Process-assisted rapid compilation method based on grouping technology and MBD model

Abstract

The invention discloses a process-assisted rapid compilation method based on a grouping technology and an MBD model, and the method comprises the steps: obtaining the MBD model, and converting the MBD model into a data format which can be recognized and analyzed; a feature information acquisition stage: analyzing the MBD data model which comprises structured annotation information and unstructured annotation information, and mapping process feature information contained in the part according to the analyzed information; a process feature comparison stage: performing empowerment distance measurement comparison according to the process feature information of the part and a process knowledge base with a process feature information identifier to obtain an initial process instruction file with high similarity; in the evaluation optimization and revision stage, manufacturing feasibility evaluation is carried out, key parameters are verified after evaluation is passed, and then other non-key description information is optimized; and if the evaluation is not passed, feeding back a matching environment, and performing secondary matching after excluding the selected answers. The method is easy to implement and has higher trueness and technical feasibility.

Description

Technical Field

[0001] The present invention belongs to the technical field of detailed design of process parts in the field of intelligent manufacturing, and specifically relates to a process-assisted rapid compilation method based on group technology and MBD model. Background Art

[0002] In modern manufacturing, process design serves as the core link between product development and production manufacturing, and its effectiveness directly determines a company's cost control and market responsiveness. Existing process design generally relies on manual experience, and process documents are compiled by interpreting two-dimensional drawings. This has pain points such as low efficiency, insufficient standardization, and difficulty in knowledge transfer. Although Model Based Definition (MBD) technology is gradually replacing traditional two-dimensional drawings, the massive amount of structured information it integrates, such as geometry, tolerances, and processes, is still difficult to be effectively analyzed by the process system. Although grouping technology can achieve process reuse through part feature classification, traditional methods rely on manual recognition, resulting in limited efficiency and accuracy, and have not yet achieved system integration with three-dimensional MBD models, which restricts the overall development of digital process design. Summary of the Invention

[0003] To address the above technical issues, the present invention provides a rapid process-assisted compilation method based on group technology and MBD models. By analyzing the geometry, tolerances, and process information in the MBD model and leveraging a dynamic mapping mechanism between process features and a group knowledge base, the method automatically extracts the process characteristic parameters of a part. Furthermore, a weighted distance measurement algorithm is employed for intelligent matching with the process knowledge base to rapidly generate highly similar process instruction files. This reduces the reliance on manual experience during the compilation process, improves the utilization of process knowledge and efficiency, and enhances the stability of the enterprise's process.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A process-assisted rapid compilation method based on group technology and MBD model, including:

[0006] Step S01, data preparation stage: obtain the MBD model and convert it into a data format that can be recognized and parsed;

[0007] Step S02, feature information acquisition stage: parsing the MBD data model, including structured annotation information and unstructured annotation information, and mapping the process feature information contained in the part based on the parsed information;

[0008] Step S03, process feature comparison stage: performing weighted distance measurement comparison on the process feature information contained in the part and the process instruction file knowledge base with process feature information identification to obtain an initial process instruction file with high similarity;

[0009] Step S04, evaluation, optimization and matching stage: a manufacturing feasibility evaluation is performed on the initial process instruction file. If the evaluation passes, a revised process instruction file is released after verification; if the evaluation fails, step S03 is executed again.

[0010] Furthermore, step S01 includes:

[0011] Step S01.1, obtain the design MBD digital model file and related part basic information through the API interface;

[0012] Step S01.2: Convert the designed MBD digital model file into a neutral format .step file.

[0013] Furthermore, step S02 includes:

[0014] Step S02.1, parse the .step file to obtain MBD feature data;

[0015] Step S02.2: Compare the obtained MBD feature data and basic part information with the process feature mapping information knowledge base to form a process feature information sequence that matches the part. The process feature information sequence is divided into "important process features" and "general process features";

[0016] Step S02.3: Establish and store the link relationship between the part and the process feature information sequence.

[0017] Furthermore, step S03 includes:

[0018] Step S03.1, based on the process characteristics of the part, call the knowledge base with process instruction files built by group technology to obtain the process instruction file sequence related to the process characteristic parameters in the knowledge base;

[0019] Step S03.2, calculating the weighted parameters required for each process feature of the part;

[0020] Step S03.3, combining the weight values ​​of “important process characteristics” and “general process characteristics”;

[0021] Step S03.4, using the weighted Euclidean similarity algorithm to calculate the similarity of parts;

[0022] Step S03.5, sorting by similarity from high to low, and outputting the best matching and sub-best matching process instruction files.

[0023] Furthermore, S04 includes:

[0024] S04.1, based on the optimal matching and suboptimal matching process instruction files, retrieve and initialize corresponding files from a process instruction knowledge base to form an initial process instruction file;

[0025] S04.2, conduct a manufacturing feasibility assessment;

[0026] S04.3, if the assessment is passed, key parameters and document contents shall be proofread and revised, and the process personnel shall interactively review the final results and publish the revised process instruction documents.

[0027] Furthermore, the important process feature marks represent part name, part type, and material, and the general process features represent heat treatment requirements, surface treatment requirements, and testing requirements.

[0028] Furthermore, step S03.1 includes:

[0029] Step S03.1.1: Obtain the important process feature sequence and general process feature sequence of the part;

[0030] Step S03.1.2: extracting process instruction file sequences related to important process feature sequences and general process features from the knowledge base with process instruction files constructed by group technology;

[0031] Step S03.1.3: Integrate step S03.1.1 and step S03.1.2 into three process sequences and output them as parameters to step S03.2.

[0032] Furthermore, step S03.2 includes:

[0033] Step S03.2.1: Set the probability values ​​of the important process characteristics and general process characteristics of the part to P and 1-P respectively;

[0034] Step S03.2.2: Calculate the weights of important process features using the discrimination weight method;

[0035] Step S03.2.3: Calculate the weights of conventional general process characteristics using the entropy weight method.

[0036] Furthermore, the step S03.2.2 includes:

[0037] Step S03.2.2.1: Count the hits of important process feature sequences in the process instruction file sequence, with hit process features recorded as 1 and miss process features recorded as 0;

[0038] Step S03.2.2.2: Calculate the mean hit value of each important process feature of the part and standard deviation ;

[0039] Step S03.2.2.3: Calculate the weights of important process characteristics using the formula Calculate the weight value of each important process feature to form an important process feature weight sequence .

[0040] Furthermore, the step S03.2.3 includes:

[0041] Step S03.2.3.1: Count the number of times the general process feature sequence hits the process instruction file sequence;

[0042] Step S03.2.3.2: Calculate the hit probability of each general process feature of the part based on the number of hits and entropy ;

[0043] Step S03.2.3.3: Calculate the general process characteristic weights using the formula Calculate the weight value of each general process feature to form a general process feature weight sequence .

[0044] Furthermore, in step S03.4, the similarity value is calculated by the following formula:

[0045] ,

[0046] Where, Represents the total number of important process features, Represents the total number of general process characteristics.

[0047] In a second aspect, the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned process-assisted rapid compilation method based on grouping technology and MBD model.

[0048] In a third aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned process-assisted rapid compilation method based on grouping technology and MBD model.

[0049] The beneficial effects of the present invention are:

[0050] By analyzing MBD information and structuring the multifaceted information of the MBD model, including geometry and tolerances, this approach reduces the risk of errors associated with traditional manual interpretation and enhances the accuracy and completeness of process data transmission. A process feature matching algorithm based on a grouped knowledge base reduces reliance on manual experience, shortens the process plan development cycle, and improves knowledge reuse. This approach provides manufacturing companies with a technical platform for accumulating process knowledge and intelligently upgrading it, promoting the transition from discrete, empirically driven process design to a systematic, data-driven one, effectively supporting the construction and development of intelligent manufacturing systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a general step diagram of a process-assisted rapid compilation method based on group technology and MBD model provided by the present invention;

[0052] Figure 2 This is a detailed step diagram of a process-assisted rapid compilation method based on group technology and MBD model provided by the present invention;

[0053] Figure 3 It is a storage mode guide map of a process instruction knowledge base with process feature identification constructed by the grouping technology provided by the present invention;

[0054] Figure 4 This is the detailed steps of obtaining similar process instruction files by using the knowledge base and part feature comparison algorithm provided by the present invention. DETAILED DESCRIPTION

[0055] The present invention will be further described below with reference to the accompanying drawings and examples.

[0056] The process-assisted rapid compilation method based on group technology and MBD model proposed in this paper significantly improves process design efficiency and quality through digital means. Its core advantages are reflected in three aspects:

[0057] 1. By analyzing the structured annotation information (such as geometric dimensions and tolerance specifications) and unstructured PMI (Product and Manufacturing Information) annotations in the MBD model, process feature information can be extracted, breaking through the limitations of traditional manual interpretation of 2D drawings and reducing knowledge transfer errors;

[0058] 2. This method enriches the group technology and customizes the corresponding process characteristics as identifiers, which improves the utilization rate of process knowledge and helps enterprises improve the standardization of process design while enhancing the ability of technological iteration;

[0059] 3. A weighted distance measurement algorithm is used to match process characteristics with the knowledge base. By optimizing the comparison logic through weight assignment, highly similar process solutions can be quickly obtained, improving efficiency and accuracy compared to traditional manual search. In practical applications, this shortens the process solution preparation cycle and accelerates the response to process changes, effectively solving the problems of fragmented process knowledge and strong reliance on experience in traditional models.

[0060] Under the development trend of intelligent manufacturing, process design will deeply integrate the full-factor definition capability of the MBD model and the clustering characteristics of group technology. By constructing a closed-loop system of feature analysis-knowledge matching-process optimization, it will realize the transformation of process solutions from experience-driven to data-driven, providing key technical support for the intelligent transformation of the manufacturing industry.

[0061] This invention implements a process-assisted rapid compilation method based on group technology and MBD model, which is used to identify various process feature elements in the manufacturing process based on MBD (Model Based Definition) information during the detailed process design stage. The algorithm is used to quickly and accurately obtain component knowledge files with the same manufacturing characteristics from the local knowledge base, thereby reducing individual differences and improving process stability. Figure 1 As shown, the method includes:

[0062] Step S01, data preparation stage: obtain the MBD model and convert it into a data format that can be recognized and parsed;

[0063] Step S02, feature information acquisition stage: parsing the MBD data model, including structured annotation information and unstructured annotation information, and mapping the process feature information contained in the part based on the parsed information;

[0064] Step S03, process feature comparison stage: performing weighted distance measurement comparison based on the process feature information contained in the part and the process knowledge base with process feature information identification to obtain an initial process instruction file with high similarity;

[0065] Step S04, evaluation, optimization and matching stage: Carry out manufacturing feasibility assessment. If the assessment passes, verify the key parameters and optimize other non-critical descriptive information. If the assessment fails, feedback on the matching environment is provided, and step S03 is re-executed after excluding the selected answer.

[0066] During the detailed process design, the main wing rear beam frame component was selected as the research object. The basic information of this component is as follows: Part No. BCA-65B98765-003, Version No. C. Figure 2 As shown, based on this part as the object, the specific implementation method of each step is specifically explained:

[0067] Step S01: Data preparation phase: Obtain the MBD model and convert it into a data format that can be identified and parsed. Since different design departments may use different CAD software, it is necessary to find the corresponding digital model file based on the basic information of the component and then convert the CAD model file into a consistent format, uniformly converting it into a common intermediate format .step; for the design MBD model of the BCA-65B98765-003 component provided by the heterogeneous system, information processing, model format conversion and file storage are performed, which requires storing the basic information provided by the design (component number, version, name, etc.), the temporary storage address of the model file, the storage address of the .step file, etc. The specific implementation process is as follows:

[0068] Step S01.1, obtain the design MBD digital model file and related part basic information through the API interface;

[0069] Step S01.2, converting the MBD digital model file into a neutral format .step file;

[0070] Step S01.2 is determined by the data format provided by the design department. If the MBD model provided by the design department can directly extract MBD information, step S01.2 can be skipped.

[0071] Step S02: Acquisition of feature information stage: Parse the MBD data model, including structured annotation information and unstructured annotation information, and map out the process feature information contained in the part based on the parsed information. Parse the component .step file generated by conversion in step S01 to extract the model-based definition (MBD) feature data in the three-dimensional model. By mapping and comparing the MBD features obtained by parsing with the manufacturing feature information preset in the process feature mapping information knowledge base, a process feature set containing a process classification identifier is output (including two-level classifications of key process features and conventional general process features). By constructing an association mapping mechanism between process features and components, intelligent process labeling based on feature classification is achieved;

[0072] For example: Use the .step file of the BCA-65B98765-003 component output from step S01 to parse and extract MBD information; then compare the extracted MBD information with the information in the process feature mapping information knowledge base to generate process feature information piece by piece. Finally, establish an association between the mapped process features and the component (the mapped process features are stored as important process features and general process features). The specific implementation process is as follows:

[0073] Step S02.1, parse the .step file to obtain MBD feature data;

[0074] Step S02.2, based on the proposed MBD feature data and part basic information, the process feature mapping information knowledge base is compared to form a process feature information sequence that matches the part, which is recorded as , the process feature information sequence is divided into "important process features" and "general process features"; important process features are represented by part name, part type, material, etc. as a sequence , general process characteristics indicate specific heat treatment requirements, surface treatment requirements, testing requirements, etc. are recorded as a sequence ;

[0075] Step S02.3: Establish and store the link relationship between the part and the process feature.

[0076] Step S02.2: The MBD feature data is compared with the process feature mapping information knowledge base by index comparison. The process feature information storage structure is shown in Figure 3 For example, the feature comparison method is as follows: if the MBD feature data is "Q235" or "Q345", it means that the process feature of "carbon structural steel" exists;

[0077] Step S02.3, the process features of the parts are obtained and distinguished according to important process features and general process features. For example, after comparing and mapping the MBD of the main wing rear beam frame part of BCA-65B98765-003 with the knowledge base, 6 process features are obtained, of which "frame parts", "aluminum alloy", and "machining materials" are important process features and are recorded as { 、 、 The three process characteristics of "double annealing", "sulfuric acid anodizing" and "non-destructive testing required" are general process characteristics. 、 、 These six process features need to be associated with the BCA-65B98765-003 main wing rear beam frame part.

[0078] Step S03: Process feature comparison stage: Perform weighted distance measurement comparison based on the part process feature information and the process knowledge base with process feature information identification to obtain the initial process instruction file with high similarity. According to the process feature identification of BCA-65B98765-003, the process instruction file features with these 6 process features in the knowledge base are extracted, and the authority value of each process feature is calculated. Finally, the weighted Euclidean similarity algorithm is used to calculate the part similarity. Figure 4 The specific implementation process is as follows:

[0079] Step S03.1, based on the process characteristic parameters of the part, calling a knowledge base with process instruction files constructed by group technology, and obtaining a sequence of process instruction files related to the process characteristic parameters in the knowledge base;

[0080] Step S03.2, calculating the weighted parameters required for each process feature of the part;

[0081] Step S03.3, combining the weight values ​​of “important process characteristics” and “general process characteristics”;

[0082] Step S03.4, using a weighted Euclidean similarity algorithm to calculate part similarity and match the process instruction file with the closest process characteristics;

[0083] Step S03.5: Summarize and output the best matching and similar matching process instruction files.

[0084] Step S03.1: The knowledge base extracts and records the process feature files containing the part, including:

[0085] Step S03.1.1: Obtain the important process feature sequence of the part and general process feature sequences ;

[0086] Step S03.1.2: Extract the process instruction file sequences related to important process feature sequences and general process features from the knowledge base with process instruction files constructed by group technology ;

[0087] Step S03.1.3: Integrate into three process sequences 、 and Output as a parameter to step S03.2.

[0088] For example, when comparing process characteristics in the knowledge base, the six benchmark process characteristics defined for the BCA-65B98765-003 product model are used as reference standards. Through knowledge base search, five documents covering the above process characteristic indicators are identified. The hit status of each process characteristic in the knowledge base is shown in Table 1 below:

[0089] Table 1

[0090] If the process features of the process files in the knowledge base are not matched, they will not be counted in the statistics in the above table.

[0091] Step S03.2 also includes:

[0092] Step S03.2.1, set the P value of the overall important process feature ratio of the part (here it represents the proportion of important process features to the overall weight, ), general process characteristics are automatically recorded as 1-P, the P value is given by experts, and the default P value is 0.4.

[0093] Step S03.2.2, calculate the weight of important process features using the discrimination weight method; calculate the mean of important features and standard deviation ; Calculate the weights of important features ;include:

[0094] Step S03.2.2.1: Sequence according to important process characteristics In the process instruction file sequence In the case of a hit, the hit process feature is recorded as 1, and the hit process feature is recorded as 0, which is converted into an important process feature parameter sequence and recorded as ;

[0095] Step S03.2.2.2: Calculate the mean hit value of each important process feature of the part and standard deviation .in (N represents the total number of samples in the knowledge base; n represents the number of samples where process features are found in the knowledge base). ;

[0096] For example, the important process characteristics calculated according to BCA-65B98765-003 are recorded as { 、 、 } and its knowledge base process feature hits, the important feature means and standard deviations can be summarized in Table 2:

[0097] Table 2

[0098] Step S03.2.2.3: Calculate the weights of important process characteristics using The formula calculates the weight value of each important process feature to form a sequence (Indicates the important process feature weights).

[0099] For example, according to BCA-65B98765-003, { 、 、 The standard deviation of the feature can be calculated = 0.447+ 0.548 + 0.548 = 1.543, 1.57%, similarly we get 4.18%.

[0100] Step S03.2.3, calculate the weight of general process characteristics using the entropy weight method; calculate the probability of general characteristics and entropy ; Calculate the weight of general features ;include:

[0101] Step S03.2.3.1: According to the general process characteristics sequence In the process instruction file sequence In the hit situation, the number of hits of general process features is recorded as ;

[0102] Step S03.2.3.2: Calculate the hit probability of each general process feature of the part and entropy The probability of general process characteristics is expressed as Calculation, entropy value is used calculate;

[0103] For example, the important process characteristics calculated according to BCA-65B98765-003 are recorded as { 、 、 } and its knowledge base process feature hits, the general feature hit probability and entropy value can be summarized as shown in Table 3:

[0104] Table 3

[0105] Step S03.2.3.3: Calculate the general process feature weights using The formula calculates the weight value of each general process feature to form a sequence .

[0106] For example, according to BCA-65B98765-003, { 、 、 } Entropy value, the difference coefficient can be calculated to be 0.029. .

[0107] Step S03.3, combine the weight values ​​of “important process characteristics” and “general process characteristics” to calculate and The sequences are summarized together. For example, the weight values ​​calculated for BCA-65B98765-003 are as follows in Table 4:

[0108] Table 4

[0109] Step S03.4, using the similarity formula Calculate the similarity between each sample data and the target data to obtain the best and similar process files. For example, the weighted Euclidean similarity of the five samples related to the BCA-65B98765-003 component is:

[0110] Sample 1 (Hit 、 、 ): .271;

[0111] Sample 2 (Hit 、 、 ): .283;

[0112] Sample 3 (Hit 、 、 ): .271;

[0113] Sample 4 (Hit 、 、 、 、 ): .418;

[0114] Sample 5 (Hit 、 、 ): .306;

[0115] Step S03.5, based on the similarity value The best matching process file is pulled from the knowledge base and the corresponding physical file is sent to the process personnel. For example, the best match for BCA-65B98765-003 is sample 4 (the best match), and sample 5 is output as a similar sample (and the second best match).

[0116] Step S04: Evaluation, Optimization, and Matching: Conduct a manufacturing feasibility assessment. If the assessment passes, key parameters are verified, and other non-critical descriptive information is optimized. If the assessment fails, feedback is provided about the matching environment, and a second match is performed after eliminating the selected answer. After obtaining the output of the process file, process personnel must conduct a process feasibility assessment based on the actual production line to determine whether the solution is compatible with the current production system. If the assessment confirms that it meets production requirements, the core process parameters should be systematically verified, and auxiliary process elements should be improved simultaneously. If there are production compatibility issues, it is necessary to return to the step S03 node and re-perform the optimal matching operation.

[0117] The specific implementation process is as follows:

[0118] Step S04.1, obtaining the best and similar matching files, retrieving and initializing the files from the process instruction knowledge base to form an initial process instruction file;

[0119] Step S04.2, conduct manufacturability assessment;

[0120] Step S04.3: proofread and revise key parameters and file contents, and process personnel interactively review the final results and publish the revised process instruction file.

[0121] In step S04.1, the process file is retrieved from the process instruction knowledge base in the form of a complete process instruction file, which can be previewed and reviewed by process personnel;

[0122] In step S04.2, a manufacturing type assessment is carried out. If the assessment passes, step S04.3 is entered. If it fails, the best matching result is obtained, the matching is deleted from the knowledge base sample, and step S03 is entered to recalculate.

[0123] In step S04.3, process engineers conduct a technical review and secondary revision of the system-recommended complete process instruction document, focusing on the compliance of critical process parameters (CPPs) and the semantic rigor of non-critical process descriptions. Once the revisions are complete, they follow a standardized release approval process based on quality management system requirements. After multi-level review and confirmation, the technical document is iterated and officially implemented.

[0124] The MBD information extraction, process feature acquisition, and similarity calculation processes involved in this invention are completely automated by a computer backend, requiring no human intervention. Compared to traditional methods, this method offers improved accuracy. In manufacturing companies, since the knowledge base already exists, the present invention can better utilize existing resources, requiring only minor modifications. As the company's knowledge base continues to expand, its accuracy will further improve. Furthermore, the present invention helps reduce individual differences in process design and enhance process stability.

[0125] In a second aspect, the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned process-assisted rapid compilation method based on grouping technology and MBD model.

[0126] In a third aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned process-assisted rapid compilation method based on grouping technology and MBD model.

[0127] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A process-assisted rapid compilation method based on group technology and MBD model, characterized in that: include: Step S01, data preparation stage: obtain the MBD model and convert it into a data format that can be recognized and parsed; Step S02, feature information acquisition stage: parsing the MBD data model, including structured annotation information and unstructured annotation information, and mapping the process feature information contained in the part based on the parsed information; Step S03, process feature comparison stage: performing weighted distance measurement comparison on the process feature information contained in the part and the process instruction file knowledge base with process feature information identification to obtain an initial process instruction file with high similarity; Step S04, evaluation, optimization and matching stage: conducting a manufacturing feasibility evaluation on the initial process instruction file. If the evaluation passes, a revised process instruction file is issued after verification; If the evaluation fails, step S03 is executed again.

2. The process-assisted rapid compilation method based on group technology and MBD model according to claim 1 is characterized in that: Step S01 includes: Step S01.1, obtain the design MBD digital model file and related part basic information through the API interface; Step S01.2: Convert the designed MBD digital model file into a neutral format .step file.

3. The process-assisted rapid compilation method based on group technology and MBD model according to claim 1 is characterized in that: Step S02 includes: Step S02.1, parse the .step file to obtain MBD feature data; Step S02.2: Compare the obtained MBD feature data and basic part information with the process feature mapping information knowledge base to form a process feature information sequence that matches the part. The process feature information sequence is divided into "important process features" and "general process features"; Step S02.3: Establish and store the link relationship between the part and the process feature information sequence.

4. The process-assisted rapid compilation method based on group technology and MBD model according to claim 1 is characterized in that: Step S03 includes: Step S03.1, based on the process characteristics of the part, call the knowledge base with process instruction files built by group technology to obtain the process instruction file sequence related to the process characteristic parameters in the knowledge base; Step S03.2, calculating the weighted parameters required for each process feature of the part; Step S03.3, combining the weight values ​​of "important process characteristics" and "general process characteristics"; Step S03.4, using the weighted Euclidean similarity algorithm to calculate the similarity of parts; Step S03.5, sorting by similarity from high to low, and outputting the best matching and sub-best matching process instruction files.

5. The process-assisted rapid compilation method based on group technology and MBD model according to claim 1 is characterized in that: S04 includes: S04.1, based on the optimal matching and suboptimal matching process instruction files, retrieve and initialize corresponding files from a process instruction knowledge base to form an initial process instruction file; S04.2, conduct a manufacturing feasibility assessment; S04.3, if the assessment is passed, key parameters and document contents shall be proofread and revised, and the process personnel shall interactively review the final results and publish the revised process instruction documents.

6. The process-assisted rapid compilation method based on group technology and MBD model according to claim 3 is characterized in that: The important process characteristics indicate the part name, part type, and material, and the general process characteristics indicate heat treatment requirements, surface treatment requirements, and testing requirements.

7. The process-assisted rapid compilation method based on group technology and MBD model according to claim 4 is characterized in that: Step S03.1 includes: Step S03.1.1: Obtain the important process feature sequence and general process feature sequence of the part; Step S03.1.2: extracting process instruction file sequences related to important process feature sequences and general process features from the knowledge base with process instruction files constructed by group technology; Step S03.1.3: Integrate step S03.1.1 and step S03.1.2 into three process sequences and output them as parameters to step S03.

2.

8. The process-assisted rapid compilation method based on group technology and MBD model according to claim 4 is characterized in that: Step S03.2 includes: Step S03.2.1: Set the probability values ​​of the important process characteristics and general process characteristics of the part to P and 1-P respectively; Step S03.2.2: Calculate the weights of important process features using the discrimination weight method; Step S03.2.3: Calculate the weights of conventional general process characteristics using the entropy weight method.

9. The process-assisted rapid compilation method based on group technology and MBD model according to claim 8 is characterized in that: The step S03.2.2 includes: Step S03.2.2.1: Count the hits of important process feature sequences in the process instruction file sequence, with hit process features recorded as 1 and miss process features recorded as 0; Step S03.2.2.2: Calculate the mean hit value of each important process feature of the part and standard deviation ; Step S03.2.2.3: Calculate the weights of important process characteristics using the formula Calculate the weight value of each important process feature to form an important process feature weight sequence .

10. The process-assisted rapid compilation method based on group technology and MBD model according to claim 8, characterized in that: The step S03.2.3 includes: Step S03.2.3.1: Count the number of times the general process feature sequence hits the process instruction file sequence; Step S03.2.3.2: Calculate the hit probability of each general process feature of the part based on the number of hits and entropy ; Step S03.2.3.3: Calculate the general process characteristic weights using the formula Calculate the weight value of each general process feature to form a general process feature weight sequence .

11. The process-assisted rapid compilation method based on group technology and MBD model according to claim 8, characterized in that: In step S03.4, the similarity value is calculated by the following formula: ， Where, Represents the total number of important process features, Represents the total number of general process characteristics.

12. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the process-assisted rapid compilation method based on group technology and MBD model as described in any one of claims 1 to 11.

13. A computer-readable storage medium, characterized in that Executable instructions are stored thereon, and when the instructions are executed by the processor, the processor can implement the process-assisted rapid compilation method based on group technology and MBD model as described in any one of claims 1-11.

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