Intelligent evaluation method, system and computer-readable storage medium for automotive product data maturity based on PLM platform

Abstract

The present invention discloses a method, system and computer-readable storage medium for intelligent assessment of automotive product data maturity based on a PLM platform. The method comprises constructing a bottom-level part product data structure model in a PDM system, determining a bottom-level part maturity assessment extraction content matrix, establishing a PDM system database, building a single virtual vehicle in the PDM system for product data maturity assessment, conducting a design data maturity assessment on all parts in the vehicle parts list one by one, and outputting the maturity assessment results to the HOME folder of the PDM system; after rectification, parts that do not meet the standards are re-evaluated for design data maturity until the vehicle product data maturity meets the standards. The present invention can quickly output a list of data maturity assessment results for vehicle parts by intelligently assessing product data maturity, thereby realizing intelligent assessment of vehicle product data maturity.

Description

Technical Field

[0001] The present invention relates to an automobile product evaluation method and system, and in particular to an automobile new product project delivery data stage maturity evaluation method and system based on an enterprise PLM platform. Background Art

[0002] Product quality is the lifeblood of any enterprise and fundamental to the success of new automotive product development projects. Ensuring that product data quality meets established targets at every stage of automotive product development (planning, engineering, manufacturing, and launch) is crucial to ensuring product quality. Therefore, data maturity testing at each stage of a new automotive product development project is a crucial component of new automotive product development project management. Continuously improving the accuracy and efficiency of data maturity testing is crucial to the success of new automotive product project implementation.

[0003] In recent years, information technology, a key driver and cornerstone of the information society and knowledge economy, has experienced rapid development. It has become an essential production factor for manufacturers as they pursue "rationalization processes" and respond to new challenges. Implementing comprehensive planning, gradually establishing an enterprise product data management platform, and implementing product lifecycle data management (PLM) and information integration with PDM (Product Data Management) as the core has become an essential technical means for automotive manufacturers to enhance their core competitiveness.

[0004] The management technology for vehicle development product data differs significantly from that for other products due to two typical aspects of automotive product data information. These two typical aspects are:

[0005] (1) There are many types and a large amount of information. There are many types of cars. Each car is made up of more than 5,000 parts, and each part contains a variety of information, including product engineering part information, data model information, drawing information, and project part tracking information;

[0006] (2) Information changes have obvious stages.

[0007] These typical characteristics of automotive product data require not only highly adaptable product data management structures and models that can accommodate complex and changing data, as well as rapidly evolving design patterns, but also require that automotive product data maturity assessments be comprehensive evaluations of multi-dimensional information. Rapidly and efficiently assessing the maturity of complex automotive products presents a technical challenge. Summary of the Invention

[0008] The purpose of the present invention is to provide an intelligent assessment method and system for automotive product data maturity based on the PLM platform, so as to achieve rapid judgment of the maturity stage of project delivery data and improve the accuracy and process efficiency of project management during the new car development process.

[0009] Another object of the present invention is to provide a computer-readable storage medium storing computer-executable instructions for a computer to execute the method for intelligently evaluating the maturity of automotive product data.

[0010] To achieve the above object, the present invention adopts the following technical solutions:

[0011] The present invention provides an intelligent evaluation method for automotive product data maturity based on a PLM platform, comprising the following steps:

[0012] S1. Construct the underlying parts product data structure model in the PDM system;

[0013] S2. Determine the underlying component maturity evaluation extraction content matrix based on the design approach and the definition of the relationship between component maturity and version;

[0014] S3. Establish a PDM system database to store the automotive product data maturity level and part version correspondence table, maturity level assignment table, and single part maturity calculation result comparison table;

[0015] S4. Creating a virtual engineering data top layer in the PDM system, storing design data of all parts in the configured vehicle parts list in the virtual engineering data top layer, and constructing a single virtual vehicle for which product data maturity assessment is to be conducted;

[0016] S5. Perform data extraction on a single virtual vehicle, conduct design data maturity assessment on all parts in the vehicle parts list one by one, and output the maturity assessment results to the HOME folder of the PDM system; after rectification, the design data maturity assessment of non-compliant parts will be re-evaluated until the vehicle product data maturity meets the standards.

[0017] Furthermore, the underlying part data structure model includes the part ID and version of the overview type defined for each part in the PDM system. Under the part version of the overview type, several pseudo-folders and a text document are constructed according to the part design data type. Each type of pseudo-folder contains a data version of the corresponding type, and the design data set of this type is stored under the data version; the text document contains the configuration information of each vehicle model of the overview type part of this version.

[0018] The part design data types include type 1-3D, type 2-2D, type 3-NOTE, and type 4-development history.

[0019] Furthermore, the bottom-level component maturity assessment extraction content matrix is:

[0020]

[0021] (i=1,2,…,m; j=1,2,…,n)

[0022] Among them, s is the evaluable coefficient;

[0023]

[0024] Matrix B=(b ik ) is a 5×4 design method coefficient matrix, where the rows represent the different stages of the car company's R&D design methods; the columns represent the four categories of product data values ​​of parts: Type 1-3D, Type 2-2D, Type 3-NOTE, and Type 4-Development History;

[0025]

[0026] Matrix C=(c kj ) is a 4×2 data content matrix, the rows represent the four categories of product data values ​​of parts: Type 1-3D, Type 2-2D, Type 3-NOTE, Type 4-Development History; the columns represent the maturity level content of various types of product data: the latest released version and the latest version. The latest released version represents the last maturity level that has been frozen and completed the professional review, and the latest version represents the maturity level of the part currently being edited.

[0027] Furthermore, the value of the evaluability coefficient s is 0 or 1, wherein when s=0, it indicates that the configuration information of the part's superstructure in the vehicle is empty and the product data maturity cannot be evaluated; when s=1, the configuration information of the part's superstructure in the vehicle is not empty and the product data maturity can be evaluated;

[0028] b ik It takes 0 or 1, and is assigned different values ​​according to the different R&D and design methods of the car companies.

[0029] When the design method is simple two-dimensional drawing, b 11 =0,b 12 =1,b 13 =0,b 14 =1;

[0030] When the design method is two-dimensional drawing + supplementary information, then b21 = 0, b22 = 1, b23 = 1, b24 = 1;

[0031] When the design method is simple three-dimensional drawing, b31=1, b32=0, b33=0, b34=1;

[0032] When the design method is 3D mapping + 2D mapping, then b41 = 1, b42 = 1, b43 = 0, b44 = 1;

[0033] When the design means is three-dimensional drawing + supplementary information, b51=1, b52=0, b53=1, b54=1.

[0034] Furthermore, the maturity level and part version correspondence table is presented based on the general data maturity level of automobile products and the version meaning and status identification in the PDM system. The general maturity level is divided into four levels: M0, M1, M2, and M3, and M3>M2>M1>M0. The subdivided version corresponding to each level and the maturity meaning of a certain type of data assigned to the version are defined by the enterprise; the maturity level assignment table is a maturity score query table for the four types of data from M0 to M3 levels set by the assignment method; the maturity calculation result comparison table of a single part is a table of the maturity assessment level, score and conclusion of the vehicle model product data calculated.

[0035] Furthermore, the maturity level assignment table has three assignment columns, wherein the first assignment column is the primary design means assignment column, the second assignment column is the secondary design means assignment column, and the third assignment column is the development history assignment column. In order to avoid repeated scores in the assignment weighted score interval and increase the discreteness of the score distribution, each level of maturity assignment adds a compensation score to the original weighted score. The compensation score is the weighted maximum value of the maturity assignment of the previous level. According to the maturity level order of M3>M2>M1>M0, the assignment of each level of maturity comprehensively compensates for the maximum value of the previous level.

[0036] Furthermore, the product data maturity score calculation formula for a single part is:

[0037]

[0038] in:

[0039] α is the weight coefficient of the first assigned column, which is 1. x is the analysis result score of the first assigned column's underlying component maturity assessment extracted content matrix A.

[0040] δ is the coefficient of the second assignment column, which takes a value of 0 or 1, where 0 indicates no secondary design means and 1 indicates the presence of secondary design means; β is the weight coefficient of the second assignment column, which takes a value of 1, and y is the analysis result score of the bottom-level component maturity assessment extraction content matrix A of the second assignment column;

[0041] γ is the weight coefficient of the third assignment column, which is 1, and z is the analysis result score of the underlying part maturity assessment extraction content matrix A of the third assignment column.

[0042] The present invention provides an intelligent assessment system for automotive product data maturity based on a PLM platform, comprising a PDM system. The system integrates a complete vehicle product data collaborative design module, a product data structure management module, and a PDM system structure manager module, and further comprises:

[0043] A PDM system database module is used to store the automotive product data maturity classification and part version correspondence table, maturity level assignment table, and single part maturity calculation result comparison table;

[0044] The top-level module of virtual engineering data is used to store the design data of all parts in the configured vehicle parts list, and to build a single virtual vehicle for product data maturity assessment;

[0045] The data processing module is used to perform data processing and calculation in the intelligent assessment method of automotive product data maturity based on the PLM platform.

[0046] The present invention provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed by a processor, the device where the storage medium is located executes an intelligent evaluation method for automotive product data maturity based on a PLM platform.

[0047] The present invention can quickly and accurately lock in design data defects and determine the data maturity of the entire project stage through intelligent evaluation of product data maturity, so as to facilitate the revision of digital models and the review of vehicle progress. It not only improves the accuracy of product design, but also provides support for transparent monitoring and management of vehicle development, ensures data consistency, and realizes top-down fully transparent monitoring of single data. The present invention adopts a highly adaptable 3D+2D+NOTE+resume model without a main body structure to construct an underlying part product data structure model that integrates part ID, type, version, structure and attributes. It is graded according to the general growth stage of automotive product data maturity, and is comprehensively analyzed using the matrix analysis method. The data maturity score interval for each level is set by the assignment method, and a single part product data maturity calculation method is provided. The present invention can quickly output a list of data maturity assessment results for vehicle parts by simply setting the main database of the enterprise's PLM platform and the structure manager module of the PDM system, thereby realizing intelligent evaluation of vehicle product data maturity. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of the automotive product data maturity intelligent assessment system based on the PLM platform according to the present invention;

[0049] Figure 2 Schematic diagram of the structural model of the bottom-level part product data in the PDM system of the present invention;

[0050] Figure 3 This is a flow chart of the intelligent assessment method for automotive product data maturity based on the PLM platform described in the present invention. DETAILED DESCRIPTION

[0051] like Figure 2 As shown, the automotive product data maturity intelligent assessment system based on the enterprise PLM platform described in the present invention refers to an enterprise-level PDM system that integrates 3D / 2D design software and enterprise BOM management software and includes a complete vehicle product data collaborative design module, a product data structure management module, and a PDM system structure manager module. The complete vehicle product data collaborative design module includes the online multi-site collaborative design, product electronic review, release, and change functions of the 3D / 2D design software integrated in the PDM system. After the complete vehicle product is designed online at the customer terminal, the complete vehicle product design, complete vehicle product EBOM data and configuration information are imported through the integrated interface of the PDM system, as well as the latest changes to the imported data. The product data structure management module includes the design, release, and change functions of other technical documents of the product other than 3D / 2D data maintained in the PDM system. The PDM system structure manager module is a product structure management module inherent in the PDM system. After the complete vehicle product data is saved in the PDM system through the integrated complete vehicle product data collaborative design module and product data structure management module, virtual evaluation vehicle model construction and structure management can be performed in the structure manager. Several modules can work together without interfering with each other.

[0052] In order to implement the automotive product data maturity intelligent assessment method constructed based on the enterprise PLM platform of the present invention, a PDM system database module is also constructed in the PDM system, which is used to store the automotive product data maturity classification and part version correspondence table, maturity level assignment table and single part maturity calculation result comparison table; a virtual engineering data top-level module is used to store the design data of all parts in the configured vehicle parts list and build a single virtual vehicle for product data maturity assessment; and a data processing module is used to perform data processing and calculation in the automotive product data maturity intelligent assessment method based on the PLM platform described below.

[0053] like Figure 3 As shown, the automotive product data maturity intelligent assessment method based on the PLM platform of the present invention includes the following steps:

[0054] S1. Based on the above-mentioned integrated modules of the PDM system, a bottom-level component product data structure model is constructed in the PDM system. The bottom-level component product data structure model is as shown in the attached figure. Figure 2As shown. The underlying part structure model includes the ID and version of the overview type defined in the PDM system for the part. Several pseudo-folders and a text document are constructed under the overview type part version. The pseudo-folders correspond to four types of part data information sets: 3D, 2D, NOTE (i.e., supplementary information), and development history. Each type of pseudo-folder contains a corresponding type of data version, and this type of data set is stored under this type of data version. The text document contains the configuration information of each vehicle model for the overview type part of this version.

[0055] S2. Determine the underlying part maturity assessment extraction content matrix A based on the company's design methods at this stage and the definition of the relationship between part maturity and version.

[0056]

[0057] (i=1,2,…,m; j=1,2,…,n)

[0058] Matrix B=(b ik ) is a 5×4 design means coefficient matrix, and the rows represent the different R&D design means of automobile companies.

[0059]

[0060] At the same stage, the columns represent the four categories of product data values ​​for part A: Type 1-3D, Type 2-2D, Type 3-NOTE, and Type 4-Resume.

[0061]

[0062] Matrix C=(c kj ) is a 4×2 data content matrix, which is the core content for comprehensively judging the maturity extraction of component product data in the whole vehicle structure, mainly consisting of two contents: the latest released version and the latest version. The rows represent the four categories of product data values ​​of part A: Type 1-3D, Type 2-2D, Type 3-NOTE, Type 4-Resume; the columns represent the maturity level content of various types of product data: the latest released version, the latest version. Among them, the version in the PDM system is strongly related to the maturity level requirements of the whole vehicle product data. The bottom-level part data maturity extraction content covers the product data maturity level version number and review identifier that have completed the review of various types of professional parts. The latest released version number of a certain type of data represents the last maturity level of the currently frozen professional review; the latest version number of a certain type of data represents the maturity level of the part currently being edited. Whether the latest version number is equal to the latest released version number is an important basis for judging whether the data is truly frozen.

[0063] Where s is the evaluability coefficient, which takes 0 or 1. When the configuration information of the part's superstructure in the whole vehicle is empty, s = 0, and the product data maturity cannot be evaluated; when the configuration information of the part's superstructure in the whole vehicle is not empty, s = 1, and the A value is extracted.

[0064] b in the design means coefficient matrix B ik The value is 0 or 1. The values ​​of Type 1-3D, Type 2-2D, Type 3-NOTE, and Type 4-Development History vary according to the different stages of the R&D and design methods of the car company.

[0065] If the design method is simple two-dimensional drawing, then b 11 =0,b 12 =1,b 13 =0,b 14 =1;

[0066] The design method is two-dimensional mapping + supplementary information, then b 21 =0,b 22 =1,b 23 =1,b 24 =1;

[0067] If the design method is simple three-dimensional drawing, then b 31 =1,b 32 =0,b 33 =0,b 34 =1;

[0068] The design method is 3D mapping + 2D mapping, then b 41 =1,b 42 =1,b 43 =0,b 44 =1;

[0069] The design method is 3D mapping + supplementary information, then b 51 =1,b 52 =0,b 53 =1,b 54 =1;

[0070] According to the general data maturity requirements of automobile companies, the maturity of automobile products is divided into M0, M1, M2, and M3 levels. The relationship between version status and each level is clearly defined using a list-based correspondence method, as shown in Table 1.

[0071] Table 1 General maturity classification table for automotive product data

[0072]

[0073]

[0074] The above levels are M3 > M2 > M1 > M0. The development history includes the following specific contents: ① Configuration difference information; ② Specification difference information (shape / structure / size / material / installation method / paint / weight / component composition, etc.); ③ Functional difference information (function / performance / regulatory compliance / quality compliance, etc.); ④ Functional / performance requirements (vehicle performance breakdown, component performance requirements); ⑤ Regulatory requirements, component design and verification standards; ⑥ Component matching, installation, and assembly strategies; ⑦ Competitive product benchmarking; ⑧ Commonality description; ⑨ Lightweight description.

[0075] S3. Establish a PDM system database to store the automotive product data maturity classification and part version correspondence table, maturity level assignment table, and single part maturity calculation result comparison table.

[0076] The table of correspondence between maturity levels and part versions is based on the general maturity level for automotive product data and the meaning and status identifiers of versions in the PDM system. The general maturity levels are divided into four levels: M0, M1, M2, and M3. The corresponding sub-versions and the maturity of the data type assigned to each level are defined by the company. The more detailed the maturity level sub-levels, the more detailed the versions and version identifiers assigned. This can be expanded indefinitely.

[0077] The maturity level assignment table is a lookup table of maturity scores for four types of data from M0 to M3, which are set by the assignment method. Table 2 is an assignment table with coefficient matrix values ​​of 1, 1, 0, and 1.

[0078] From the design means phase coefficient matrix logic of step S2, we know that only two of type 1, type 2, and type 3 can appear at the same time, and there can only be one primary design means, which is set as assignment column 1. When two types appear at the same time, the secondary design means column is set as assignment column 2. Type 4 is a fixed occurrence item and is set as assignment column 3. The maximum number of assignment columns is 3. Therefore, the corresponding relationship between the design means coefficient matrix and the assignment is:

[0079]

[0080] The following example uses coefficients of 1, 1, 0, and 1 to assign data maturity of three types to a single part as shown in Table 2:

[0081] Table 2 Single part data maturity assignment table

[0082]

[0083]

[0084] According to the maturity level order, M3 > M2 > M1 > M0. Each maturity level's value is comprehensively compensated for the maximum value of the previous level: the compensation value for M1 is 40, the compensation value for M2 is 120, and the compensation value for M3 is 200. To avoid duplicate scores in the assigned weighted score interval and increase the dispersion of the score distribution, the compensation score for each maturity level is added to the original weighted score. The compensation score is the weighted maximum value of the maturity value assigned to the previous level.

[0085] As can be seen from the table above, the analysis results of the extracted value A of a single part with maturity levels M0, M1, M2, and M3 are presented in 13 states:

[0086] Among them, the analysis results of the single part extraction value A of the result maturity level M0 are divided into four result distributions: the latest released version number is empty, the latest released version = a certain version of the M0 level, the latest released version = a certain version of the M0 level and the latest version, the latest released version = a certain version of the M0 level and the version number < the latest version number;

[0087] The analysis results of the extracted value A of a single part at maturity level M1 are distributed into three types: the latest released version = a certain version at M1 level, the latest released version = a certain version at M1 level and the latest version, and the latest released version = a certain version at M1 level and the version number < the latest version number;

[0088] The analysis results of the extracted value A of a single part at maturity level M2 are distributed into three types: the latest released version = a certain version at M2 level, the latest released version = a certain version at M2 level and the latest version, and the latest released version = a certain version at M2 level and the version number < the latest version number;

[0089] The analysis results of the single part extraction value A at maturity level M3 are distributed into three types: the latest released version = a certain version of M3 level, the latest released version = a certain version of M3 level and the latest version, and the latest released version = a certain version of M3 level and the version number < the latest version number.

[0090] The formula for calculating the product data maturity score of a single part is as follows:

[0091]

[0092] in:

[0093] α is the weight coefficient of column 1, which is generally set to 1, and x is the score of the A value analysis result of column 1;

[0094] δ is the value coefficient of column 2, which is 0 or 1, 0 means no secondary design means, and 1 means there is a secondary design means;

[0095] β is the weight coefficient of column 2, which is generally set to 1, and y is the score of the A value analysis result of column 2;

[0096] γ is the weight coefficient of column 3, which is generally set to 1, and z is the score of the A value analysis result of column 3;

[0097] The single part maturity calculation result comparison table is a table that uses the single part product data maturity calculation formula to obtain the vehicle model product data maturity assessment level, score and conclusion.

[0098] Table 3 Comparison table of single part maturity calculation results

[0099] Rating range Maturity Results 0 The maturity level of this part is <M0 10～40 The maturity level of this part is M0, and the maturity score is E, which means it does not meet the requirements for upgrading. 40 The maturity level of this part is M0, and the maturity score is 40, which means it has the conditions for upgrading. 70～120 The maturity level of this part is M1, and the maturity score is E, which means it does not meet the requirements for upgrading. 120 The maturity level of this part is M1, and the maturity score is 120, which means it has met the requirements for upgrading. 170～260 The maturity level of this part is M2, and the maturity score is E. It does not meet the conditions for upgrading. 260 The maturity level of this part is M2, and the maturity score is 260, which means it has met the requirements for upgrading. 330～560 The maturity level of this part is M3, and the maturity score is E, which means it does not meet the conditions for upgrading. 560 The maturity level of this part is M3, and the maturity score is 560, which means it has met the freezing conditions.

[0100] S4. Build a single virtual vehicle for product data maturity assessment. Create a new virtual engineering data top layer in the PDM system. After sending this virtual engineering data top layer to the PDM system structure manager module, query the configured vehicle parts list in the PDM system's integrated product data structure management module by part ID and overview type. Copy and paste this list into the virtual engineering data top layer to complete the construction of the single virtual vehicle.

[0101] S5. Perform data extraction on a single virtual vehicle, conduct design data maturity assessment on all parts in the vehicle parts list one by one, and output the maturity assessment results to the HOME folder of the PDM system; after rectification, the design data maturity assessment of non-compliant parts will be re-evaluated until the vehicle product data maturity meets the standards.

Claims

1. An intelligent evaluation method for automotive product data maturity based on a PLM platform, characterized by: The steps include: S1. Construct the underlying parts product data structure model in the PDM system; S2. Determine the underlying component maturity evaluation extraction content matrix based on the design approach and the definition of the relationship between component maturity and version; The bottom-level component maturity assessment extraction content matrix is: Where i = 1, 2, ..., m; j = 1, 2, ..., n, m is the total number of types of design methods, which is a positive integer greater than 1, and n is the total number of versions related to the automotive product data maturity level assessment, which is a positive integer greater than 1. Among them, s is the evaluable coefficient; Matrix B=(b ik ) is a 5×4 design method coefficient matrix, where the rows represent the different stages of the car company's R&D design methods; the columns represent the four categories of product data values ​​of parts: Type 1-3D, Type 2-2D, Type 3-NOTE, and Type 4-Development History; Matrix C=(c kj ) is a 4×2 data content matrix, where the rows represent the four categories of product data values ​​for parts: Type 1-3D, Type 2-2D, Type 3-NOTE, and Type 4-Development History; the columns represent the maturity levels of various types of product data: the latest released version and the latest version. The latest released version represents the last maturity level that has been frozen and completed professional review, and the latest version represents the maturity level of the part currently being edited. S3. Establish a PDM system database to store the automotive product data maturity level and part version correspondence table, maturity level assignment table, and single part maturity calculation result comparison table; S4. Creating a virtual engineering data top layer in the PDM system, storing design data of all parts in the configured vehicle parts list in the virtual engineering data top layer, and constructing a single virtual vehicle for which product data maturity assessment is to be conducted; S5. Perform data extraction on the virtual vehicle, conduct design data maturity assessment on all parts in the vehicle parts list one by one, and output the maturity assessment results to the HOME folder of the PDM system; after rectification, the design data maturity assessment of non-compliant parts will be re-performed until the vehicle product data maturity meets the standards.

2. The intelligent evaluation method for automotive product data maturity based on the PLM platform according to claim 1 is characterized in that: The underlying part data structure model includes the part ID and version of the overview type defined for each part in the PDM system. Under the overview type part version, several pseudo-folders and a text document are constructed according to the part design data type. Each type of pseudo-folder contains a data version of the corresponding type, and the design data set of this type is stored under the data version; the text document contains the configuration information of each vehicle model of the overview type part of this version.

3. The intelligent evaluation method for automotive product data maturity based on the PLM platform according to claim 2 is characterized in that: The value of the evaluation coefficient s is 0 or 1, wherein when s=0, it means that the configuration information of the part's superstructure in the whole vehicle is empty and the product data maturity cannot be evaluated; when s=1, it means that the configuration information of the part's superstructure in the whole vehicle is not empty and the product data maturity can be evaluated; b ik It takes 0 or 1, and is assigned different values ​​according to the different R&D and design methods of the car companies. When the design method is simple two-dimensional drawing, b 11 =0,b 12 =1,b 13 =0,b 14 =1; When the design method is two-dimensional mapping + supplementary information, b 21 =0,b 22 =1,b 23 =1,b 24 =1; When the design method is simple three-dimensional drawing, b 31 =1,b 32 =0,b 33 =0,b 34 =1; When the design method is 3D mapping + 2D mapping, then b 41 =1,b 42 =1,b 43 =0,b 44 =1; When the design method is 3D mapping + supplementary information, b 51 =1,b 52 =0,b 53 =1,b 54 =1.

4. The method for intelligently evaluating automotive product data maturity based on a PLM platform according to claim 3 is characterized in that: The maturity level and part version correspondence table is presented based on the general data maturity level of automobile products and the version meaning and status identification in the PDM system. The general data maturity level is divided into four levels: M0, M1, M2, and M3, and M3>M2>M1>M0. The subdivided version corresponding to each level and the maturity meaning of a certain type of data assigned to the version are defined by the enterprise; the maturity level assignment table is a maturity score query table for the four types of data from M0 to M3 levels set by the assignment method; the single part maturity calculation result comparison table is a table of the maturity assessment level, score and conclusion of the vehicle model product data calculated.

5. The intelligent evaluation method for automotive product data maturity based on the PLM platform according to claim 4 is characterized in that: The maturity level assignment table has three assignment columns, wherein the first assignment column is the primary design means assignment column, the second assignment column is the secondary design means assignment column, and the third assignment column is the development history assignment column. In order to avoid repeated scores in the assignment weighted score interval and increase the discreteness of the score distribution, each level of maturity assignment adds a compensation score to the original weighted score. The compensation score is the weighted maximum value of the maturity assignment of the previous level. According to the maturity level order of M3>M2>M1>M0, the assignment of each level of maturity comprehensively compensates for the maximum value of the previous level.

6. The method for intelligently evaluating automotive product data maturity based on a PLM platform according to claim 5 is characterized in that: The formula for calculating the product data maturity of a single part is: in: α is the weight coefficient of the first assigned column, which is 1. x is the analysis result score of the first assigned column's underlying component maturity assessment extracted content matrix A. δ is the coefficient of the second assignment column, which takes a value of 0 or 1, where 0 indicates no secondary design means and 1 indicates the presence of secondary design means; β is the weight coefficient of the second assignment column, which takes a value of 1, and y is the analysis result score of the bottom-level component maturity assessment extraction content matrix A of the second assignment column; γ is the weight coefficient of the third assignment column, which is 1, and z is the analysis result score of the underlying part maturity assessment extraction content matrix A of the third assignment column.

7. An automotive product data maturity intelligent assessment system based on a PLM platform, comprising a PDM system, wherein the system integrates a vehicle product data collaborative design module, a product data structure management module, and a PDM system structure manager module, characterized in that: Also includes: A PDM system database module is used to store the automotive product data maturity classification and part version correspondence table, maturity level assignment table, and single part maturity calculation result comparison table; The top-level module of virtual engineering data is used to store the design data of all parts in the configured vehicle parts list, and to build a single virtual vehicle for product data maturity assessment; A data processing module, configured to perform data processing and calculation in the method according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the device where the storage medium is located executes the method according to any one of claims 1 to 6.

Images