nodes that meet the machining requirements of the mechanical parts to be machined from the process knowledge graph, and obtain a complete process plan.

[0035] A third aspect of the present invention provides an electronic device, comprising:

[0036] at least one processor, and a memory communicatively coupled to the at least one processor;

[0037] The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the process identification method according to any embodiment of the first aspect of the present invention.

[0038] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the process identification method described in any embodiment of the first aspect of the present invention.

[0039] The beneficial effects of the present invention are:

[0040] Compared with traditional technologies, the present invention combines process knowledge graphs to form a process identification method based on model features, which significantly improves the intelligent level of process selection. Traditional methods often rely on manual experience and are prone to subjective judgment and errors, while this system can quickly and accurately identify feasible process solutions, thereby reducing the error rate. This method can not only process complex process information, but also effectively integrate multi-source heterogeneous data to meet the needs of modern large-scale intelligent industrial production.

[0041] Compared with existing process identification solutions, the present invention shows advantages in many aspects. First, traditional solutions usually rely on fixed rules and linear processes, and cannot flexibly respond to complex and changing processing requirements. The present invention uses knowledge graphs for process identification, which can flexibly adapt to diverse process conditions and improve the flexibility and scalability of the system. In addition, the process field involved in the present invention is more basic, and can deeply explore the correlation between geometric features and processing technology, and has strong interpretability. This correlation not only expands the scope of application, but also improves the accuracy of process identification and ensures better processing results. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is an overall flow chart of a process identification method based on model features provided by an embodiment of the first aspect of the present invention;

[0043] Figure 2 yes Figure 1 A schematic diagram of each node type in the process knowledge graph constructed by the process identification method shown;

[0044] Figure 3 It is a schematic diagram of a part of the process knowledge graph formed by using Neo4j Browser in a specific embodiment of the present invention;

[0045] Figure 4 (a) and (b) are the topological relationships between the hole feature and its component surfaces, respectively;

[0046] Figure 5 is a schematic diagram of predefining processing feature attributes according to an embodiment of the present invention;

[0047] Figure 6 is a schematic diagram of determining the topological relationship between processing features according to an embodiment of the present invention;

[0048] Figure 7 It is a schematic diagram of a process selection route screened by a reverse search method according to an embodiment of the present invention;

[0049] Figure 8 is a schematic diagram of a process identification result finally obtained in an embodiment of the present invention;

[0050] Fig. 9 It is a schematic diagram of an interactive software interface and operating effect designed based on the process identification method provided by an embodiment of the present invention;

[0051] Fig.10 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the third aspect of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0053] On the contrary, the present application covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present application as defined by the claims. Further, in order to make the public have a better understanding of the present application, some specific details are described in detail in the detailed description of the present application below. Those skilled in the art can fully understand the present application without the description of these details.

[0054] See also Figure 1 The first aspect of the present invention provides a process identification method based on model features, comprising the following steps:

[0055] Step S1, obtaining process-related data, preprocessing it, and obtaining a standardized process description text;

[0056] Step S2, constructing a process knowledge graph by extracting the relationship node triples in the process description text, the node types constituting the process knowledge graph include: feature nodes, process nodes, equipment nodes, tool nodes and fixture nodes; the relationship between two adjacent nodes includes: available processes, previous processes, available equipment, initial processing, equipped tools and equipped fixtures;

[0057] Step S3, obtaining a geometric model of the mechanical part to be processed, extracting its geometric feature attributes and determining feature topological relationships, and obtaining mechanical processing features of the mechanical part model to be processed;

[0058] Step S4: Using the reverse search method, starting from the feature nodes corresponding to the machining features, select nodes that meet the machining requirements of the mechanical parts to be machined from the constructed process knowledge graph to obtain a complete process plan.

[0059] In some embodiments, the process-related data acquired in step S1 include: part processing characteristics, material information, process requirements, equipment parameters and other data. Part processing characteristics include specific requirements such as the geometric shape, dimensional tolerance, and surface roughness of the mechanical parts to be processed, which directly affect the selection of processing methods; material information involves the type of material used for the parts, physical and chemical properties such as hardness, heat resistance, and ductility. Different materials will put forward different requirements for the setting of processing technology; process requirements include operating conditions such as process routes, processing steps, and tools used during the processing process to ensure that parts are efficiently manufactured under the premise of meeting design requirements; equipment parameters refer to information related to processing equipment, such as equipment type, specifications, and processing accuracy. Accurate acquisition and analysis of these data are the basis for building a process knowledge map.

[0060] In some embodiments, in step S1, the acquired process-related data is divided into two categories: structured process data and unstructured process data, and different preprocessing methods are used for the two categories of process data.

[0061] The preprocessing of structured process data includes: (1) data cleaning, removing redundancy, correcting erroneous information, and filling missing values ​​to ensure data integrity and accuracy; (2) standardizing data from different sources, unifying the data formats from different sources, and converting categorical data into analyzable numerical forms; (3) normalizing numerical data to eliminate the impact of different dimensions. The preprocessed structured process data is directly stored in the form of triples to facilitate the subsequent construction of the process knowledge graph.

[0062] Preprocessing of unstructured process data includes:

[0063] (1) Word segmentation: Unstructured process sentences or process descriptions are broken down into words or phrases. Since the process field has a large number of specific terms, the word segmentation results need to be adjusted in combination with the process field dictionary. For example, "high temperature treatment" is treated as a whole word instead of "high temperature" and "treatment" as two independent words. The process field dictionary is a collection of well-known words and terms in the process field.

[0064] (2) Part-of-speech tagging: Perform part-of-speech tagging on the text after word segmentation, marking each word as a noun, verb, or adjective. Through part-of-speech tagging, the entity type corresponding to the node can be identified more accurately. For example, nouns are often names of materials or equipment, while verbs may be process steps.

[0065] (3) Regularization: In order to improve the consistency of entity recognition, the terms in the process field must be regularized. For example, "aluminum alloy" can be unified into "aluminum alloy" to eliminate the differences between different vocabulary expressions (such as "aluminum" and "aluminum material"). In addition, it is necessary to clean up the noise data in the text, such as symbols and useless stop words.

[0066] (4) Stop word filtering: Many words in the process description are irrelevant to entity recognition (such as "is", "of", "and", etc.). They can be removed by stop word filtering. The filtered text will be more concise and retain key information.

[0067] In some embodiments, in step S2, when extracting the relationship node triples in the process description text, the stored relationship node triples are directly extracted for the pre-processed structured process data. For the pre-processed unstructured process data, the key information and relationship nodes in the process description text are extracted by manual intervention and named entity recognition methods to form "entity-relationship-entity" triples, for example: "In the milling process, high-speed steel tools are used to process aluminum alloy parts, and the surface roughness Ra is required not to exceed 1.6." In such a process text, the entities identified include tools, materials, processes, etc.; the relationships include process methods, processing requirements, etc.; the final triples are ("aluminum alloy parts", "available processes", "milling"), ("milling", "processing requirements", "surface roughness Ra does not exceed 1.6"). Finally, the relationship node triples formed by the processed data are sorted and stored in the graph database, so as to construct a process knowledge graph.

[0068] Further, see Figure 2 The process knowledge graph constructed in this embodiment mainly includes the following node types:

[0069] (1) Feature nodes: represent the features of the mechanical parts to be processed, such as holes, planes, etc. In the process knowledge graph, feature nodes are key elements that define the geometry and processing requirements of the parts and ensure the accuracy of process selection;

[0070] (2) Process node: represents the optional process corresponding to the feature node. Each process node maintains two key attributes: machining accuracy IT and surface roughness Ra. These attributes help evaluate the applicability of the process and provide a basis for process selection.

[0071] (3) Equipment node: represents the available processing equipment that matches the process. Each equipment node maintains the relevant attributes of the respective equipment, such as equipment type and applicable process, to ensure the matching of process and equipment.

[0072] (4) Tool node: represents the available machining tools that match the equipment. Each tool node stores information such as tool type, material, and diameter to support process implementation.

[0073] (5) Fixture node: represents the available fixtures that match the equipment. Each fixture node stores the characteristics and applicable scope of the fixture to ensure the stability and safety of the workpiece during the processing.

[0074] The relationships between nodes in the process knowledge graph constructed in this embodiment include:

[0075] (1) Available process: It means that a certain feature of the machine part to be processed can be processed by a certain process, ensuring the consistency between the process selection and the feature requirements;

[0076] (2) Precursor process: indicates the order of processes and clarifies the logical flow of processing steps;

[0077] (3) Available equipment: This means that the process can be processed using a certain type of equipment to ensure the compatibility of the equipment and the process;

[0078] (4) Initial processing: indicates that the process is the first step in a certain feature processing route, which helps determine the processing sequence;

[0079] (5) Equipped tool: indicates that the device can be equipped with a certain tool;

[0080] (6) Equipped with fixture: Indicates that the device can be equipped with a certain fixture.

[0081] In a specific embodiment of the present invention, the relationship node triples in the extracted standardized process description text are sorted (see Table 1-1 and Table 1-2 for some of the sorted results) and stored in the Neo4j database. With the help of Neo4j Browser, the node distribution and its relationship can be visualized, so that users can intuitively understand the relationship between the process route and the nodes. The process knowledge graph finally formed is shown in Figure 3 .

[0082] Table 1-1 Relationship node triples

[0083]

[0084]

[0085] Table 1-2 Relationship node triples

[0086]

[0087] It can be understood that the process knowledge graph structure constructed in this embodiment has significant advantages. First, by carefully defining feature nodes, process nodes, and equipment nodes, the accuracy of the nodes is improved, so that they can accurately reflect the specific characteristics of the parts to be processed and related process information. Secondly, it comprehensively covers various relationships between nodes, deeply reveals the dependencies and sequences between processes, and provides a more scientific basis for process selection. Users can intuitively understand the relationship between process routes and nodes, provide users with intelligent decision-making support, and meet the needs of modern manufacturing for efficient, flexible, and precise process management.

[0088] In some embodiments, the core of step S3 is how to accurately extract the corresponding machining features from the geometric model of the mechanical part to be processed. Although the application protocol STEP AP214 provides geometric information of the mechanical part model, such as the attributes and topological relationships of geometric features such as faces and lines, geometric features are not the same as machining features, which include planes, holes, fillets, etc. Figure 4 For example, the hole feature shown in FIG. 1 includes plane F1 (the end plane of the part to be drilled), plane F3 (the end plane of the hole), and cylindrical surface F2 (the diameter of the cylindrical surface is the inner diameter of the hole). Figure 4 In (a), in terms of topological relationship, the cylindrical surface F2 is nested between the planes F1 and F3, and the connectivity between the surfaces is as follows: Figure 4 As shown in (b). In terms of positional relationship, plane F3 is located on the inner side of plane F1. If plane F3 is located on the outer side of plane F1, the geometric feature of the mechanical part to be processed is a boss. The attributes of the machining feature usually include size, machining accuracy and surface roughness. Although the size is closely related to the geometric feature, the machining accuracy and surface roughness are independent of the attributes of the geometric feature. Therefore, they need to be pre-defined in the modeling stage of the mechanical part to be processed. The method adopted in this embodiment is to add entity attributes to each surface in the modeling stage, so as to define the machining accuracy and surface roughness. Specifically, step S3 includes the following steps:

[0089] Step S31, loading the geometric model: importing the geometric model of the mechanical part to be processed through a supported file format, ensuring that the required geometric data is converted into a processable format;

[0090] Step S32, extracting geometric features: extracting all geometric features of the mechanical parts to be processed from the loaded geometric model, including faces, edges and vertices;

[0091] Step S33, screening processing features: according to the processing requirements, the extracted geometric features are classified to identify the geometric features that meet the machining requirements, for example, which features are holes, planes or other processing features, to ensure that the key features that need to be processed are accurately found among many geometric shapes;

[0092] Step S34, extracting machining feature attributes: further extracting key attributes of the selected machining features, including size, precision, and surface roughness, etc.; the size can be directly obtained from the geometric features, while the machining precision and surface roughness need to be predefined in the modeling stage. By adding entity attributes to each surface in the geometric model, the machining precision and surface roughness are defined, see Figure 5 ;

[0093] Step S35, determination of feature topological relationships and arrangement of processing sequence: after completing the extraction of the types and attributes of each processing feature in the mechanical part model, it is also necessary to determine the topological relationships between the processing features in order to arrange the processing sequence of each processing feature; the embodiment of the present invention uses normal vectors and boundary points to detect adjacent processing features for all geometric features extracted in step S32, constructs topological relationships, uses the unique identifiers of processing features as nodes, and the connection relationships between adjacent processing features as edges, and uses the spatial positions of geometric features to analyze the dependencies between processing features, and identifies the processing sequence between adjacent processing features, for example, determining which features are prerequisites to support the processing of other features. More specifically, this embodiment performs a topological analysis on the closed curves of processing features within a set common plane. If the closed curve of the second processing feature is completely within the closed curve of the first processing feature, then the processing sequence of the first processing feature is superior to that of the second processing feature; Figure 6 Taking the feature set shown as an example, there are plane features and hole features, which coexist on the geometric surface F1. On this surface, the closed curve L1 of the plane feature surrounds the closed curve L2 of the hole feature. Therefore, the hole feature can be set as a child of the plane feature, that is, during processing, the plane will be processed before the hole; for other cases, if the closed curves of two processing features are coplanar but there is only partial overlap between the two, the processing features corresponding to the overlapping area or the processing features with a larger depth in the three-dimensional space will be processed first, which can ensure the rationality of the processing sequence and avoid subsequent processing interference caused by improper processing sequence; if there is no overlap, it means that the first processing feature and the second processing feature are independent in geometric features, and there is no spatial interaction or conflict between them. The priority does not need to be judged by the overlapping relationship of the closed curves, and can be reasonably arranged according to the overall process flow.

[0094] Step S36, output machining feature information: organize the extracted machining features and their related attributes and topological relationships into structured information, that is, obtain the machining features of the mechanical part model to be processed, provide basic data for subsequent process analysis and decision-making, and ensure rapid response and recommendation of appropriate process solutions during the manufacturing process.

[0095] In some embodiments, the process selection algorithm in step S4 adopts the idea of ​​reverse search, which helps to improve the efficiency and accuracy of process selection, especially in complex process planning scenarios. The core reason is that the machining process often starts from the final machining requirements or product features, and has a clear final goal, that is, the dimensional accuracy, surface roughness, etc. that the machining features of the mechanical parts to be processed need to achieve, and there may be multiple machining paths and process methods to achieve this goal. Therefore, the reverse search starts from the feature node and derives forward, gradually finding the corresponding process nodes, and screening each process method that meets the machining requirements. This method can effectively avoid the "path blind spot" problem in the forward search, avoid wasting a lot of computing resources, and reduce the exploration of unnecessary process steps. At the same time, through the path backtracking method, it can also help operators intuitively understand the rationality of each process and provide a basis for subsequent process optimization.

[0096] To better illustrate the central idea of ​​the algorithm, see Figure 7 First, enter the process knowledge graph from the target feature node (such as "hole"), and find the corresponding process nodes in turn through the "available process" relationship, such as "hand reaming", "reaming", "expanding", and "drilling". In this process, determine whether each process node meets the process method requirements of the processing. If it does not meet the requirements, directly end the search for the current path. If it meets the requirements, perform the next search based on the "previous process" relationship. When the found process node has an "initial processing" relationship, the process method corresponding to the obtained node is determined to be the last process. Through the target feature node (according to the order of processing timing, it is the end node on the current path) and the initial node (the last process), the path between the two can be effectively found, thus forming a complete processing flow, such as Figure 7 The dark and thick arrows form a complete processing path. All process plans that meet the processing requirements are finally formatted and output as process identification results. Figure 8 .

[0097] See also Fig. 9 , is an interactive software interface designed based on the process identification method provided by the embodiment of the present invention and a schematic diagram of the operation effect, wherein the mechanical part to be processed is a box, which realizes the intelligent process scheme screening and output of the part features. First, the geometric model of the part to be processed is loaded, and the geometric feature information is extracted. These geometric features are used as input and enter the process knowledge graph to match the feature nodes. Taking the "hole" feature as an example, the user selects the feature of the model of the part to be processed, and through the idea of ​​the reverse search algorithm, starting from the "hole" feature node, sequentially searches for the process method that matches the feature, and screens out all the process schemes that meet the processing conditions. Finally, a visual output is performed to display the processing conditions including each step of the process and its response, providing the operator with an intuitive reference for process selection.

[0098] A second aspect of the present invention provides a process identification device based on model features, comprising:

[0099] A first module stores a standardized process description text, wherein the standardized process description text is data obtained by preprocessing the acquired process-related data;

[0100] The second module is used to construct a process knowledge graph by extracting the relationship node triples in the process description text. The node types constituting the process knowledge graph include: feature nodes, process nodes, equipment nodes, tool nodes and fixture nodes; the relationship between two adjacent nodes includes: "available process", "previous process", "available equipment", "initial processing", "equipped tool" and "equipped fixture";

[0101] The third module is used to obtain the geometric model of the mechanical part to be processed, extract its geometric feature attributes and determine the feature topological relationship, and obtain the mechanical processing features of the mechanical part model to be processed;

[0102] The fourth module is used to adopt a reverse search method starting from the feature nodes corresponding to the machining features, screen out nodes that meet the machining requirements of the mechanical parts to be machined from the process knowledge graph, and obtain a complete process plan.

[0103] It should be noted that the above explanations of the embodiment of the process identification method are also applicable to the process identification device of this embodiment, and will not be repeated here.

[0104] In order to implement the above embodiment, the embodiment of the present invention further proposes a computer-readable storage medium, on which a computer program is stored. The program is executed by a processor to execute the process identification method of the above embodiment.

[0105] Reference below Fig.10 , which shows a schematic diagram of the structure of an electronic device suitable for implementing an embodiment of the present invention. It should be noted that the electronic device in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, servers, etc. Fig.10 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0106] like Fig.10As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 102 or a program loaded from a storage device 108 to a random access memory (RAM) 103. In the RAM 103, various programs and data required for the operation of the electronic device are also stored. The processing device 101, the ROM 102, and the RAM 103 are connected to each other via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0107] Typically, the following devices may be connected to the I / O interface 105: an input device 106 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, etc.; an output device 107 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 108 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 109. The communication device 109 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Fig.10 An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0108] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the present embodiment includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 109, or installed from the storage device 108, or installed from the ROM 102. When the computer program is executed by the processing device 101, the above-mentioned functions defined in the method of the embodiment of the present invention are executed.

[0109] It should be noted that the computer-readable medium of the present invention can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0110] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0111] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the process identification method.

[0112] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, python, and conventional procedural programming languages ​​such as "C-" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0113] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0114] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0115] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0116] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0117] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0118] A person of ordinary skill in the art may understand that all or part of the steps carried out in the above-mentioned embodiment method may be completed by instructing the relevant hardware through a program, and the developed program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0119] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0120] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A process identification method based on model features, characterized in that: include: Obtain process-related data, pre-process it, and obtain standardized process description text; A process knowledge graph is constructed by extracting the relationship node triples in the process description text. The node types constituting the process knowledge graph include: feature nodes, process nodes, equipment nodes, tool nodes and fixture nodes; the relationship between two adjacent nodes includes: "available process", "previous process", "available equipment", "initial processing", "equipped tool" and "equipped fixture"; Acquire the geometric model of the mechanical part to be processed, extract its geometric feature attributes and determine the feature topological relationship, and obtain the mechanical processing features of the mechanical part model to be processed; A reverse search method is adopted to start from the feature nodes corresponding to the machining features, and the nodes that meet the machining requirements of the mechanical parts to be machined are screened out from the process knowledge graph to obtain a complete process plan.

2. The process identification method according to claim 1, characterized in that: The process-related data include part processing characteristics, material information, process requirements and equipment parameters; the part processing characteristics include the specific requirements for the geometric shape, dimensional tolerance and surface roughness of the mechanical parts to be processed; the material information involves the physical and chemical properties of the materials used for the parts; the process requirements include the operating conditions during the processing, including the process route, processing steps and the tools used; the equipment parameters include the type, specifications and processing accuracy of the equipment.

3. The process identification method according to claim 1, characterized in that: The process-related data is divided into two categories: structured process data and unstructured process data. Preprocessing of structured process data includes: data cleaning, removing redundancy, correcting erroneous information, and filling missing values ​​to ensure data integrity and accuracy; standardizing data from different sources, unifying data formats from different sources, and converting categorical data into analyzable numerical forms; normalizing numerical data to eliminate the impact of different dimensions; The preprocessing of unstructured process data includes: word segmentation, part-of-speech tagging, regularization and stop word filtering.

4. The process identification method according to claim 1, characterized in that: In the process knowledge graph, the feature nodes represent the features of the mechanical parts to be processed; The process node represents the optional process corresponding to the feature node, and the attributes maintained inside each process node include processing accuracy and surface roughness; the equipment node represents the available processing equipment matching the process, and each equipment node maintains the relevant attributes of the respective equipment; the tool node represents the available processing tools matching the equipment, and each tool node stores the relevant attributes of the respective tools; the fixture node represents the available fixture matching the equipment, and each fixture node stores the characteristics and applicable scope of the respective fixture; The "available process" means that a certain feature of the mechanical part to be processed can be processed by a certain process; the "previous process" means the order of processes; the "available equipment" means that a certain process can be processed by a certain equipment; the "initial processing" means that a certain process is the first process in a certain feature processing route; the "equipped tool" means that a certain equipment can be equipped with a certain tool; the "equipped fixture" means that a certain equipment can be equipped with a certain fixture.

5. The process identification method according to claim 1, characterized in that: The machining features of the mechanical part model to be machined are obtained according to the following steps: Step S31, importing the geometric model of the mechanical part to be processed through a supported file format, ensuring that the required geometric data is converted into a processable format; Step S32, extracting all geometric features of the mechanical part to be processed from the geometric model, adding entity attributes to each surface in the geometric model, and predefining the processing accuracy and surface roughness; Step S33: classify the extracted geometric features according to the machining requirements, and identify the geometric features that meet the machining requirements as machining features; Step S34, extracting key attributes of the processing features, including size, accuracy and surface roughness; Step S35: Detect adjacent features of all extracted geometric features using normal vectors and boundary points, construct topological relationships, use unique identifiers of processing features as nodes, and connections between adjacent processing features as edges, analyze dependencies between processing features using spatial positions of geometric features, and identify processing sequences between adjacent processing features; Step S36: Arrange the extracted machining features and their related attributes and topological relationships into structured information to obtain the machining features of the mechanical part model to be machined.

6. The process identification method according to claim 5, characterized in that: In step S35, the specific steps of identifying the processing order between adjacent processing features include: Assuming that the first processing feature and the second processing feature are in a set common plane, a topological analysis is performed on the closed curves of the first processing feature and the second processing feature: if the closed curve of the second processing feature is completely within the closed curve of the first processing feature, the processing order of the first processing feature is superior to that of the second processing feature; if the closed curves of the first processing feature and the second processing feature only partially overlap, the processing features corresponding to the overlapping area or the processing features with a larger depth in three-dimensional space are preferentially processed; if the closed curves of the first processing feature and the second processing feature do not overlap, it indicates that the first processing feature and the second processing feature are independent in terms of geometric features, and the processing order of the two is arranged according to the overall process flow.

7. The process identification method according to claim 1, characterized in that: The method of using reverse search starts from the feature nodes corresponding to the machining features, selects nodes that meet the machining requirements of the mechanical parts to be machined from the process knowledge graph, and obtains a complete process plan, which specifically includes: Enter the process knowledge graph from the target feature node, and find the corresponding process nodes in turn through the "available process" relationship. In this process, determine whether each process node meets the process method required for processing. If it does not meet the requirements, directly end the search for the current path. If it meets the requirements, perform the next search based on the "previous process" relationship. When the found process node has an "initial processing" relationship, determine that the process method corresponding to the obtained node is the last process; find the path between the target feature node and the initial node corresponding to the last process, thereby completing the process plan.

8. A device based on the process identification method according to any one of claims 1 to 7, characterized in that: include: A first module stores a standardized process description text, wherein the standardized process description text is data obtained by preprocessing the acquired process-related data; The second module is used to construct a process knowledge graph by extracting the relationship node triples in the process description text. The node types constituting the process knowledge graph include: feature nodes, process nodes, equipment nodes, tool nodes and fixture nodes; the relationship between two adjacent nodes includes: "available process", "previous process", "available equipment", "initial processing", "equipped tool" and "equipped fixture"; The third module is used to obtain the geometric model of the mechanical part to be processed, extract its geometric feature attributes and determine the feature topological relationship, and obtain the mechanical processing features of the mechanical part model to be processed; The fourth module is used to adopt a reverse search method starting from the feature nodes corresponding to the machining features, screen out nodes that meet the machining requirements of the mechanical parts to be machined from the process knowledge graph, and obtain a complete process plan.

9. An electronic device, characterized in that: include: at least one processor, and a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are configured to execute the process identification method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the process identification method according to any one of claims 1 to 7.

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