Method for constructing resource model structured database of complex equipment mass production line

Abstract

The application belongs to the technical field of process knowledge management, and particularly relates to a resource model structured database construction method for a complex equipment large-scale production line. The specific process is as follows: product information modeling: modeling product information into three levels of product classification, product series and product instance; complex equipment manufacturing resource information modeling: for each manufacturing process, a process resource information library is established to store four types of knowledge of process data, tooling knowledge, auxiliary tooling and process equipment in the process of process processing; complex equipment process ontology information modeling: process ontology information includes process composition information and process requirement information, and when modeling, the establishment of the composition relationship between process composition information, the hierarchical relationship between process requirement information, and the association relationship between process composition information and process requirement information are also included; process resource database management module building: the process resource database management module establishes process resource model management and process resource object management.

Description

Technical Field

[0001] This invention belongs to the field of process knowledge management technology, specifically relating to a method for constructing a structured database of resource models for large-scale production lines of complex equipment. Background Technology

[0002] The traditional development model, which focuses on equipment quality and performance, has resulted in problems such as high equipment costs, difficulty in increasing production scale, low production efficiency, and insufficient capacity. It is difficult to meet the complex equipment supply needs of future high-intensity and high-severity continuous work. In order to meet the growing demand for production efficiency and cost control, a low-cost and high-efficiency process technology with general industrial scale characteristics is needed in manufacturing.

[0003] Data is increasingly widely used in enterprises. By processing this data, companies can conduct in-depth analysis to uncover the knowledge hidden behind the data, which greatly guides decision-making and improves productivity. Therefore, mastering data processing technology is essential to enhancing knowledge innovation capabilities. The proper storage and classification of necessary information is a prerequisite for intelligent information management.

[0004] In a management system, information and data are the primary sources of knowledge for organizations and individuals. Integrating information and data to build a shareable and searchable knowledge repository, and then using relevant tools and technologies to effectively manage and utilize knowledge, forms a management system. The construction of this system provides enterprises with a comprehensive knowledge management platform. It allows for the analysis and summarization of enterprise data using the analytical tools of the knowledge management system, enabling deeper mining of data generated in the enterprise's production activities. This data is then categorized and summarized to provide the enterprise with more accurate, complete, and efficient data.

[0005] Currently, in the field of process knowledge management, existing general technologies are based on specific process resources to establish their knowledge systems and manage their knowledge. The data to be managed is categorized, and after categorization, it is organized according to the goals and principles of digital resource construction. Through the analysis of knowledge in various process resource domains, the knowledge attributes of various process resources are determined, and corresponding domain ontology models are established using ontology technology. This allows process resources to be managed visually according to domain knowledge, enhancing user retrieval experience and efficiency to a certain extent.

[0006] 1) Insufficient depth of knowledge model: Existing technologies are too simplistic in classifying product structure information, making it difficult to adapt to increasingly complex product structure changes, especially lacking detailed process requirements and parameter configurations at the product instance level.

[0007] 2) Limited resource optimization and allocation capabilities: Existing technologies lack the ability to conduct in-depth analysis and optimization of process resources, thus failing to maximize resource utilization.

[0008] 3) The model is not targeted enough: Although the existing technology has established a knowledge system and ontological model, it is not deep enough in supporting process design activities and cannot quickly retrieve and match the knowledge required to generate manufacturing solutions. Summary of the Invention

[0009] To address the challenge of constructing a process information database for complex equipment products, which are characterized by their intricate structures, diverse components, and numerous and complex process solutions, this invention proposes a method for constructing a structured database based on a resource model of a low-cost, large-scale production line for complex equipment. This method enables the efficient construction of the process information database.

[0010] The technical solution for implementing the present invention is as follows:

[0011] A method for constructing a structured database for resource models of complex equipment mass production lines, the specific process of which is as follows:

[0012] Step 1, Product Information Modeling:

[0013] Product information is modeled into three levels: product category, product series, and product instance. Product series belong to product category, and product instance belongs to product series.

[0014] Step 2: Modeling resource information for complex equipment manufacturing:

[0015] For each manufacturing process, a process resource information database has been established, which mainly stores four categories of knowledge: process data, tooling knowledge, auxiliary tooling, and process equipment.

[0016] Step 3: Modeling the process information of complex equipment:

[0017] The process ontology information includes process composition information and process requirement information. When modeling, it also includes establishing compositional relationships between process composition information, hierarchical relationships between process requirement information, and correlation relationships between process composition information and process requirement information.

[0018] Step 4: Setting up the process resource database management module:

[0019] The process resource database management module establishes process resource model management and process resource object management.

[0020] Furthermore, step one of the present invention includes product classification modeling, product series modeling, and product instance modeling, wherein:

[0021] Product classification modeling: Classifying complex equipment products according to similarity in selected dimensions to form a product classification method with clear structure and well-defined boundaries;

[0022] Product series modeling: Grouping products with similar product elements into a product series, where product elements include: function, structure, and process;

[0023] Product instance modeling: The product BOM (Bill of Materials), technical requirements, and product part technical characteristics are used as features of the product instance model. The product BOM contains all necessary raw materials and components, including their models, quantities, and sources; the technical requirements include performance standards and test conditions; and the product part technical characteristics provide detailed dimensions, shapes, and material properties for each component.

[0024] Furthermore, step two of the present invention includes: process data modeling, auxiliary tooling modeling, process knowledge modeling, and equipment resource modeling;

[0025] Process data modeling: Classify process data according to its type and purpose, and define a unified data model for each type of data. In the modeling process, define multiple key attributes, data sources and validity for process data.

[0026] Auxiliary tooling modeling: First, identify and classify the various tools and equipment used in the manufacturing process, and determine the function and application scenario of each type of tooling; second, establish a detailed classification system to group tooling according to its function, purpose and design standards; third, define a series of key attributes for auxiliary tooling to comprehensively describe its characteristics and application conditions.

[0027] Process knowledge modeling: Process knowledge is categorized into three types: process decision knowledge, process example knowledge, and auxiliary process resources. In the process of process knowledge modeling, the expert experience, factual knowledge, and procedural knowledge accumulated in the process design are systematically organized and digitally expressed.

[0028] Equipment resource modeling: The model not only includes the basic information of the equipment, but also covers the equipment's operating status, capability parameters, and remarks.

[0029] Furthermore, step three of the present invention includes: process composition information modeling and process requirement information modeling;

[0030] Process composition information modeling includes step information modeling, process attribute information modeling, and process route information modeling;

[0031] Process requirement information modeling includes process attribute feature representation, process parameter feature representation, and process ontology information representation.

[0032] Furthermore, the process resource database management module of the present invention includes: a database storage tool, a complex equipment product resource management module, a complex equipment manufacturing resource management module, and a complex equipment process information management module.

[0033] Furthermore, the complex equipment product resource management module of the present invention is used for product classification management, product series management, and product specification management;

[0034] Product category management uses a tree structure to classify the products of manufacturing enterprises based on macroscopic characteristics, and provides basic functions such as adding, deleting, modifying, and querying category tree nodes.

[0035] Product series management involves: establishing a product attribute library and parameter library that describe product characteristics; configuring attributes and parameters for product series; assigning values ​​to product parameters included in the product series; and forming product specification information that contains complete product characteristic descriptions.

[0036] Product specification management uses ER diagrams to represent the data relationships between various concepts. Entity-relationship diagrams can be transformed into relational schemas using formulaic methods, thereby establishing tables in a relational database.

[0037] Furthermore, the complex equipment manufacturing resource management module of the present invention includes three levels: resource classification, resource series, and manufacturing resource instance, which are used for manufacturing resource classification management, manufacturing resource series management, and manufacturing resource specification management.

[0038] Manufacturing resource classification management uses a tree structure to classify the products of manufacturing enterprises based on macroscopic characteristics. The functional logic includes adding, deleting, modifying, and querying classification tree nodes.

[0039] The elements for establishing a manufacturing resource series correspond to the model, including series name, series category, and series description. A resource attribute library and parameter library describing product characteristics are established, and attributes and parameters are configured for the resource series.

[0040] The manufacturing resource specification management includes assigning values ​​to resource parameters to form resource specification information containing complete resource characteristic descriptions.

[0041] Furthermore, the complex equipment process information management module of the present invention realizes the function of establishing and integrating the application of models related to process composition information and process requirement information, including sub-modules for process step management, process management, process route management and process requirement management;

[0042] The process requirements management submodule divides process requirements into two categories: target requirements and operational requirements. By associating a process requirement with a certain type of process component information, the process requirement under that association represents the requirements at the corresponding level. During the configuration process, it is specified whether each process requirement is displayed in the final process design results.

[0043] The work step management submodule includes the creation and maintenance of work steps, as well as the configuration management functions for work steps and work step requirements;

[0044] The process management submodule adds a process classification function on the basis of process content, so as to classify and manage processes according to requirements;

[0045] The functions of the process route management submodule are basically the same as those of the process management submodule, except that it lacks classification and manufacturing resource allocation.

[0046] Furthermore, the present invention also includes database updating, the specific process of which is as follows:

[0047] First, various parameters of the production line equipment are monitored in real time and sent to the database system in the form of a stream;

[0048] Secondly, the data stream is processed to transform the corresponding data changes into feature changes in the manufacturing resource model and write them into the database. By listening to data change events, specific events or data patterns can be identified and responded to, and corresponding update operations can be taken immediately when the event occurs, thereby achieving real-time updates.

[0049] Secondly, after receiving an update request, the database management system will detect the parent category of the update characteristics and use a concurrency control mechanism to handle multiple simultaneous update operations.

[0050] Finally, after the data was successfully written to the database, the data backup and recovery strategy was further updated.

[0051] Secondly, this application provides a process design method based on a structured database of resource models for large-scale production lines of low-cost complex equipment. The specific process is as follows:

[0052] (1) For the production and manufacturing of a specific product, obtain the bill of materials and part characteristics of the product instance based on the product classification information, and list the manufacturing plan requirements;

[0053] (2) Based on the manufacturing scheme requirements, search in the process body information database to find similar process instances. If an identical instance is found, it can be used directly. If there is a similar instance, it can be used as a reference for manufacturing scheme design.

[0054] (3) In the production process analysis and manufacturing scheme determination stage, based on the consideration of part information and process constraints, the process data and process knowledge in the manufacturing resource information should be considered, and more auxiliary tooling and equipment resource information should be applied to select idle equipment and tooling resources.

[0055] (4) During the manufacturing scheme verification stage, it is necessary to consider process data and process knowledge in more detail to ensure the safe production of the manufacturing scheme, and to conduct inspection based on the technical requirements of product examples in the product information database after the manufacturing scheme is roughly determined.

[0056] (5) Finally, compile complete process documentation, including process flow diagrams, operating procedures and quality control plans, and feed new process knowledge back into the knowledge base to provide reference for future process design.

[0057] Beneficial effects

[0058] First, this invention significantly improves the efficiency of organizing and retrieving process information through systematic data classification and ontology construction. This structured approach not only makes information storage more orderly, but also strengthens the intrinsic connections between information through the application of ontological technology, making knowledge discovery and innovation easier.

[0059] Secondly, the intelligent management system constructed in this invention effectively solves the pain points in existing process resource management. In the intelligent production process, it breaks down the information barriers between the production site and the data management center, enabling real-time database updates. This integrates all relevant data into a unified platform and significantly improves the optimal allocation of resources.

[0060] Third, this invention provides strong support for manufacturing scheme design through the construction of an overall database framework and the classification and organization of data. The system can quickly perform preliminary planning of manufacturing processes based on product instance feature information, and match similar instances in the database for detailed manufacturing scheme design, thereby significantly improving the efficiency and quality of process design. Attached Figure Description

[0061] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 Build an overall framework for the database;

[0063] Figure 2 Flowchart of a method for constructing a structured database for resource models of low-cost, complex equipment mass production lines;

[0064] Figure 3 A framework diagram for product resource modeling;

[0065] Figure 4 Framework diagram for manufacturing resource modeling;

[0066] Figure 5 Model a framework diagram for the process equipment;

[0067] Figure 6 This is a diagram showing the relationships between information related to the process entity.

[0068] Figure 7 ER diagram for product information;

[0069] Figure 8 To create an ER diagram for manufacturing resources;

[0070] Figure 9 Process resource information ER diagram;

[0071] Figure 10 Flowchart of real-time database update;

[0072] Figure 11 Data resource requirements diagram generated for manufacturing solutions. Detailed Implementation

[0073] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0074] It should be noted that, in the absence of conflict, the following embodiments and features can be combined with each other; and, based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0075] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0076] This application addresses pain points in existing process resource management by constructing a low-cost, structured database of resource models for large-scale production lines of complex equipment. Through systematic data classification and organization, ontology construction, and database design, it achieves efficient management and optimized allocation of process resources. The overall content of the complex equipment process resource information database is as follows: Figure 1As shown. The main modeling content of the database includes complex equipment product information modeling, process resource information modeling, and process information modeling. The content includes all information about complex equipment from design to production, and the classification is clear and explicit.

[0077] Traditional process resource management methods suffer from problems such as information silos, data inconsistencies, and low resource utilization efficiency. Therefore, constructing a low-cost, high-efficiency structured database of resource models for large-scale production lines of complex equipment is of great significance for improving production management, reducing costs, and enhancing market competitiveness. In this application's embodiment, the knowledge association module links the process module with the product instance module, and the process template module with the product instance, thus connecting the entire database data for convenient management and strengthening the knowledge association of process information for complex equipment. In the content operation module, divided into a model layer and an object layer, data operations are standardized for database data management.

[0078] like Figure 1-2 As shown in the embodiment of this application, a method for constructing a structured database for a resource model of a low-cost, complex equipment mass production line is described. The specific process is as follows:

[0079] Step 1, Product Information Modeling: Model product information into three levels: product category, product series, and product instance. Product series belong to product category, and product instance belongs to product series.

[0080] Product information serves as input for process design. When conducting process design activities, process engineers first need to clarify the characteristic information of the target product and, based on this information, make preliminary plans for the manufacturing process. There is often a certain correlation between product characteristics and manufacturing processes. For example, if a cable product contains an "insulation layer," then the manufacturing process must include the "insulation" step. The complete expression of product information is primarily achieved through the coordinated use of conceptual models at three different levels: product classification, product series, and product examples.

[0081] In a multi-variety, small-batch manufacturing model, complex equipment product information is diverse and rapidly updated. Structured representation of this information enables rapid selection of target products and the formulation of reasonable, customized requirements. It also supports process engineers in quickly understanding the characteristics of complex equipment products and completing efficient process design tasks. Complex equipment products typically exhibit differences at different levels in function or structure. Based on this, complex equipment products can be categorized into three levels: product classification, product object, and product instance, and a corresponding representation model can be established for each level, such as... Figure 3 As shown.

[0082] (1) Product classification modeling

[0083] Complex equipment products (finished products, semi-finished products, parts, etc.) are classified according to similarity in selected dimensions, resulting in a product classification method with a clear structure and well-defined boundaries.

[0084] The design process for this step is described in detail below:

[0085] Common product classification criteria include function, usage scenario, material, structure, and processing status. For more complex products, multiple classification criteria can be combined, using a hierarchical tree structure to establish product classification for enterprises. Classification by function refers to products that meet the same functional requirements, suitable for manufacturing diverse product types. Classification by scenario refers to products applicable to the same scenarios, usually used in conjunction with functional classification for multi-dimensional product description. Classification by material is typically used for parts and blanks, indicating that these products share similar raw material types and material structures. Classification by structure refers to products with similar geometric features and dimensions, classifying local product characteristics, usually existing as a lower-level classification criterion in multi-level classification trees. Classification by processing status refers to products at similar stages in the manufacturing process, typically used to classify semi-finished products and parts.

[0086] Based on the above analysis, the product classification information of complex equipment can be represented by four characteristics:

[0087] Prod_Cat=(Prod_Cat_ID,Prod_Cat_Parent_ID,Prod_Cat_Name,Prod_Cat_Remarks)

[0088] in,

[0089] Prod_Cat:Product Category indicates the product category for complex equipment;

[0090] Prod_Cat_ID: Represents the product category number for complex equipment;

[0091] Prod_Cat_Parent_ID: Indicates the parent category number of the current complex equipment product category;

[0092] Prod_Cat_Name: This indicates the name of a complex equipment product category. To distinguish the product category from the product series and product instance names, it is recommended that the category name end with "Category".

[0093] Prod_Cat_Remarks: Represents notes for the classification of complex equipment products.

[0094] (2) Product series modeling

[0095] Products with similar product elements are grouped into a product series, where the product elements include: function, structure, and process.

[0096] The product series attributes and parameters of complex equipment are used as characteristics to describe the product series. Structured attribute and parameter information is used to describe the characteristics of the product series, and a product series model is established accordingly.

[0097] Among them, product series attributes include enumerated attributes and quantified parameters;

[0098] Enumerated attributes are used to describe the enumerable type of feature information in complex equipment products. Each enumerated attribute can be configured with multiple attribute enumeration values ​​at the same time to adapt to the feature description needs of different series of products.

[0099] Quantitative parameters are used to describe the non-enumerable types of characteristic information in complex equipment products. The value of the quantitative parameter can be recorded as a "single value", "interval value" or "deviation value" according to the product characteristic information.

[0100] The design process for this step is described in detail below:

[0101] The purpose of establishing product classification is to quickly locate complex equipment and help process engineers understand the macro-level information of the product. Therefore, an overly detailed product classification tree will lose its value and create unnecessary difficulties. To further structure and apply information describing the characteristics of complex equipment based on product classification information, a product series model can be established. A complex equipment product series is a collective term for a series of products developed by a manufacturing enterprise that have essentially the same function, similar structure, and highly consistent manufacturing processes. The division of product series adopts the similarity concept of group technology, grouping parts (products) with similar functions, structures, and processes into a part (product) family. For multi-variety, small-batch production models, designing processes according to part (product) families can increase product batch size, reduce product variety, and mitigate production efficiency losses caused by the manufacturing model.

[0102] To describe the similarities and differences among these complex equipment products, a structured description of product features is needed. Therefore, the first step is to establish a descriptive model for these product features. Complex equipment product features are diverse, including geometric features, surface features, dimensional features, environmental adaptability features, structural composition features, and material performance features. To differentiate these features in detail and establish a representation method that accurately describes this information, the established method must be supported by sufficient professional background from the user. Furthermore, most manufacturing enterprises only focus on a small portion of the product feature information. Therefore, the attribute parameters of the complex equipment product series can be used as features to describe the product series.

[0103] Enumerated attributes are used to describe enumerable types of feature information in complex equipment products. This information typically describes the same or similar features of products within the same product series during the series division process, i.e., the similarity within the product series. Each enumerated attribute can be configured with multiple attribute enumeration values ​​simultaneously to accommodate the feature description needs of different product series.

[0104] Quantitative parameters are used to describe the non-enumerable characteristic information of complex equipment products. This type of information is usually used to describe the specific parameter differences between various specifications of products within a certain series, that is, the dissimilarity within a complex equipment product series. The value of the quantitative parameter can be recorded as a "single value", "interval value" or "deviation value" according to the product characteristic information.

[0105] In the attribute parameters of complex equipment product series, enumerated attributes and quantified parameters can be distinguished by the value type of the attribute parameter. For example, if the value type of the attribute parameter is a single value, it is an enumerated attribute; otherwise, it is a quantified parameter.

[0106] The attribute quantification parameters of complex equipment products can be represented by six characteristics:

[0107] Prod_AP=(Prod_AP_ID,Prod_AP_Name,Prod_AP_Code,Prod_AP_Unit,Prod_AP_Type,Prod_AP_Remarks)

[0108] in,

[0109] Prod_AP: Represents product attribute parameters;

[0110] Prod_AP_ID: Indicates the product parameter number;

[0111] Prod_AP_Name: Represents the product parameter name;

[0112] Prod_AP_Code: Represents the code or abbreviation of product parameters;

[0113] Prod_AP_Unit: Indicates the unit of the product parameter;

[0114] Prod_AP_Type: Indicates the value type of the product parameter, including single value, range value, and deviation value;

[0115] Prod_AP_Remarks: Represents the notes for product parameters.

[0116] After establishing a representation model of product attributes, structured attribute and parameter information can be used to describe the characteristics of the product series, thereby establishing a product series model.

[0117] Information on complex equipment product series can be represented by six characteristics:

[0118] Prod_Series=(Prod_Series_ID,Prod_Series_Cat_ID,Prod_Series_Name,Prod_Series_Attr,Prod_Series_AP,Prod_Series_Remarks)

[0119] in,

[0120] Prod_Series: Indicates a product series;

[0121] Prod_Series_ID: Indicates the product series number;

[0122] Prod_Series_Cat_ID: Represents the product category number to which the product series belongs, establishing the association between product series and product category;

[0123] Prod_Series_Name: Represents the product series name;

[0124] Prod_Series_AP: Represents the set of attribute quantification parameters for a product series;

[0125] Prod_Series_Remarks: Represents the product series remarks, providing additional product information besides the series name, attributes, and parameters.

[0126] (3) Product instance modeling

[0127] The product BOM (Bill of Materials), technical requirements, and technical features of product parts are used as features of the product instance model.

[0128] The product BOM includes all necessary raw materials and components, including their type, quantity, and source; the technical requirements include performance standards and testing conditions; and the product part technical characteristics provide detailed dimensions, shape, and material properties for each component.

[0129] The design process for this step is described in detail below:

[0130] Through variant design, a main product can be used to create a group of products with similar attributes but different specific parameters. These similar products together form a product series, and product objects with different detailed features within this series are called product instances. These detailed features typically include the specification information of such product objects; therefore, product instances can also be called product specifications. Since product instances belong to a specific product series, they inherit the attribute information of that product series, including all enumerated attributes and their corresponding attribute value selections, as well as all quantified parameters. Furthermore, the description of product instance information has penetrated into the product specification data level, and the specific detailed features of each instance are determined.

[0131] Product instance information for complex equipment is a key component of the manufacturing and quality control process, as it defines the product's specific composition and characteristics in detail. By combining the three core elements of the product BOM (Bill of Materials), technical requirements, and technical characteristics of product parts, a comprehensive description can be formed, ensuring that every detail of the product meets design and functional standards.

[0132] As the basis for manufacturing, the product Bill of Materials (BOM) lists all necessary raw materials and components, including their type, quantity, and source. For example, a complex mechanical device may require hundreds of different parts, each with its specific specifications and source, all of which are detailed in the BOM.

[0133] Technical requirements further define the performance standards and testing conditions that a product must meet, such as temperature resistance, pressure resistance, or specific environmental adaptability. These requirements ensure that the product can exhibit the expected performance in practical applications and meet the user's needs.

[0134] Product component technical characteristics provide detailed dimensions, shape, and material properties for each component, including manufacturing tolerances and quality control standards. These characteristics are crucial for ensuring component compatibility and overall product performance.

[0135] By combining these three elements, the product instance information for complex equipment provides a clear blueprint for production teams, supply chain partners, and quality assurance departments, guiding them on how to manufacture, inspect, and deliver high-quality products that meet standards.

[0136] Information on complex equipment product instances can be represented by seven characteristics:

[0137] Prod_Inst=(Prod_Inst_ID,Prod_Inst_Series_ID,Prod_Inst_Name,Prod_Inst_BOM,Prod_Inst_Remarks,Prod_Inst_Req,Prod_Inst_Feature)

[0138] in,

[0139] Prod_Inst:Product Instance, representing a product instance;

[0140] Prod_Inst_ID: Represents the product instance number;

[0141] Prod_Inst_Series_ID: This indicates the product series number to which the product instance belongs, thereby establishing the association between the product instance and the product series, so as to inherit the product attribute information possessed by the product series;

[0142] Prod_Inst_Name: Represents the product instance name, which can typically be the product's specification information;

[0143] Prod_Inst_BOM: Represents the bill of materials collection for a product instance, listing all parts, raw materials, and subassemblies required to manufacture, assemble, or maintain the product;

[0144] Prod_Inst_Req: Represents the set of technical requirements for a product instance, listing the performance standards and test conditions that the product must meet, such as temperature resistance, pressure resistance, or specific environmental adaptability;

[0145] Prod_Inst_Feature: Represents the set of technical features of product parts for a product instance, providing detailed dimensions, shape, and material properties for each part, including manufacturing tolerances and quality control standards;

[0146] Prod_Inst_Remarks: Represents the remarks information for product instances.

[0147] The Bill of Materials (BOM) information for complex equipment products can be represented by seven characteristics:

[0148] Prod_PART=(Prod_PART_ID,Prod_PART_Name,Prod_PART_Num,Prod_PART_Source,Prod_PART_Material,Prod_PART_Remarks)

[0149] in,

[0150] Prod_PART: Indicates the parts required for the product;

[0151] Prod_PART_ID: Indicates the product part number;

[0152] Prod_PART_Name: Indicates the product part name;

[0153] Prod_PART_Num: Indicates the number of parts required for the product;

[0154] Prod_PART_Material: Indicates the material of the product parts;

[0155] Prod_PART_Source: Indicates the source of the product parts;

[0156] Prod_PART_Remarks: Represents the notes information for product parts.

[0157] The technical requirements of complex equipment products can be represented by four characteristics:

[0158] Prod_Req=(Prod_Req_ID,Prod_Req_Name,Prod_Req_Test,Prod_Req_Remarks)

[0159] in,

[0160] Prod_Req: Indicates the technical requirements that the product needs to meet;

[0161] Prod_Req_ID: Represents the technical requirement number;

[0162] Prod_Req_Name: Indicates the name of the technical requirement;

[0163] Prod_Req_Test: Indicates the number of conditions required by the technical requirements;

[0164] Prod_Req_Remarks: Represents notes on technical requirements.

[0165] The technical characteristics of product parts for complex equipment can be represented by four features:

[0166] Prod_Feature=(Prod_Feature_ID,Prod_Feature_Name,Prod_Feature_PART_ID,Prod_Feature_Size,Prod_Feature_Remarks)

[0167] in,

[0168] Prod_Req: Indicates the technical requirements that the product needs to meet;

[0169] Prod_Req_ID: Represents the technical requirement number;

[0170] Prod_Feature_PART_ID: Represents the part number corresponding to the technical feature of the product part, establishing the association between the technical feature of the part and the product part;

[0171] Prod_Req_Name: Indicates the name of the technical requirement;

[0172] Prod_Req_Test: Indicates the number of conditions required by the technical requirements;

[0173] Prod_Req_Remarks: Represents notes on technical requirements.

[0174] This step divides complex equipment product information into three levels: "Classification - Series - Instance". Classification categorizes product information from a macro perspective; series has enumerated attributes, attribute values ​​and unassigned quantitative parameters that describe the characteristics of product information; and instance supplements the specific characteristics of the product by instantiation.

[0175] Step 2: Complex Equipment Manufacturing Resource Information Modeling: For each manufacturing process, a process resource information database was established, which mainly stores four categories of knowledge: process data, tooling knowledge, auxiliary tooling, and process equipment.

[0176] This step, to complete the construction of the ontology of the resource domain for complex equipment manufacturing processes, requires conceptual abstraction. Classifying the included technological knowledge is the foundation for this ontology. The complex, disorganized, and disordered technological resources for complex equipment processing are categorized and constructed into a systematic whole. This initial classification of knowledge provides a basis for future knowledge management and is a crucial step in establishing the domain ontology. Classifying complex equipment manufacturing process resources involves synthesizing and categorizing the relevant technological knowledge based on the product lifecycle of the complex equipment. This allows the originally disorganized complex equipment manufacturing process resources to be categorized and organized, making them more structured and systematic, thus establishing a technological resource structure system. Classifying and organizing the included processes is the starting point for understanding, managing, and applying complex equipment manufacturing process resources.

[0177] Manufacturing process resources, in the field of manufacturing process design, are the collection of physical or conceptual objects required to complete a specific process design task. Manufacturing process resources include equipment, tooling, gauges, fixtures, and auxiliary tools used in process design; process design data such as materials, feed rates, depths of cut, cutting speeds, and design calculations; and other process resources, which are the most fundamental components of collaborative process design. To facilitate resource management, classifying complex equipment manufacturing resources is an important aspect of information management.

[0178] According to the national standard JB / T5992-92 "Classification and Codes of Manufacturing Processes," manufacturing processes can be divided into nine major categories: casting, pressure processing, welding, machining, special machining, heat treatment, coating, assembly and packaging, and others. Each major category can be further divided into medium and minor categories. For example, casting includes two medium categories: sand casting and special casting. The medium category of sand casting is further divided into minor categories such as wet casting, dry casting, surface dry casting, and self-hardening casting. Even within the minor categories, there are many different process types in practice. Currently, descriptions of these manufacturing processes only include technical performance data and lack data or descriptions of their green characteristics.

[0179] Therefore, this step establishes a process resource information database for each manufacturing process, mainly storing four categories of knowledge: process data, tooling knowledge, auxiliary tooling, and process equipment. Figure 4 As shown

[0180] (1) Process data modeling

[0181] Process data includes various data such as engineering materials, cutting parameters, time quotas, tolerances and fits, as well as various process terms, so that process designers can easily and quickly retrieve the data they need.

[0182] Process data is categorized according to its type and purpose, and a unified data model is defined for each category. During the modeling process, a series of key attributes are defined for the process data, including its unique identifier, name, type, specific value, unit, source, and validity conditions. Particular attention should be paid to the source and validity of the data during modeling, as this information is crucial to ensuring the accuracy and applicability of the process data.

[0183] The design process for this step is described in detail below:

[0184] The core objective of process data modeling is to provide process designers with a comprehensive and structured database that can quickly respond to their needs for key process parameters during the design process, thereby supporting decision-making. To this end, process data is first meticulously categorized and systematically managed to ensure that each type of data can be accurately identified and retrieved based on its characteristics and application scenarios, enabling process designers to quickly and accurately obtain the necessary process parameters and information.

[0185] Process data modeling employs a structured approach, classifying process data according to its type and purpose, and defining a unified data model for each category. During the modeling process, a series of key attributes are defined for the process data, including its unique identifier, name, type, specific value, unit, source, and validity conditions. These attributes collectively constitute a comprehensive description of the process data, enabling each piece of data to play its due role in design decisions. Particular attention should be paid to the source and validity of the data during modeling, as this information is crucial for ensuring the accuracy and applicability of the process data. By clearly defining the source of the data, it is possible to trace it back to the original reference standards or authoritative documents, thereby ensuring the data's authority. Simultaneously, by recording the validity conditions of the data, it is ensured that the data is reliable within the specific application scope.

[0186] Process data modeling has a wide range of applications. It not only supports process design by providing necessary parameters and reference data, but also plays a vital role in quality control, cost estimation, and employee training. Through this structured process data model, we can significantly improve the efficiency of process design, ensure product quality, and provide strong support for knowledge management and technology transfer in manufacturing enterprises.

[0187] The process data model can be represented by eight features:

[0188] Process_Data=(Process_Data_ID,Process_Data_Name,Process_Data_Type,Process_Data_Value,Process_Data_Unit,Process_Data_Source,Process_Data_Validity,Process_Data_Remarks)

[0189] in,

[0190] Process_Data_ID: A unique identifier for process data, used to uniquely identify a process data record in the database.

[0191] Process_Data_Name: The name of the process data, used to describe the main characteristics or purpose of the data.

[0192] Process_Data_Type: The type of process data, such as cutting parameters, time quotas, tolerances, etc.

[0193] Process_Data_Value: The specific numerical value or description of the process data, which can be numbers, text, or more complex data structures.

[0194] Process_Data_Unit: The unit of the data value, such as millimeters, hours, revolutions per minute, etc.

[0195] Process_Data_Source: The data source can be internal company standards, industry norms, or authoritative documents.

[0196] Process_Data_Validity: The validity or applicable conditions of the data, such as temperature range, material type, etc.

[0197] Process_Data_Remarks: Additional notes or comments on the data, such as usage precautions, data update records, etc.

[0198] (2) Auxiliary tooling modeling

[0199] The auxiliary tooling modeling process is as follows: First, the various tools and equipment used in the manufacturing process are identified and classified, and the function and application scenario of each type of tooling are determined. Second, a detailed classification system is established to group the tooling according to its function, purpose and design standards. Third, a series of key attributes are defined for the auxiliary tooling to comprehensively describe its characteristics and application conditions.

[0200] The design process for this step is described in detail below:

[0201] This includes various tooling resources used in the product manufacturing process, such as cutting tools, fixtures, measuring tools, molds, inspection tools, auxiliary tools, fitter's tools, and workstation equipment. The resource information involved in tooling design is extremely complex, with diverse content and forms. It mainly includes various tooling design standards, standard part information, general part information, 3D models, calculation methods, part design process information, and design technical documents. Tooling resources are an important component of manufacturing process resources. From a resource management perspective, this involves providing a complete and consistent representation of the resources of the analysis object at a higher level, and a comprehensive description of the various types of resources involved in each analysis object and the relationships between those resources.

[0202] Tooling modeling is a crucial step in ensuring that tools and equipment effectively support production activities during the manufacturing process. It aims to provide a systematic management framework for the various tools and equipment used in manufacturing. This framework ensures that process designers and operators can quickly identify suitable tooling to support specific machining tasks. Furthermore, this modeling process helps optimize tooling use and maintenance, reducing production delays and costs.

[0203] Tooling modeling begins with the identification and classification of various tools and equipment used in the manufacturing process. This includes cutting tools, fixtures, measuring tools, molds, etc., each with its specific function and application scenario. To achieve this goal, a detailed classification system was established, grouping tooling according to its function, purpose, and design standards to ensure that each piece of tooling can be accurately identified and retrieved.

[0204] In model construction, a series of key attributes were defined for the auxiliary tooling to comprehensively describe its characteristics and application conditions. These attributes include the tooling's unique identifier, type, specifications, 3D model, instructions for use, design standards, and remarks. For example, Tooling_Resource_ID provides a unique reference number for each tooling piece, while Tooling_Resource_Type specifies the tooling's category, such as a milling cutter or a fixture. Tooling_Resource_Spec details the tooling's specific specifications and dimensions, which is crucial for ensuring the tooling matches the machining task.

[0205] 3D models are an important component of tooling modeling, providing process designers with intuitive visual references and helping to anticipate potential compatibility issues during the design phase. Furthermore, by integrating design standards and user manuals, the correct selection and application of tooling are ensured, thereby avoiding errors and delays in the production process.

[0206] The value of tooling modeling lies in several aspects. It not only improves the efficiency of process design and reduces design errors, but also optimizes production processes, increasing machining accuracy and production efficiency. Furthermore, through structured tooling data, enterprises can better plan and manage resources, achieving cost control and quality assurance.

[0207] Tooling modeling is a complex but crucial process, involving multiple stages such as detailed tooling classification, attribute definition, data entry, and application implementation. This process provides manufacturing enterprises with a powerful tooling resource management platform, thereby supporting continuous improvement and innovation in their process design and manufacturing.

[0208] The auxiliary tooling model can be represented by eight features:

[0209] Tooling_Resource=(Tooling_Resource_ID,Tooling_Resource_Name,Tooling_Resource_Type,Tooling_Resource_Spec,Tooling_Resource_Model,Tooling_Resource_Usage,Tooling_Resource_Standard,Tooling_Resource_Remarks)

[0210] in,

[0211] Tooling_Resource_ID: A unique identifier for auxiliary tooling, used to uniquely identify a tooling in the database.

[0212] Tooling_Resource_Name: The name of the auxiliary tooling, used to describe the main features or purpose of the tooling.

[0213] Tooling_Resource_Type: The type of auxiliary tooling, such as cutting tools, fixtures, measuring tools, etc.

[0214] Tooling_Resource_Spec: The specific specifications or dimensions of the auxiliary tooling, which is crucial for ensuring that the tooling matches the machining task.

[0215] Tooling_Resource_Model: A 3D model or design drawing of the auxiliary tooling, providing an intuitive visual reference.

[0216] Tooling_Resource_Usage: This section provides instructions or application scenarios for auxiliary tooling, guiding process designers and operators on how to use it correctly.

[0217] Tooling_Resource_Standard: The standards upon which tooling design and application are based, such as international standards or internal company standards.

[0218] Tooling_Resource_Remarks: Additional notes or comments on the tooling, such as usage precautions, maintenance requirements, etc.

[0219] (3) Process knowledge modeling

[0220] Process knowledge is categorized into three types: process decision knowledge, process example knowledge, and auxiliary process resources. In the process of process knowledge modeling, the expert experience, factual knowledge, and procedural knowledge accumulated in the process design process are systematically organized and digitally expressed.

[0221] The design process for this step is described in detail below:

[0222] Process resources belong to professional domain knowledge, including expert-inspired experience, factual knowledge, and procedural knowledge. These types of knowledge are comprehensively applied at every moment in process design. For example, the selection of process methods, the determination of clamping schemes, and the arrangement of process routes all involve heuristic knowledge of decision-making rules, factual knowledge, and knowledge of problem analysis, understanding, and optimization. This knowledge can be summarized and abstracted into three types: process decision-making knowledge, process example knowledge, and auxiliary process resources.

[0223] 1. Process decision knowledge consists of empirical rules, process decision logic, procedural algorithms, and decision habits, such as rules for selecting machining methods, rules for process sequence, rules for selecting machine tools, cutting tools, fixtures, and measuring tools, decision knowledge for machining parameters, decision knowledge for process routes, and decision knowledge for machining methods.

[0224] 2. Process Example Knowledge. This includes knowledge of process specification examples and typical process data. A process specification example is a synthesis of existing part and process information within a process system. It is a process specification that has been proven correct and mature beforehand. A typical process is a set of standard processes for parts obtained by standardizing and normalizing a process specification.

[0225] 3. Auxiliary process resources are primarily used to support various auxiliary prompts during the process design process, providing process engineers with real-time, fast, and practical information services. This leaves decision-making tasks to process engineers. These include process dictionaries, commonly used process elements, and commonly used processes, which are automatically obtained from process data. Under certain conditions, these resources can be transformed into standard or typical processes, or used as components of them, improving the adaptability of group technology-based derived systems.

[0226] The core of process knowledge modeling is to systematically organize and digitally represent the expert experience, factual knowledge, and procedural knowledge accumulated during process design, enabling process engineers to quickly access and effectively apply them, thereby improving the accuracy and efficiency of process design. This process involves not only the collection and organization of expert experience, factual knowledge, and procedural knowledge, but also the careful design of the knowledge structure to ensure its retrieval and usability, aiming to build a knowledge base that supports process decision-making, optimization, and innovation.

[0227] Process knowledge modeling employs structured and semantic methods, transforming process knowledge into a queryable and analyzable data model by defining knowledge categories, attributes, and relationships. This enables precise description and effective organization of knowledge. Key features of the process knowledge model include unique identifiers, types, specific content, sources, application scenarios, and related knowledge or resources. These features collectively form the foundation of the process knowledge model, maximizing the utility of knowledge in process design.

[0228] The process of process knowledge modeling includes steps such as knowledge identification, classification, attribute definition, data modeling, data entry, verification, and updating. Through this process, a knowledge base can be built to support process decision-making, optimization, and innovation, providing process designers with a powerful knowledge support platform.

[0229] Process knowledge models have a wide range of applications. They not only serve as a support tool for process decision-making, helping designers select appropriate process methods and parameters, but also promote process optimization and improve production efficiency. Furthermore, process knowledge models facilitate knowledge sharing and transfer, accelerate the training process for new employees, and inspire new process design ideas and solutions. Through process knowledge modeling, we can significantly improve the efficiency and quality of process design, enhancing the competitiveness and innovation capabilities of the entire manufacturing process.

[0230] The process knowledge model can be represented by seven features:

[0231] Process_Knowledge=(Process_Knowledge_ID,Process_Knowledge_Type,Process_Knowledge_Content,Process_Knowledge_Source,Process_Knowledge_Application,Process_Knowledge_Related,Process_Knowledge_Remarks)

[0232] in,

[0233] Process_Knowledge_ID: A unique identifier for process knowledge, used to uniquely identify a process knowledge record in the database.

[0234] Process_Knowledge_Type: The type of process knowledge, such as decision knowledge, instance knowledge, auxiliary resources, etc.

[0235] Process_Knowledge_Content: The specific content of process knowledge, including rules, parameters, methods, etc.

[0236] Process_Knowledge_Source: The source of knowledge, which may come from expert experience, historical data, or literature.

[0237] Process_Knowledge_Application: The application scenarios and conditions of knowledge, guiding when and how to use this knowledge in process design.

[0238] Process_Knowledge_Related: Other knowledge or resources associated with this knowledge, supporting the comprehensive application of the knowledge.

[0239] Process_Knowledge_Remarks: Additional notes or comments on the knowledge, providing further background information or usage suggestions.

[0240] (4) Equipment resource modeling

[0241] When modeling process equipment, the model not only includes the basic information of the equipment, but also covers the equipment's operating status, capacity parameters, and remarks.

[0242] The design process for this step is described in detail below:

[0243] Based on their function, resources are categorized into motorized equipment, instruments and auxiliary equipment, power, electrical, transmission, and mechanical equipment. Mechanical equipment can be derived into lathes, milling machines, etc. Regardless of whether it's a lathe, milling machine, or drilling machine, object-level organization and management also involves descriptions of its structural form, capabilities, and status. The resource structure is the carrier of resource capabilities; its hierarchical structured representation helps analyze resource capabilities at different levels. Resource capabilities are mainly used to describe the manufacturing capabilities of manufacturing resources at different levels; resource status describes the performance status and dynamic characteristics of resources, such as changes in resource status, load conditions, and production status. It determines whether the resource's manufacturing capability can be achieved under the current conditions.

[0244] The core of equipment resource modeling is establishing a comprehensive equipment information database. This database not only contains basic equipment information but also covers operating status, capacity parameters, and notes. Such a model helps process designers, equipment maintenance engineers, and production managers quickly obtain the necessary information during the decision-making process, thereby improving production efficiency and equipment utilization.

[0245] Equipment resource modeling employs a structured approach, classifying equipment resources and describing their structural form, capabilities, and status. The model is constructed by defining a series of equipment attributes. These attributes include not only static information such as equipment number, name, model, and manufacturer, but also dynamic information such as equipment status and capabilities. Furthermore, for equipment status and capabilities, sub-attributes are defined to provide more detailed information, such as... Figure 5 As shown.

[0246] The equipment resource model consists of the following key features:

[0247] Equipment=(Equipment_ID,Equipment_Name,Equipment_Manufacturer,Equipment_Model,Equipment_Status,Equipment_Ability,Equipment_Remarks)

[0248] in,

[0249] Equipment_ID: A unique identifier for process equipment, used to uniquely identify a piece of equipment in the database.

[0250] Equipment_Name: The name of the process equipment, used to describe the main function or purpose of the equipment.

[0251] Equipment_Manufacturer: Manufacturer, provides information about the equipment manufacturer.

[0252] Equipment_Model: The model and 3D model of the process equipment, providing the physical specifications of the equipment.

[0253] Equipment_Status: A collection of device states, containing multiple state elements, each of which consists of the following sub-attributes:

[0254] Equipment_Ability: A collection of equipment capabilities, containing multiple capability elements, each of which consists of the following sub-attributes:

[0255] Equipment_Remarks: Additional notes or descriptions of the equipment, which may include specific operating requirements, historical maintenance records, or other relevant information.

[0256] The status of a device can be represented by the following five characteristics.

[0257] Equipment_Status=(Equipment_Status_ID, Equipment_Status_Name, Equipment_Status_Parameter, Equipment_Status_Normal_Parameter_Range, Equipment_Status_Remarks)

[0258] in,

[0259] Equipment_Status_ID: A unique identifier for the status.

[0260] Equipment_Status_Name: The name of the status, such as "Running", "Maintenance", etc.

[0261] Equipment_Status_Parameter: Status parameters, which describe specific indicators of the equipment status.

[0262] Equipment_Status_Normal_Parameter_Range: Normal parameter range, defines the expected range of status parameters when the equipment is running normally.

[0263] Equipment_Status_Remarks: Remarks that provide additional information or notes regarding the status.

[0264] Equipment capabilities can also be represented by the following five characteristics:

[0265] Equipment_Ability=(Equipment_Ability_ID,Equipment_Ability_Name,Equipment_Ability_Parameter,Equipment_Ability_Normal_Parameter_Range,Equipment_Ability_Remarks)

[0266] in,

[0267] Equipment_Ability_ID: A unique identifier for an ability.

[0268] Equipment_Ability_Name: The name of the capability, such as "maximum processing size" or "working efficiency".

[0269] Equipment_Ability_Parameter: Capability parameter, a quantitative indicator describing the equipment's capabilities.

[0270] Equipment_Ability_Normal_Parameter_Range: Normal parameter range, defines the normal value range of equipment capabilities.

[0271] Equipment_Ability_Remarks: Remarks that provide additional information or notes about the capability.

[0272] Step two of this application's embodiments outlines four key components of manufacturing resource modeling: process data, auxiliary tooling, process knowledge, and equipment resources. Through integrated modeling of these four components, a comprehensive resource management framework is provided for manufacturing enterprises, aiming to improve the efficiency and quality of process design, production scheduling, and equipment maintenance.

[0273] Process data modeling provides a structured database, enabling process designers to quickly retrieve key data such as required engineering materials and cutting parameters, thereby accelerating the process design process and improving the accuracy of decision-making. Auxiliary tooling modeling covers various tool resources used in the manufacturing process, such as cutting tools and fixtures. By detailing the specifications and usage conditions of these tooling components, it ensures the accuracy of process design and the efficiency of production. Process knowledge modeling integrates expert experience, factual knowledge, and procedural knowledge to form a knowledge base supporting process decision-making and optimization, promoting knowledge sharing and inheritance. Finally, equipment resource modeling provides data support for the effective use and maintenance of equipment by recording its specifications, performance, and status in detail.

[0274] The modeling work, which integrates these four parts, presents a comprehensive view of manufacturing resource management, which not only improves the efficiency of resource utilization but also enhances the transparency and controllability of the production process.

[0275] Step 3: Modeling the process information of complex equipment:

[0276] The process ontology information includes process composition information and process requirement information. When modeling, it also includes establishing compositional relationships between process composition information, hierarchical relationships between process requirement information from top to bottom, and correlation relationships between process composition information and process requirement information.

[0277] The design process for this step is described in detail below:

[0278] To support the process design of low-cost, large-scale production lines, it is necessary to model and describe the manufacturing processes of complex equipment to support process resource design and reuse. This step introduces the modeling methods for process composition information and process requirement information, and then proposes a process template model representation method that integrates various types of process ontology information.

[0279] Process ontology information is fundamental information describing the manufacturing process of a product. It plays the role of a design information carrier in the process design process, representing all content describing the manufacturing process and requirements. Therefore, the inductive representation of process ontology information is a necessary prerequisite for realizing the process design activities of complex equipment. Process ontology information can be divided into process component information and process requirement information, which complement each other and jointly realize the structured expression of process design activities. There are compositional relationships among process component information such as process routes, operations, and steps; and there is a top-down decomposition among process requirement information such as process route requirements, operation requirements, and step requirements. Furthermore, there is a correlation between process component information and process requirement information. A process template simultaneously includes process routes, operations, steps, and all process requirements, serving as a phased integrated carrier of process ontology information. The relationships between process ontology information are as follows: Figure 6 As shown.

[0280] (1) Process composition information modeling

[0281] To effectively utilize process design knowledge, it is essential to first establish a structured model representing process composition information, summarize and categorize the process composition information of complex equipment, and form standardized terminology and unified understanding to improve communication efficiency. At the same time, the processing methods of computer systems should be considered to facilitate computer processing and utilization, thereby improving the storage and management of process resources.

[0282] 1. Process step information modeling

[0283] A process step is an independent operation within a manufacturing process. Dividing processes into steps facilitates more detailed control and optimization of complex technological activities. Therefore, process steps should be used as the basic unit for representing the unit information of the manufacturing process.

[0284] When designing product processes, the product's characteristic requirements are typically first transformed into macroscopic process requirements. Then, a series of processes and corresponding process requirements are designed based on these requirements. Finally, detailed step-by-step planning and requirements are developed for each process. In this process design process, the step-by-step is the final embodiment of all process requirements: the overall process requirements and the process requirements at each stage must ultimately be translated into specific step-by-step requirements to truly provide direct guidance for the manufacturing process. Therefore, a step-by-step can be represented by the following seven characteristics:

[0285] Step=(Step_ID,Step_Name,Step_Req,Step_Equipment,Step_Quality_Control,Step_Material,Step_Remarks)

[0286] in,

[0287] Step: indicates a step in the process.

[0288] Step ID: Indicates the step number.

[0289] Step Name: Indicates the name of the work step.

[0290] Step Req: indicates the process step requirements, and specific information is introduced in the process requirement information modeling section.

[0291] Step_Equipment: Specifies the critical equipment required for the step.

[0292] Step_Quality_Control: Defines the quality control standards and testing methods for each process step.

[0293] Step_Material: Lists the raw materials or auxiliary materials used in each step.

[0294] Step Remarks: Represents notes or comments for each step of the process.

[0295] 2. Modeling of process attribute information

[0296] A process refers to a continuous production process performed by an operator at the same work location on one or more materials. In the above definition, the shared elements of "operator," "work location," "materials being processed," and "continuous process" are the primary basis for process division. A process consists of a series of steps that work together to change the characteristics of the materials being processed. The composition of these steps varies depending on the manufacturing resources used to achieve the same characteristic change. Each process must have corresponding manufacturing resources to realize it; this process can be completed by a single manufacturing resource or by multiple manufacturing resources working together. To improve production efficiency, it is often necessary to plan the space and logistics of the manufacturing resources that work together within the same process.

[0297] Based on the above analysis, the process can be represented by the following seven characteristics:

[0298] Process=(Process_ID,Process_Name,Process_Comp,Process_Duration,Process_Manures_Inst_Set,Process_Reg,Process_Remarks)

[0299] in,

[0300] Process: refers to a work procedure.

[0301] Process_ID: Indicates the process number.

[0302] Process_Name: Represents the process name.

[0303] Process_Comp: Represents the composition of a process, which is an ordered combination of multiple steps, and can be represented as:

[0304] Proc Comp = <stepi>,i=1,2,3….

[0305] Process_Duration: The estimated duration of the process.

[0306] Process_Manures_Inst_Set: Process Manufacturing Resource Instance Set, representing the set of manufacturing resource instances corresponding to a process.

[0307] Process_Reg: This represents the process requirements, which will be described in detail in the process requirements information modeling section.

[0308] Process_Remarks: Represents the notes or comments for the process.

[0309] 3. Process route information modeling

[0310] A process route is a summary of all the technological methods involved in the production and manufacturing of complex equipment, consisting of a series of operations combined in their implementation sequence. Process routes can also be divided into stages based on specialization, workshop, etc., such as heat treatment process routes and machining process routes. To improve production efficiency, enterprises typically plan the allocation of manufacturing resources based on typical process routes, including the quantity ratio, spatial layout, and logistics methods among different equipment. A well-designed process route can not only improve production efficiency and reduce manufacturing costs but also ensure product processing quality. In the manufacturing process of complex equipment, the completion of one operation will lead to a change in some characteristics of the processed material (including material, structure, properties, etc.), while the implementation of a process route will lead to a series of characteristic changes interacting to ultimately form the required product characteristics. There are usually mutual constraints on processing characteristics between the operations included in a process route. For example, earlier operations provide the necessary characteristic basis for later operations, while later operations generally cannot destroy the characteristics that need to be retained formed by all previous operations. Therefore, the operations in the process route and their sequential arrangement are fundamental elements for evaluating the rationality of the process route.

[0311] Parts that are similar to a certain extent require the same processing steps and sequence, differing only in the specific requirements of certain steps within those steps. By establishing typical process routes for these types of products or parts, they can be applied to the entire category of products or parts. Establishing typical process routes and a series of derivative process routes derived from them can provide significant assistance to a company's process design activities.

[0312] Based on the above analysis, the process route can be represented by the following seven characteristics:

[0313] Proc_Route=(Proc_Route_ID,Proc_Route_Name,Proc_Route_Comp,Proc_Route_Req,Proc_Route_Variants,Proc_Route_Optimization,Proc_Route Remarks)

[0314] in,

[0315] Proc_Route: indicates the process route.

[0316] Proc_Route_ID: Represents the process route number.

[0317] Proc_Route_Name: Represents the name of the process route.

[0318] Poc Route Comp: Represents the composition of a process route, which is an ordered combination of multiple processes, and can be represented as:

[0319] Route Comp= <processi>,i=1,2,3..

[0320] Proc_Route_Req: This indicates the process route requirements, which will be described in detail in the process requirement information modeling section.

[0321] Proc_Route_Variants: Records variations of the process route to handle different production scenarios.

[0322] Proc_Route_Optimization: Records the optimization history and improvement measures of the process route.

[0323] Proc_Route Remarks: Represents notes or comments related to the process route.

[0324] (2) Modeling of process requirements information

[0325] Process requirements are the constraints imposed by an enterprise on the technological methods used in the product manufacturing process, based on information such as product requirements, the enterprise's hardware and software conditions, and industry standards. The process component information was previously divided into three levels: steps, processes, and process routes. Correspondingly, process requirement information can also be divided into step requirements, process requirements, and process route requirements.

[0326] Process route requirements describe the overall process requirements for the materials being processed at a given stage of the process route. They typically focus only on the objectives of the process route without specifying the concrete methods used to achieve those objectives. Process route requirements are usually correlated with product requirements during the process design process and play a role in the selection of process routes and process templates. These requirements must be determined by the process engineer to define the components of the process route and further translated into process steps and procedures before they can guide production.

[0327] Process requirements are the phased process requirements formulated by process engineers for each process step in the overall process route, based on the overall process route requirements. They also involve the process objectives and operations for that phase. During process design, process requirements are typically decomposed and derived by process engineers from the process route requirements, and they impose processing capacity requirements on the manufacturing resources needed to achieve the process, thus influencing the selection of manufacturing resources. As mentioned earlier, a process consists of a series of steps performed in sequence; therefore, the realization of process requirements also requires the coordinated implementation of a series of steps.

[0328] Process step requirements are specific technological requirements designed by process engineers based on the company's actual production situation after determining the process steps and the manufacturing resources used to achieve them. These requirements directly guide the production process and involve the process objectives of the step and the technological operations required to achieve those objectives. Determining the process step requirements is the core and the most challenging aspect of the process design process.

[0329] The above three types of process requirements can all be summarized as target requirements and operational requirements. Target requirements refer to the process objectives expected to be achieved at the corresponding process stage, while operational requirements refer to the operational elements that need to be followed when using specified manufacturing resources (manufacturing equipment, site, personnel, etc.) to achieve the target requirements, such as the operating parameters of various equipment and site environmental parameters. Since the corresponding manufacturing resources are not yet determined at the process route stage, process route requirements usually only include target requirements and not operational requirements.

[0330] Both target requirements and operational requirements can be further represented using process attributes and process parameters. Similar to the enumerable attributes and quantifiable parameters of product information series, process attributes are typically used to describe enumerable process requirements, such as insulation materials and the number of spindles in a braiding machine; while process parameters are used to describe process requirements that are not suitable for enumeration, such as material usage and wire feeding speed.

[0331] 1. Representation of process attribute characteristics

[0332] The process attribute Proc_Attr can be represented by the following seven characteristics:

[0333] Proc_Attr=(Proc_Attr_ID,Proc_Attr_Name,Proc_Attr_Code,Proc_Attr_Unit,Proc_Attr_Enum,Proc_Attr_Dependencies,Proc_Attr_Remarks)

[0334] in,

[0335] Proc_Attr_ID: Represents the process attribute number.

[0336] Proc_Attr_Name: Represents the process attribute name.

[0337] Proc_Attr_Code: Represents the code or abbreviation of the process attribute.

[0338] Proc_Attr_Unit: Indicates the unit of the process attribute.

[0339] Proc_Attr_Enum: Represents a list of enumerated values ​​for process attributes.

[0340] Proc_Attr_Dependencies: Describes the dependencies between process attributes.

[0341] Proc_Attr_Remarks: Represents notes on process attributes.

[0342] 2. Representation of process parameters

[0343] The process parameter Proc_Param can be represented by the following six characteristics:

[0344] Proc_Param=(Proc_Param_ID,Proc_Param_Name,Proc_Param_Code,Proc_Param_Unit,Proc_Param_Type,Proc_Param_Calculations,Proc_Param_Remarks)

[0345] in,

[0346] Proc_Param_ID: Represents the process parameter number.

[0347] Proc_Param_Name: Represents the name of the process parameter.

[0348] Proc_Param_Code: Represents the code or abbreviation of the process parameter.

[0349] Proc_Param_Unit: Indicates the unit of the process parameter.

[0350] Proc_Param_Type: Indicates the value type of the process parameter, including single value, range value, and deviation value.

[0351] Proc_Param_Calculations: Defines the calculation method or formula for process parameters.

[0352] Proc_Param_Remarks: Represents notes on process parameters.

[0353] 3. Process Body Information Representation

[0354] Products or components within the same series share similar structural features, and their manufacturing processes are also largely similar. When designing the process for such products or components, process engineers typically refer to historical process designs of similar products to design new process routes and determine process attributes and parameters. In multi-variety, small-batch manufacturing enterprises, process design activities for similar products are extremely frequent. These activities require repeating parts of the design process that have already been performed once or even multiple times, resulting in a waste of human resources. Furthermore, human error may occur during the repetition process, leading to design results that cannot correctly guide the manufacturing process. To improve the efficiency and accuracy of process design for such typical products, process engineers can integrate process routes, operations, steps, and related process requirements to form a process entity information framework and establish a process template.

[0355] A process template can be represented by eight features:

[0356] Proc_Temp=(Proc_Temp_ID,Proc_Temp_Name,Proc_Temp_Prod_Series,Proc_Temp_Comp_Tree,Proc_Temp_Reg_Set,Proc_Temp_Conditions,Proc_Temp_Modifications,Proc_Temp_Remarks)

[0357] in,

[0358] Proc_Temp: represents the process template.

[0359] Proc_Temp_ID: Represents the process template number.

[0360] Proc_Temp_Name: This represents the process template name, which is usually represented by "product series name + product instance name" corresponding to the process template, so that process engineers can obtain rich information from it.

[0361] Proc_Temp_Prod_Series: indicates the typical product series that the process template targets.

[0362] Proc_Temp_Comp_Tree: Represents the process composition tree of the process template, which represents the process route, operation, and step in a tree form according to their interrelationships.

[0363] Proc_Temp_Reg_Set: Represents the set of process requirements for the process template, which is derived from the process route, operation, and step contained in the process composition tree.

[0364] Proc_Temp_Conditions: Defines the specific conditions and environment under which the process template applies.

[0365] Proc_Temp_Modifications: Records the modification history of process templates and the reasons for changes.

[0366] Proc_Temp_Remarks: Represents the notes information for the process template.

[0367] A process template is a carrier of all process-related information for a product or component at a certain production stage. The process route requirements are often generated based on product requirements, and the processes are directly related to manufacturing resources. Therefore, a process template is a phased integration of product information, manufacturing resource information, and process-related information. Establishing process templates based on typical enterprise products can lay the foundation for building a composite process resource model.

[0368] This step describes the modeling of process information for complex equipment. It introduces the modeling and integration methods for process ontology information from two perspectives: process composition information and process requirement information. The modeled representation of process ontology information is divided into three levels: process step, process operation, and process route. Finally, it is integrated through process templates to form a process ontology information framework for typical products, providing a carrier for intelligent process design.

[0369] Step 4: Setting up the process resource database management module

[0370] The requirements of process resource management are to ensure that users can easily and quickly manage process resource data, rapidly search and apply process resources, and modify, delete, and add new process resources. Due to the large variety of process resources and their interrelationships, users in a process resource management system need the specific attributes and characteristics of a particular process resource object. Achieving comprehensive process resource management requires extracting features from all process resource objects. The above describes the modeling of various types of process resources; each type of process resource is a model, and managing the knowledge model is equivalent to managing process resource classes. Each type of process resource, after being assigned attribute values, becomes a process resource object; managing process objects is equivalent to managing process resource attribute values. Simultaneously, process resources must support model import and export, enabling interaction with knowledge models from other systems and ensuring the consistency of knowledge structure attributes.

[0371] Process resource model management

[0372] The process resource model can be represented by a tree, with classes of different hierarchical structures. Each class of knowledge has unique attributes, and knowledge objects of the same class have consistent models and the same attributes. Managing the process resource model helps reduce data redundancy in the system and improves data utilization. This module can also define the attribute values ​​and attribute types for each class of knowledge. Specifically, it implements functions such as adding, deleting, and modifying attributes. When an attribute of a class is edited, the attributes of its subclasses will also change accordingly. This facilitates the use of a database to build relationship models between different process resource categories, improving the application efficiency of process resources.

[0373] Process resource object management

[0374] The process resource object management module targets each node in the process resource structure tree. This module implements functions such as adding, editing, and deleting knowledge objects, and provides a quick data query function. When editing a process resource object, its characteristic values ​​can be directly modified. When adding a new process resource object, the characteristics of existing similar process resources can be utilized; only the corresponding characteristic values ​​need to be entered.

[0375] (1) Database storage tools

[0376] Database selection is fundamental for data storage and management. MySQL, as a relational database, can be implemented using different compilers, ensuring its source code portability. It supports multiple operating systems and multi-threaded operation, maximizing CPU utilization. It can function as a standalone application or be embedded as a database within other software. It provides multilingual support, and common character encodings such as GB2312 and BIG5 for Chinese can be used as table and column names. Furthermore, it offers various database interfaces, such as TCP / IP, ODBC, and JDBC, and supports large databases, enabling operations on massive datasets with millions of records.

[0377] (2) Complex Equipment Product Resource Management Module

[0378] The model representation of product information includes three levels: product category, product series, and product instance. Therefore, the product information management module in the prototype system is divided into three parts: product category management, product series management, and product specification management.

[0379] Product category management uses a tree structure to categorize manufacturing companies' products based on macroscopic characteristics, such as function, process status, and usage scenarios. The main functional logic of this module includes basic functions such as adding, deleting, modifying, and querying category tree nodes.

[0380] Product series management involves grouping and managing similar products. The elements for creating a product series correspond to the model, including series name, series category, and series description. To complete product series information, it's necessary to first establish a product attribute library and parameter library describing product characteristics, and then configure attributes and parameters for the product series based on these. Product specifications are instantiations of product series. The product specification management submodule is responsible for assigning values ​​to the product parameters included in the product series, forming product specification information containing complete product characteristic descriptions.

[0381] The data relationships between the concepts in the above product information management module can be represented by an ER diagram (Entity Relationship Diagram), such as... Figure 7 As shown in the diagram. To more clearly illustrate the relationships between the various conceptual entities, some unimportant entity attributes, such as product attribute and parameter codes and units, have been removed from the entity relationship diagram presented in this paper. The entity relationship diagram can be converted into a relational schema using a formulaic method, thereby establishing tables in a relational database. Detailed methods for this conversion exist in the field of software engineering and will not be elaborated upon here.

[0382] The ER diagram concept primarily refers to entities, attributes, and relationships. In an ER diagram, entities are represented by rectangles, with the entity's name written inside. For example, in a product information management module, entities might include "product category," "product series," and "product specification." Attributes are represented by ellipses and connected to the corresponding entities. Attributes in the product information management module might include the name of the product category, the description of the product series, and the specific parameters of the product specification. In an ER diagram, relationships are represented by diamonds and connected to the related entities. Relationships can also have attributes to describe information related to the relationship. For example, in a product information management module, there might be relationships like "belongs to" (product specification belongs to product series) or "contains" (product series contains product specification).

[0383] (3) Complex Equipment Manufacturing Resource Management Module

[0384] The model representation of manufacturing resource information includes three levels: resource classification, resource series, and manufacturing resource instance. Therefore, the manufacturing resource information management module in the prototype system is divided into three parts: manufacturing resource classification management, manufacturing resource series management, and manufacturing resource specification management.

[0385] Manufacturing resource classification management uses a tree structure to categorize manufacturing enterprises' products based on macroscopic characteristics, such as function, process status, and usage scenarios. The main functional logic of this module includes basic functions such as adding, deleting, modifying, and querying classification tree nodes.

[0386] Manufacturing resource series management involves grouping and managing similar resources. The elements for establishing a manufacturing resource series correspond to the model, including series name, series category, and series description. Improving resource series information requires first establishing a resource attribute library and parameter library describing product characteristics, and then configuring attributes and parameters for the resource series based on these.

[0387] Manufacturing resource specifications are instantiations of resource series. The resource specification management submodule is responsible for assigning values ​​to the resource parameters contained in the resource series, forming resource specification information containing complete resource characteristic descriptions.

[0388] The data relationships between the concepts in the above manufacturing resource information management module can be represented by an ER diagram (Entity Relationship Diagram), such as... Figure 7 As shown in the diagram. To more clearly illustrate the relationships between the various conceptual entities, some unimportant entity attributes, such as the codes and units of manufacturing resource attributes and parameters, have been removed from the entity relationship diagram presented in this paper. The entity relationship diagram can be converted into a relational schema using a formulaic method, thereby establishing tables in a relational database. Detailed methods for this conversion exist in the field of software engineering and will not be elaborated upon here.

[0389] (4) Complex Equipment Process Information Management Module

[0390] The process (entity) information management module enables the establishment and integrated application of models related to process composition information and process requirements information. This module mainly includes sub-modules such as step management, process management, process route management, and process requirements management.

[0391] The process requirement management submodule categorizes process requirements into two types: target requirements and operational requirements. Both types are represented by process attributes and parameters. Since many process requirements are difficult to categorize as either target or operational, they can be specified as one type or created as general requirements when creating them in the system. The prototype system does not differentiate between process steps, operations, or process routes within the process requirements themselves. Associating a process requirement with a specific type of process component information determines the level of requirement represented by that association. During configuration, it can be specified whether each process requirement is displayed in the final process design results, balancing the completeness of the process design with the simplicity of the design outcome.

[0392] After establishing the process requirement management submodule, a process step management submodule can be implemented on top of it. The process step management submodule mainly includes the creation and maintenance of process steps, and the configuration management functions for process steps and process step requirements.

[0393] Building upon the step management submodule, a process management submodule can be further established. Since there are clear distinctions in process specialties (trades) between the various processes of complex equipment, to further clarify the classification of process types, the process management submodule, based on process content (clamping and positioning datum, machined surface, and machining requirements, etc.), needs to add a process classification function to allow for the categorized management of processes according to requirements.

[0394] In addition to basic information such as process classification and process name, the elements for establishing process information also include process requirements, process manufacturing resources, process composition, and other elements that create relationships with other types of information. The configuration of process requirements is consistent with the requirements for process steps and will not be elaborated further. The configuration of process manufacturing resources needs to be based on the actual situation, selecting manufacturing resource instances suitable for implementing the process. Because different manufacturing resource instances may use different specific steps to implement a certain process, the process composition may be affected by the configuration of process manufacturing resources. Depending on the configuration of manufacturing resources, each process may have a series of process entities in the system.

[0395] The functions of the process route management submodule are basically the same as those of the process management submodule, except that it lacks classification and manufacturing resource allocation. Further details will not be provided here.

[0396] A process template is an integration of process (entity) information, which includes a process route and all its processes, steps, and related process requirements. Creating a process template requires associating it with a typical product series.

[0397] The entity relationship diagram corresponding to the data relationships between various conceptual entities in the process information management module is as follows: Figure 8 As shown, there is a hierarchical relationship between process routes, processes, and steps, and the process template contains information from all three types of models. Since the prototype system does not mandate a distinction between target requirements and operational requirements, the diagram does not further differentiate between the three types of process requirements.

[0398] If the relationships between process templates and typical product objects (series) and between processes and manufacturing resource instances are also reflected in the ER diagram, then the ER diagram of this integrated information would look like this: Figure 9 As shown, product information is integrated with process information through the association between product series and process templates, while manufacturing resource information is integrated with process information through the association between instances and processes.

[0399] Another embodiment of this application further includes the step of updating the database in real time:

[0400] With the development of industrial automation and intelligence, the amount of data generated in the production process has increased dramatically. This data is crucial for monitoring production lines, optimizing production processes, preventing equipment failures, and improving resource utilization. In the field of industrial databases, real-time update technology primarily addresses the issues of data immediacy and accuracy. Real-time update technology ensures that this critical data is reflected in the database in an up-to-date and accurate manner, thereby providing reliable information support for decision-makers. This technology achieves real-time data writing and updating by rapidly responding to changes in the production environment, keeping the database state synchronized with the actual production status.

[0401] Industrial databases require immediate updates when data changes to ensure consistency and real-time performance. This involves using triggers, event-driven architectures, and efficient data synchronization mechanisms to achieve real-time data updates. Due to the database structure design in this study, the data requiring real-time updates on the production line primarily resides in the manufacturing resource module, such as... Figure 10 As shown.

[0402] First, real-time update technology relies on efficient data acquisition mechanisms. In industrial environments, this typically involves direct integration with sensors, controllers, and other data sources. These devices monitor various parameters on the production line in real time, such as temperature, pressure, and speed, and send this data as a stream to a database system.

[0403] Next, the data stream processing component processes these continuous data streams. This may include data cleaning, transformation, and aggregation operations, converting corresponding data changes into feature changes in the manufacturing resource model to ensure that only relevant and accurate data is written to the database. In this step, the system uses a data-driven architecture to listen for data change events (such as insert, update, or delete operations) to identify and respond to specific events or data patterns, and immediately takes appropriate update operations when the event occurs, thereby achieving real-time updates.

[0404] Subsequently, after receiving the update request, the database management system will detect the parent category of the update feature and determine the update location according to "process type - resource type - feature type". It will use a concurrency control mechanism to handle multiple simultaneous update operations to ensure data consistency and integrity, and maintain data accuracy even in a high-concurrency environment.

[0405] Finally, after the data is successfully written to the database, real-time update technology also includes data backup and recovery strategies to ensure system robustness and data persistence. This means that even in the event of a system failure, committed updates must be permanently saved, and the system can quickly recover to its pre-failure state.

[0406] In another embodiment of this application, the process of generating a manufacturing plan based on the above-mentioned database is as follows:

[0407] The ultimate goal of building a structured database is to reduce the overall production cost of the production line. To achieve this goal, the overall framework of the structured database is designed to generate manufacturing solutions. In determining a manufacturing solution, not only are product manufacturing requirements and various knowledge describing the process ontology concept needed, but also the requirements for manufacturing resources to generate the manufacturing solution must be clearly defined. By connecting and integrating "product-process-manufacturing resources," a feasible manufacturing solution can be provided, such as… Figure 11 As shown.

[0408] (1) For the production and manufacturing of a specific product, first list the manufacturing plan requirements based on the bill of materials and part characteristics of the product instance.

[0409] (2) Based on the manufacturing scheme requirements, search in the process ontology information database to find similar process instances. If an identical instance is found, it can be directly used. If there is a similar instance, it can be used as a reference for manufacturing scheme design.

[0410] (3) In the production process analysis and manufacturing plan determination stage, based on the consideration of part information and process constraints, process data and process knowledge information should be considered, and more auxiliary tooling and equipment resource information should be applied to select idle equipment and tooling resources.

[0411] (4) During the manufacturing scheme verification stage, more detailed consideration of process data and process knowledge is needed to ensure the safe production of the manufacturing scheme. After the manufacturing scheme is roughly determined, it is tested according to the technical requirements of the product example, such as temperature adaptability.

[0412] (5) Finally, compile complete process documentation, including process flow diagrams, operating procedures, and quality control plans. Feed new process knowledge into the knowledge base to provide a reference for future process design.

[0413] This application describes the modeling of process information for complex equipment. It introduces the modeling and integration methods for process ontology information from two perspectives: process composition information and process requirement information. The modeled representation of process ontology information is divided into three levels: process step, process operation, and process route. Finally, it is integrated through process templates to form a process ontology information framework for typical products, providing a carrier for intelligent process design.

[0414] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.< / processi> < / stepi>

Claims

1. A method for constructing a structured database of resource models for large-scale production lines of complex equipment, characterized in that, The specific process is as follows: Step 1, Product Information Modeling: Product information is modeled into three levels: product category, product series, and product instance. Product series belong to product category, and product instance belongs to product series. Step 2: Modeling resource information for complex equipment manufacturing: For each manufacturing process, a process resource information database has been established, which stores four categories of knowledge: process data, process knowledge, auxiliary tooling, and process equipment. Step 3: Modeling the process information of complex equipment: The process ontology information includes process composition information and process requirement information. When modeling, it also includes establishing compositional relationships between process composition information, hierarchical relationships between process requirement information, and correlation relationships between process composition information and process requirement information. Step 4: Setting up the process resource database management module: The process resource database management module establishes process resource model management and process resource object management; Step one includes product category modeling, product series modeling, and product instance modeling, wherein: Product classification modeling: Classifying complex equipment products according to similarity in selected dimensions to form a product classification method with clear structure and well-defined boundaries; Product series modeling: Grouping products with similar product elements into a product series, where product elements include: function, structure, and process; Product instance modeling: The product BOM (Bill of Materials), technical requirements, and product part technical characteristics are used as features of the product instance model. The product BOM contains all parts, raw materials, and sub-assemblies required to manufacture, assemble, or maintain the product, including their models, quantities, and sources; the technical requirements contain performance standards and test conditions; and the product part technical characteristics provide detailed dimensions, shapes, and material properties for each component. Step two includes: process data modeling, auxiliary tooling modeling, process knowledge modeling, and equipment resource modeling. Process data modeling: Classify process data according to its type and purpose, and define a unified data model for each type of data. In the modeling process, define multiple key attributes, data sources and validity for process data. Auxiliary tooling modeling: First, identify and classify the various tools and equipment used in the manufacturing process, and determine the function and application scenario of each type of tooling; second, establish a detailed classification system to group tooling according to its function, purpose and design standards; third, define a series of key attributes for auxiliary tooling to comprehensively describe its characteristics and application conditions. Process knowledge modeling: Process knowledge is categorized into three types: process decision knowledge, process example knowledge, and auxiliary process resources. In the process of process knowledge modeling, the expert experience, factual knowledge, and procedural knowledge accumulated in the process design are systematically organized and digitally expressed. Equipment resource modeling: The model not only includes the basic information of the equipment, but also covers the equipment's operating status, capacity parameters, and remarks. Step three includes: process composition information modeling and process requirement information modeling; Process composition information modeling includes step information modeling, process attribute information modeling, and process route information modeling; Process requirement information modeling includes process attribute feature representation, process parameter feature representation, and process ontology information representation; The process resource database management module includes: a database storage tool, a complex equipment product resource management module, a complex equipment manufacturing resource management module, and a complex equipment process information management module; The complex equipment product resource management module is used for product classification management, product series management, and product specification management. Product category management uses a tree structure to classify the products of manufacturing enterprises based on macroscopic characteristics, and provides basic functions such as adding, deleting, modifying, and querying category tree nodes; Product series management involves: establishing a product attribute library and parameter library that describe product characteristics; configuring attributes and parameters for product series; assigning values ​​to product parameters included in the product series; and forming product specification information that contains complete product characteristic descriptions. Product specification management uses ER diagrams to represent the data relationships between various concepts. Entity relationship diagrams can be transformed into relational schemas using formulaic methods, thereby establishing tables in a relational database. The complex equipment manufacturing resource management module includes three levels: resource classification, resource series, and manufacturing resource instances, which are used for manufacturing resource classification management, manufacturing resource series management, and manufacturing resource specification management. Manufacturing resource classification management uses a tree structure to classify the products of manufacturing enterprises based on macroscopic characteristics. The functional logic includes adding, deleting, modifying, and querying classification tree nodes. The elements for establishing a manufacturing resource series correspond to the model, including series name, series category, series description, establishing a resource attribute library and parameter library to describe product characteristics, and configuring attributes and parameters for the resource series; The manufacturing resource specification management includes assigning values ​​to resource parameters to form resource specification information containing complete resource characteristic descriptions; The complex equipment process information management module realizes the establishment and integrated application of models related to process composition information and process requirement information, including sub-modules for process step management, process management, process route management and process requirement management; The process requirements management submodule divides process requirements into two categories: target requirements and operational requirements. By associating a process requirement with a certain type of process component information, the process requirement under that association represents the requirements at the corresponding level. During the configuration process, it is specified whether each process requirement is displayed in the final process design results. The work step management submodule includes the creation and maintenance of work steps, as well as the configuration management functions for work steps and work step requirements; The process management submodule adds a process classification function on the basis of process content, so as to classify and manage processes according to requirements.

2. The method for constructing a structured database for a resource model of a large-scale production line of complex equipment according to claim 1 further includes a database update step, the specific process of which is as follows: First, various parameters of the production line equipment are monitored in real time and sent to the database system in the form of a stream; Secondly, the data stream is processed to transform the corresponding data changes into feature changes in the manufacturing resource model and write them into the database. By listening to data change events, specific events are identified and responded to, and corresponding update operations are immediately taken when the event occurs, thereby achieving real-time updates. Secondly, after receiving an update request, the database management system will detect the parent category of the update characteristics and use a concurrency control mechanism to handle multiple simultaneous update operations. Finally, after the data was successfully written to the database, the data backup and recovery strategy was further updated.

3. A process design method based on a structured database of resource models for large-scale production lines of cost-complex equipment according to any one of claims 1-2, the specific process of which is as follows: (1) For the production and manufacturing of a specific product, obtain the bill of materials and part characteristics of the product instance based on the product classification information, and list the manufacturing plan requirements; (2) Based on the manufacturing scheme requirements, search in the process ontology information database to find similar process instances. If an identical instance is found, it is directly borrowed. If there is a similar instance, it is used as a reference for manufacturing scheme design. (3) In the production process analysis and manufacturing scheme determination stage, based on the consideration of part information and process constraints, the process data and process knowledge in the manufacturing resource information are considered, and the auxiliary tooling and equipment resource information is applied to select idle equipment and tooling resources. (4) During the manufacturing scheme verification stage, process data and process knowledge need to be considered in more detail to ensure the safe production of the manufacturing scheme, and after the manufacturing scheme is determined, it is inspected according to the technical requirements of the product examples in the product information database. (5) Finally, compile complete process documentation, including process flow diagrams, operating procedures and quality control plans, and feed new process knowledge back into the knowledge base to provide reference for future process design.

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