An Automatic Update Method for DFMEA Combining After-sales Data and Fault Tree

Abstract

The present invention discloses an automatic update method for DFMEA combining after-sales data and fault tree, including: S1, constructing a product BOM interface based on SQL database and JAVA; S2, constructing a product fault mode knowledge base interface based on JAVA and Vue according to product after-sales service data; S3, constructing a product fault tree interface according to the product BOM interface and the product fault mode knowledge base interface; S4, realizing the automatic update of the product DFMEA table. Based on the intelligent data integration of after-sales service data and product fault mode knowledge base and the automated DFMEA update mechanism based on fault tree, it can intelligently identify the key fault links of the product and automatically generate and update the DFMEA table. This method can reflect the functional characteristics and structural features of the product, and can effectively analyze the potential fault modes in the design stage of the product, so as to intelligently identify the key links that may lead to product failures.

Description

Technical Field

[0001] The present invention relates to the technical field of the reliability of manufacturing products, and particularly relates to an automatic update method for DFMEA combining after-sales data and fault trees. Background Art

[0002] The structure of manufacturing products has become more complex with their increasingly rich functions, and the functions of products produced by different manufacturing enterprises have become more and more similar year by year, which makes users pay more attention to product performance when choosing products. Since reliability is one of the important indicators for measuring the performance of manufacturing products, in recent years, manufacturers have increased their attention to product reliability, and reliability technology has become an important means to improve the performance of manufacturing products. Compared with other electronic products with simple structures, the loading methods and relative motion forms between components of manufacturing products are more complex. During their operation, they are jointly affected by mechanical stress, electrical stress and thermal stress, so the product failure modes are also more complex. Predicting and analyzing a certain potential failure mode of a product alone cannot represent the overall reliability of manufacturing products. How to predict the potential failure modes of manufacturing products through an efficient and accurate method and establish a failure mode library for them is the key to improving the reliability level of manufacturing products.

[0003] DFMEA (Design Failure Mode and Effects Analysis) is a reliability analysis method specifically for the product design stage, used to identify and evaluate potential failure modes in product design and their impacts on product performance. DFMEA is used to analyze the functions of the defined system, subsystem or related components, the relationships between their internal elements, and the relationships with external elements outside the system boundary, so as to identify possible design defects and minimize potential failure risks. FTA (Fault Tree Analysis) is a systematic reliability analysis method used to identify and analyze the root causes that may lead to product or system failures. The FTA method constructs a fault tree, decomposes the top event into a series of basic events and traces the fault occurrence path, thereby revealing the causal relationship of fault occurrence. However, in traditional DFMEA fault analysis and FTA fault attribution, it is easy to cause fuzzy and overlapping definitions and explanations of failure modes, failure causes, and failure effects, which in turn leads to incomplete analysis of potential failure modes of products. Summary of the Invention

[0004] In order to overcome the deficiencies of the above technologies, the present invention provides an automatic update method for DFMEA that combines after-sales data and fault trees. Based on the intelligent data integration of after-sales service data and the product fault mode knowledge base, as well as the automated DFMEA update mechanism based on fault trees, it can intelligently identify the key fault links of products and automatically generate and update the DFMEA table. This method can reflect the functional characteristics and structural features of products, and can effectively analyze potential fault modes during the design stage of products, thereby intelligently identifying the key links that may lead to product failures.

[0005] Term Explanation:

[0006] 1. SQL: Structured Query Language, a structured language.

[0007] 2. BOM: Bill of Materials, a list of materials.

[0008] 3. Doccano: A data annotation platform.

[0009] 4. API: Application Programming Interface, an application programming interface.

[0010] 5. HTTP: Hyper Text Transfer Protocol, a hypertext transfer protocol.

[0011] 6. NLP: Natural Language Processing, natural language processing.

[0012] 7. RPN: Risk Priority Number, a risk priority number.

[0013] 8. Axios: An HTTP client used to send asynchronous HTTP requests in JavaScript applications.

[0014] The technical solution adopted by the present invention to overcome its technical problems is:

[0015] An automatic update method for DFMEA that combines after-sales data and fault trees, including the following steps:

[0016] Step S1: Build a product BOM interface based on an SQL database and JAVA: Decompose the product into hierarchical structures according to product design requirements and similarity, clarify the assembly relationship, component functions, and the relationship between hierarchical structures; design the SQL product database table structure to record component information and relationships, and build the product BOM interface;

[0017] Step S2. Based on the product after-sales service data, build the product failure mode knowledge base interface using JAVA and Vue: Organize the product after-sales service data, classify the failure descriptions and associate them with the components in the product BOM table, create an SQL product failure mode database, and implement intelligent failure recognition through JAVA and Vue;

[0018] Step S3. Build the product fault tree interface based on the product BOM interface and the product failure mode knowledge base interface: Create the product fault tree structure in SQL according to the product BOM and the product failure mode knowledge base, so that the product fault tree is associated with the product BOM and the product failure mode knowledge base; Build a recursive query and update mechanism for the fault tree to achieve dynamic display; Use the JAVA backend to operate on the fault tree data and develop the JAVA fault tree interface;

[0019] Step S4. Implement the automatic update of the product DFMEA form: Design an SQL database for DFMEA to store DFMEA data, and achieve automatic update through dynamic interaction with the product BOM, the product failure mode knowledge base, and the product fault tree; Use the JAVA backend to synchronously update the DFMEA data so that the DFMEA data is automatically updated when the product BOM and the product failure mode change; Develop the front end based on Vue to achieve intelligent display and interaction of DFMEA.

[0020] Furthermore, step S1 specifically includes the following steps:

[0021] Step S11. Design the database of the product BOM table: First, create a product BOM table containing the basic information of the product, including at least the product ID, product model, and product description. Use the product BOM table to identify different models of products, and set the product BOM table as the core table to manage the parent product, sub-components, and hierarchical nesting relationships;

[0022] Step S12. Build the backend framework of the product BOM interface based on JAVA: Define entity classes corresponding to the product BOM database in the backend framework, and implement the business logic of querying, expanding the hierarchy, and structure updating according to the hierarchical structure of the product BOM; Write an API controller to implement adding product materials, updating product BOM relationships, and deleting product BOM structures;

[0023] Step S13. Build the front-end interface of the product BOM using the Vue framework: Achieve data communication between the front end and the back end through Axios, and the front end calls the back-end API interface through HTTP requests to implement functions of adding, deleting, modifying, and querying.

[0024] Further, in step S11, the product BOM table is used for the product management interface that displays all basic product information, including operations such as addition, deletion, modification, and query; the product BOM hierarchy is displayed in the form of a form so that users can expand and view the product hierarchy level by level and enable users to click on the current BOM data to export an Excel file.

[0025] Further, step S2 specifically includes the following steps:

[0026] Step S21: Combine the fault types that appear in the product after-sales service data, pre-define several fault mode categories, and set the pre-defined fault mode categories as tags in Doccano;

[0027] Step S22: Establish the association between the historical fault description text and the fault mode category tags: Import the historical fault description text into Doccano item by item, and through manual annotation, match each fault description text with the most relevant fault mode category tag;

[0028] Step S23: Establish the association between the historical fault description text and the components in the product BOM table: Set each product component in the product BOM table as a tag in Doccano, and through manual annotation, match each fault description text with the corresponding product component category tag;

[0029] Step S24: Create the SQL database structure of the historical fault mode data, construct the SQL database table structure of the historical fault mode that stores product components and fault modes, and import the labeled fault description text data in step S22 into the historical fault mode SQL database; when the newly input fault description text data is labeled, the new fault description text data is automatically added to the historical fault mode SQL database for real-time update of the fault mode knowledge base;

[0030] Step S25: Build the product fault mode knowledge base interface based on JAVA and Vue, use the JAVA backend framework to handle the storage and logical query of fault data, use the stored historical fault mode data, perform text analysis and probability matching on the newly input fault description text through the NLP model, identify potential fault modes, and return the most matching fault mode.

[0031] Further, step S3 specifically includes the following steps:

[0032] Step S31: Construct the SQL database table structure of the product fault tree: According to the product BOM and the historical fault mode knowledge base, construct the SQL product fault tree database structure that includes the relationship between the fault event name, fault event type, and hierarchical structure; associate the product fault tree SQL database structure with the product BOM and the product fault mode knowledge base database;

[0033] Step S32: Build a recursive query and update mechanism for the product fault tree: Build a recursive query in the product fault tree SQL database. Starting from the top-level node, recursively obtain all child nodes. When the product BOM or the data in the product fault mode knowledge base is updated, trigger the data update operation of the fault tree to synchronize the fault tree with the latest data;

[0034] Step S33: Build the backend logic and frontend interface of the fault tree: Use the JAVA backend service to handle the addition, deletion, modification, and query operations of the fault tree data, and develop a tree-structured fault tree interface based on the Vue framework;

[0035] Step S34: Automatic update of the fault tree and database linkage: When the product BOM or the product fault mode knowledge base is updated, automatically trigger the update of the fault tree SQL database. That is, when a new fault mode of a component is added, the system automatically creates a fault node for the component and updates the fault tree frontend interface.

[0036] Furthermore, in step S33, building the backend logic and frontend interface of the fault tree specifically includes:

[0037] Interact with the fault tree SQL database through the JAVA backend to handle the addition, deletion, modification, and query operations of the fault tree: Provide functions for adding, deleting, modifying, and querying fault tree nodes through the node management module, recursively obtain the structure information of the fault tree from the fault tree SQL database, and return it to the frontend in a tree-structured data format;

[0038] Develop a fault tree interface based on the Vue framework, display the fault tree nodes and hierarchical relationships through a visual tree component so that users can see the fault hierarchical structure, realize the interaction of adding, deleting, modifying, and querying nodes, and after updating the data in the fault tree SQL database, refresh the fault tree interface through a frontend asynchronous request to achieve real-time data update.

[0039] Furthermore, step S4 specifically includes the following steps:

[0040] Step S41: Build the core database structure of the DFMEA table: Design the DFMEA database structure to achieve real-time automatic update through dynamic data interaction with the product BOM, product fault mode knowledge base, and product fault tree;

[0041] Step S42: Design a product similarity algorithm: Obtain the product similarity by calculating the edit distance between the product BOM and the new product BOM; Set a similarity threshold. If the similarity between the new product BOM and the product BOM exceeds this similarity threshold, the new product can inherit the initial DFMEA table from the product;

[0042] Step S43. Design an intelligent update mechanism for the DFMEA table: Obtain the failure modes related to each component from the product failure mode knowledge base. When a component in the product BOM is newly added or modified, the system automatically updates the failure modes of this component to the DFMEA table by querying the product failure mode knowledge base; for each component in the product BOM, the system automatically updates these failure modes to the DFMEA table by looking up the associated ID; by extracting severity and frequency data from the product failure mode knowledge base, the system automatically associates this data with the corresponding fields in the DFMEA table; according to the structure of the fault tree, when the failure mode of a component affects its parent component, the system automatically raises its severity rating, and the system determines the potential impact of this failure on the system by recursively traversing the hierarchical relationship of the fault tree; according to the product fault tree, when a certain component fails, the system automatically calculates the impact path of this failure on the entire product system and updates these results to the "Failure Impact" field in the DFMEA table; when the fault tree changes, the system re-evaluates the position of this failure in the entire fault propagation path through automatic calculation and updates the impact scope and severity rating of this failure;

[0043] Step S44. Automatically calculate the Risk Priority Number (RPN) in the product DFMEA: The system automatically calculates the RPN value based on the latest severity, frequency, and detectability. The system automatically fills in and updates the RPN according to the standardized rules in the product failure mode knowledge base;

[0044] Step S45. Build the intelligent update system framework for the product DFMEA table:

[0045] First, build the backend service development: The data synchronization module synchronizes with the product BOM, product failure mode knowledge base, and product fault tree table in the SQL database through the JAVA backend service; whenever the product BOM or the product failure mode knowledge base is updated, the system automatically triggers the update operation of the DFMEA table; and processes the automatic calculation of severity, frequency, and detectability, combines the recursive calculation of the impact scope by the fault tree, and then enables the system to automatically update the DFMEA table, obtains product information through the API interface and returns the failure mode and failure impact information;

[0046] Secondly, the intelligent display and interaction of the DFMEA form are realized through the Vue framework: The front-end interface dynamically displays the information in the DFMEA form, including failure modes, severity, occurrence probability, impact scope, and RPN values, by calling the API interface of the back-end. When the DFMEA form is automatically updated in the background, the front-end interface notifies the user via the web socket protocol, prompts that the DFMEA form has been updated, and allows the user to immediately view the new DFMEA results. This enables the user to modify certain fields in the DFMEA on the front-end interface, and the system submits all updates to the back-end through the API while updating the records in the DFMEA database.

[0047] Furthermore, in step S42, the product similarity between the product BOM and the new product BOM is calculated, specifically the cosine similarity between the two, and the calculation formula is as follows:

[0048] (1)

[0049] In formula (1), A represents the feature vector of the product BOM, and B represents the feature vector of the new product BOM. indicates that the current calculation is for the th component of the feature vector, represents the dimension of the feature vector, represents the th component of the feature vector A, represents the th component of the feature vector B.

[0050] Furthermore, in step S44, the calculation formula for the risk priority number RPN is as follows:

[0051] RPN = S × O × D (2)

[0052] In formula (2), S represents severity, O represents frequency, and D represents detectability.

[0053] The beneficial effects of the present invention are:

[0054] 1. By automatically updating the product BOM, product failure mode knowledge base, and product fault tree, the present invention reflects the functional and structural characteristics of the product, enables the automatic interactive update of the new product DFMEA form, and ensures the comprehensiveness of the product DFMEA construction.

[0055] 2. Using the cosine similarity algorithm, by calculating the similarity value between the new product BOM and the product BOM, the present invention provides an initial DFMEA form for the new product, more quickly and accurately identifies potential failures in the new product design stage, and intelligently identifies the key links that may cause product failures, ensuring the timeliness, integrity, and accuracy in the new product design process. Description of the Drawings

[0056] Figure 1 This is a flowchart of the DFMEA automatic update method combining after-sales data and fault tree according to the embodiments of the present invention.

[0057] Figure 2 This is a schematic structural diagram of the fault tree of product C according to the embodiments of the present invention. Detailed implementation manners

[0058] To facilitate better understanding of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The following is only exemplary and does not limit the protection scope of the present invention.

[0059] The present invention discloses a DFMEA automatic update method combining after-sales data and fault tree, including the following steps:

[0060] Step S1: Build a product BOM interface based on SQL database and JAVA: Decompose the product into hierarchical structures according to product design requirements and similarity, clarify the assembly relationship, component functions and the relationship between hierarchical structures; Design the SQL product database table structure to record component information and relationships, and build the product BOM interface;

[0061] Step S2: Build a product fault mode knowledge base interface based on JAVA and Vue according to product after-sales service data: Sort out product after-sales service data, classify fault descriptions and associate them with components in the product BOM table, create an SQL product fault mode database, and realize intelligent identification of faults through JAVA and Vue;

[0062] Step S3: Build a product fault tree interface according to the product BOM interface and the product fault mode knowledge base interface: Create a product fault tree structure in SQL according to the product BOM and the product fault mode knowledge base, so that the product fault tree is associated with the product BOM and the product fault mode knowledge base; Build a recursive query and update mechanism for the fault tree to achieve dynamic display; Use the JAVA backend to operate on the fault tree data and develop a JAVA fault tree interface;

[0063] Step S4: Realize the automatic update of the product DFMEA form: Design an SQL database for DFMEA to store DFMEA data, and realize automatic update through dynamic interaction with the product BOM, the product fault mode knowledge base and the product fault tree; Use the JAVA backend to synchronously update the DFMEA data so that the DFMEA data is automatically updated when the product BOM and the product fault mode change; Develop the front end based on Vue to realize the intelligent display and interaction of DFMEA.

[0064] To better understand the above technical solution, the exemplary embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. These are only the exemplary embodiments of the present invention. However, it should be understood that the present invention can be implemented in various forms and is not limited to the embodiments described herein. These embodiments are to enable those skilled in the art to understand the present invention more clearly and thoroughly.

[0065] This embodiment will be described by taking a certain injection molding machine factory as an example, and includes the following steps:

[0066] Step S1: Build a product BOM interface based on the SQL database and JAVA.

[0067] (1) Some of the fields in the product BOM table structure of a certain injection molding machine factory are shown in Table 1 below.

[0068] Table 1 Some of the fields in the product BOM table structure of a certain injection molding machine factory

[0069]

[0070] (2) By calculating the similarity between the product BOM table and the new product BOM table, obtain the initial DFMEA of the new product.

[0071] The component levels in the product BOM table of a certain injection molding machine factory are shown in Table 2 below.

[0072] Table 2 The component levels in the product BOM table of a certain injection molding machine factory

[0073]

[0074] The component levels in the BOM table of the newly designed product of a certain injection molding machine factory are shown in Table 3 below.

[0075] Table 3 The component levels in the BOM table of the newly designed product of a certain injection molding machine factory

[0076]

[0077] Construct the feature vectors of the product and the newly designed product respectively, specifically as follows:

[0078] First, convert the product BOM table and the new product BOM table into feature vectors: The component names in the product BOM table and the new product BOM table form a "feature word bag" containing all possible components. After de-recombination, all the components form the following feature word bag: ["moving template", "tail plate", "fixed template", "screw injection mechanism", "toggle link mechanism", "die adjustment mechanism", "lubrication mechanism", "ejector cylinder", "hydraulic valve group", "hydraulic mechanism", "machine body"].

[0079] Secondly, for the product BOM and the BOM of the newly designed product, create a binary vector according to the bag of feature words. The feature vector corresponds to a component in the bag of feature words. Let 0 indicate the non-existence of the component and 1 indicate the existence of the component.

[0080] Then, map the components in the product to their positions in the bag of feature words, and let the feature vector be A; map the components in the newly designed product to their positions in the bag of feature words, and let the feature vector be B. The feature vector A and the feature vector B are expressed as follows:

[0081] A = [1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1],

[0082] B = [1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1].

[0083] Among them, for the components in the BOM table of the newly designed product: moving template, tail plate, screw injection mechanism, die-setting mechanism, lubrication mechanism, hydraulic mechanism, and machine body, their corresponding positions in the feature vector B are 1, and the rest are 0.

[0084] Calculate the cosine similarity between the product BOM and the BOM of the newly designed product:

[0085]

[0086] In the above formula, A represents the feature vector of the product BOM, B represents the feature vector of the BOM of the newly designed product, indicates that the current calculation is for the th component of the feature vector, represents the dimension of the feature vector, represents the th component of the feature vector A, represents the th component of the feature vector B.

[0087] Calculate the dot product of the feature vectors of the product BOM and the BOM of the newly designed product, as follows:

[0088]

[0089] Calculate the Euclidean norm: and are the Euclidean norms of the feature vector A and the feature vector B respectively, as follows:

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] Set a similarity threshold. If the similarity between the BOM of the new designed product and the BOM of the product reaches the preset similarity threshold, an initial DFMEA form of the new product can be generated based on the DFMEA form of the product.

[0096] (3) Implement data update and interaction between the front end and the back end of the product BOM form based on JAVA.

[0097] Step S2: According to the product after-sales service data, construct an interface for the product failure mode knowledge base based on JAVA and Vue.

[0098] In this embodiment, for the historical failure description text in the product after-sales service data, use the data annotation platform Doccano to annotate the historical failure description text. Specifically: L1: The manual assembly quality does not meet the standard, L2: Appearance problem, L3: Missing parts, L4: Paint powder accumulation and caking, L5: Quality problem of a single part. Set the above predefined failure categories as labels in the data annotation platform Doccano.

[0099] Through the data in the product BOM, use Doccano to annotate the main components of the product. Specifically: S1: Moving template, S2: Fixed template, S3: Die setting mechanism, S4: Hydraulic mechanism, S5: Machine body. Set the above components as labels in the data annotation platform Doccano.

[0100] In this embodiment, assume that some failures occur in product C. According to the structure of the product BOM form, map the annotated failure modes to the matching components, as shown in Table 4.

[0101] Table 4 Product C Failure Mode Description Text - Component - Failure Label Matching Table

[0102]

[0103] Construct a historical failure mode SQL database table for storing product components and failure modes.

[0104] Step S3: Construct an interface for the product failure tree according to the product BOM interface and the product failure mode knowledge base interface.

[0105] In this embodiment, first, according to the product BOM and the product failure mode knowledge base, determine the relationship between the failure event name, failure event type, and hierarchical structure of the product failure tree. Among them, the failure event type includes failure mode, failure impact, etc. The structure schematic diagram of the product C failure tree is as Figure 2 shown.

[0106] Secondly, construct a recursive query in the SQL database of the product fault tree to dynamically display fault information according to the fault tree structure.

[0107] Then, use the JAVA backend service to handle the addition, deletion, modification, and query operations of the fault tree data, display the fault tree structure on the front end through the Vue framework, and implement interaction with users.

[0108] Step S4: Implement the automatic update of the product DFMEA form.

[0109] In this embodiment, design an SQL database for DFMEA to store DFMEA data, and achieve automatic update through dynamic interaction with the product BOM, product fault mode knowledge base, and product fault tree.

[0110] The system queries through the SQL database, identifies the fault modes in the fault mode knowledge base, searches for specific component information in the new product BOM, and obtains the fault description, severity, frequency, and detectability information of the corresponding components in the fault mode knowledge base.

[0111] The system automatically calculates the Risk Priority Number RPN in the product DFMEA according to the latest severity, frequency, and detectability. The calculation formula for the Risk Priority Number RPN is: RPN = S × O × D, where S represents severity, O represents frequency, and D represents detectability; the system automatically fills the newly added component information into the DFMEA form and updates the fault modes and impact contents related to this component.

[0112] Implement real-time synchronization and update of DFMEA through JAVA: When the new product BOM or the product fault mode knowledge base is updated, the JAVA backend automatically synchronizes the updated content of the DFMEA form through the data trigger mechanism, and the Vue front end dynamically displays the updated DFMEA content, allowing users to view the latest fault modes and related information of each component of the new product.

[0113] The above only describes the basic principles and preferred embodiments of the present invention. Those skilled in the art can make many changes and improvements based on the above description, and these changes and improvements should fall within the protection scope of the present invention.

Claims

1. A DFMEA automatic update method combining after-sales data and fault tree, characterized in that: The steps include: Step S1, constructing a product BOM interface based on SQL database and JAVA: based on product design requirements and similarity, decomposing the product into hierarchical structures, clarifying the assembly relationship, component functions and the relationship between hierarchical structures; Design SQL product database table structure to record component information and relationships, and build product BOM interface; Step S2: Based on the product after-sales service data, build a product failure mode knowledge base interface based on JAVA and Vue: sort out the product after-sales service data, classify the failure descriptions and associate them with the components in the product BOM table, create an SQL product failure mode database, and realize intelligent identification of failures through JAVA and Vue; Step S3, constructing a product fault tree interface according to the product BOM interface and the product failure mode knowledge base interface: creating a product fault tree structure in SQL according to the product BOM and the product failure mode knowledge base, so that the product fault tree is associated with the product BOM and the product failure mode knowledge base; constructing a recursive query and update mechanism of the fault tree to achieve dynamic display; using the JAVA backend to operate the fault tree data, and developing a JAVA fault tree interface; Step S4, realize automatic update of product DFMEA table: design DFMEA SQL database to store DFMEA data, realize automatic update through dynamic interaction with product BOM, product failure mode knowledge base and product failure tree; use JAVA backend to synchronously update DFMEA data so that DFMEA data is automatically updated when product BOM and product failure mode change; Develop the front end based on Vue to realize the intelligent display and interaction of DFMEA; It also includes the design of an intelligent update mechanism for the DFMEA table: the failure mode associated with each component is obtained from the product failure mode knowledge base. When a component in the product BOM is added or modified, the system automatically updates the failure mode of the component to the DFMEA table by querying the product failure mode knowledge base; for each component in the product BOM, the system automatically updates these failure modes to the DFMEA table by finding the associated ID; by extracting severity and frequency data from the product failure mode knowledge base, the system automatically associates these data with the corresponding fields of the DFMEA table; according to the structure of the fault tree, when the failure mode of a component affects its parent component, the system automatically increases its severity rating, and the system determines the potential impact of the fault on the system by recursing the hierarchical relationship of the fault tree; according to the product fault tree, when a component fails, the system automatically calculates the impact path of the fault on the entire product system, and updates these results to the "fault impact" field in the DFMEA table; when the fault tree changes, the system re-evaluates the position of the fault in the entire fault propagation path through automatic calculation, and updates the impact range and severity rating of the fault.

2. The DFMEA automatic update method combining after-sales data and fault tree according to claim 1 is characterized in that: Step S1 specifically includes the following steps: Step S11, designing a database of product BOM table: first, create a product BOM table containing basic product information, including at least product ID, product model and product description, identify products of different models through the product BOM table, set the product BOM table as the core table, and use it to manage parent products, sub-components and hierarchical nesting relationships; Step S12, constructing a backend framework of the product BOM interface based on JAVA: defining an entity class corresponding to the product BOM database in the backend framework, and implementing query, level expansion, and structure update business logic according to the hierarchical structure of the product BOM; writing an API controller to implement adding new product materials, updating product BOM relationships, and deleting product BOM structures; Step S13, using the Vue framework to build the front-end interface of the product BOM: Axios is used to realize data communication between the front-end and the back-end, and the front-end calls the back-end API interface through HTTP requests to realize the functions of adding, deleting, modifying and querying.

3. The DFMEA automatic update method combining after-sales data and fault tree according to claim 2 is characterized in that: In step S11, the product BOM table is used to display the product management interface of all product basic information, including adding, deleting, modifying and querying operations; The product BOM hierarchy is displayed in a form so that users can expand and view the product hierarchy relationship level by level and click on the current BOM data to export an Excel file.

4. The DFMEA automatic update method combining after-sales data and fault tree according to claim 1 is characterized in that: Step S2 specifically includes the following steps: Step S21, pre-define several failure mode categories in combination with the failure types appearing in the product after-sales service data, and set the pre-defined failure mode categories as labels in Doccano; Step S22, building an association between historical fault description texts and fault mode category labels: importing historical fault description texts into Doccano one by one, and matching each fault description text with the most relevant fault mode category label through manual annotation; Step S23, building an association between historical fault description texts and components in the product BOM: setting each component of the product in the product BOM as a label in Doccano, and matching each fault description text with the corresponding product component category label through manual labeling; Step S24, create an SQL database structure for historical fault mode data, construct a historical fault mode SQL database table structure for storing product components and fault modes, and import the fault description text data annotated in step S22 into the historical fault mode SQL database; when the newly input fault description text data is annotated, automatically add the new fault description text data to the historical fault mode SQL database so that the fault mode knowledge base can be updated in real time; Step S25: Build a product failure mode knowledge base interface based on JAVA and Vue, use the JAVA backend framework to process the storage and logical query of failure data, use the stored historical failure mode data, and use the NLP model to perform text analysis and probability matching on the newly input failure description text, identify potential failure modes, and return the failure mode that best matches it.

5. The DFMEA automatic update method combining after-sales data and fault tree according to claim 1 is characterized in that: Step S3 specifically includes the following steps: Step S31, constructing the SQL database table structure of the product fault tree: constructing the SQL product fault tree database structure including the relationship between the fault event name, the fault event type and the hierarchical structure according to the product BOM and the historical fault mode knowledge base; associating the product fault tree SQL database structure with the product BOM and the product fault mode knowledge base database; Step S32, constructing a recursive query and update mechanism for the product fault tree: constructing a recursive query in the product fault tree SQL database, starting from the top-level node, recursively obtaining all child nodes, and when the product BOM or product failure mode knowledge base data is updated, triggering the data update operation of the fault tree to synchronize the fault tree with the latest data; Step S33, constructing the fault tree backend logic and frontend interface: using the JAVA backend service to process the addition, deletion, modification and query operations of the fault tree data, and developing a tree-structured fault tree interface based on the Vue framework; Step S34, automatic update of the fault tree and database linkage: when the product BOM or product failure mode knowledge base is updated, the update of the fault tree SQL database is automatically triggered, that is, when a failure mode of a component is added, the system automatically creates a fault node for the component and updates the fault tree front-end interface.

6. The DFMEA automatic update method combining after-sales data and fault tree according to claim 5 is characterized in that: In step S33, the fault tree backend logic and frontend interface are constructed, specifically including: Interact with the fault tree SQL database through the JAVA backend to process the addition, deletion, modification and query operations of the fault number: provide the functions of adding, deleting, modifying and querying fault tree nodes through the node management module, recursively obtain the structural information of the fault tree from the fault tree SQL database, and return it to the front end in a tree data format; The fault tree interface is developed based on the Vue framework. The fault tree nodes and hierarchical relationships are displayed through the visual tree component so that users can see the fault hierarchy structure and realize the addition, deletion, modification and query interaction of nodes. After updating the data in the fault tree SQL database, the fault tree interface is refreshed through the front-end asynchronous request to realize real-time data update.

7. The DFMEA automatic update method combining after-sales data and fault tree according to claim 1 is characterized in that: Step S4 specifically includes the following steps: Step S41, constructing the core database structure of the DFMEA table: designing the DFMEA database structure so that real-time automatic updates can be achieved through dynamic data interaction with the product BOM, product failure mode knowledge base and product failure tree; Step S42, design a product similarity algorithm: obtain product similarity by calculating the edit distance between the product BOM and the new product BOM; set a similarity threshold, if the similarity between the new product BOM and the product BOM exceeds the similarity threshold, the new product can inherit the initial DFMEA table from the product; Step S43, automatic calculation of risk priority number RPN in product DFMEA: the system automatically calculates the RPN value based on the latest severity, frequency and detection, and the system automatically fills in the standardized rules in the product failure mode knowledge base and updates the RPN in real time; Step S44: construct the intelligent update system framework of the product DFMEA table: First, build the backend service development: the data synchronization module synchronizes the product BOM, product failure mode knowledge base and product fault tree table in the SQL database through the JAVA backend service; whenever the product BOM or product failure mode knowledge base is updated, the system automatically triggers the update operation of the DFMEA table; and handles the automatic calculation of severity, frequency and detection, combined with the recursive calculation of the impact range of the fault tree, so that the system automatically updates the DFMEA table, obtains product information through the API interface and returns the failure mode and failure impact information; Secondly, the intelligent display and interaction of the DFMEA table is realized through the Vue framework: the front-end interface dynamically displays the information in the DFMEA table, including failure mode, severity, probability of occurrence, impact range and RPN value, by calling the back-end API interface. When the background automatically updates the DFMEA table, the front-end interface notifies the user through the web socket protocol, prompting that the DFMEA table has been updated, and allows the user to view the new DFMEA results immediately, so that the user can modify certain fields in the DFMEA on the front-end interface. The system submits all updates to the back-end through the API and updates the records in the DFMEA database at the same time.

8. The DFMEA automatic update method combining after-sales data and fault tree according to claim 7 is characterized in that: In step S42, the product similarity between the product BOM and the new product BOM is calculated, specifically, the cosine similarity between the two is calculated, and the calculation formula is as follows: （1） In formula (1), A represents the feature vector of product BOM, and B represents the feature vector of new product BOM. Indicates that the feature vector currently being calculated is Quantity, represents the feature vector dimension, The first Quantity, represents the first feature vector B A quantity.

9. The DFMEA automatic update method combining after-sales data and fault tree according to claim 7, characterized in that: In step S43, the calculation formula of the risk priority number RPN is as follows: RPN=S×O×D(2) In formula (2), S represents severity, O represents frequency, and D represents detection.

Images