A product management system and management method for the entire life cycle

Abstract

The present invention discloses a product management system and management method for the whole life cycle, which relates to the technical field of product management, and includes: dividing the product life cycle to clarify the product data management content in each stage; using AI technology to preliminarily analyze the product data in the cache; recursively obtaining the product data of each sub-node according to the root node number and version number of the product in combination with the preliminary analysis report, constructing a virtual simulation model by using digital twin technology in combination with the classification result, performing full life cycle tracking through blockchain technology, and using AI technology for detection and warning; when the product life cycle ends, stopping the product data update; by dividing the product life cycle, clarifying the data management content in each stage, and introducing AI technology and digital twin technology, the present invention realizes the automatic acquisition, anomaly detection and simulation modeling of product data, and significantly improves the overall efficiency and accuracy of product data management.

Description

Technical Field

[0001] The present invention relates to the technical field of product management, and in particular to a product management system and management method for the entire life cycle. Background Art

[0002] With the development of Industry 4.0 and intelligent manufacturing, the research and development of Product Lifecycle Management (PLM) has gradually become an important means of informatization in the manufacturing industry. Traditional product management methods often only focus on data management in a single life cycle stage, such as the design stage or the manufacturing stage, lacking unified management and coordination of data throughout the life cycle. This one-sided management method is prone to the following problems. Data in each stage is often scattered in different systems and departments, lacking unified standards and interfaces, resulting in untimely information transmission and affecting work efficiency. Due to the lack of unified version management and data tracking mechanisms, the data versions used by different departments may be inconsistent, easily leading to problems such as misoperations, design or manufacturing errors. In multiple stages of the product life cycle, it is difficult to track the source and changes of data in each stage, affecting product quality control and rapid problem positioning. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a product management system and management method for the entire life cycle to solve the complexity and inefficiency problems in existing product life cycle management.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a product management method for the entire life cycle, which includes: dividing the product life cycle into five stages: design, trial production, procurement, maintenance, and mass production, and clarifying the product data management content in each stage;

[0007] After each life cycle stage is defined clearly, start automatically collecting product data, caching it according to the root node number and version number of the product, using AI technology to perform a preliminary analysis on the product data in the cache, checking and marking abnormal situations of the product data, and generating a preliminary analysis report;

[0008] According to the root node number and version number of the product combined with the preliminary analysis report, recursively obtain the product data of each sub-node, classify it, and use digital twin technology to combine the classification results to construct a virtual simulation model and obtain simulation data;

[0009] Integrate the simulation data and the classified product data to generate a product data packet. Through blockchain technology, track the entire life cycle of the product data packet from issuance to reception, and use AI technology for detection and warning;

[0010] When the product life cycle ends, automatically archive all product data, mark it with an end flag, and stop product data updates.

[0011] As a preferred solution of the product management method for the entire life cycle described in the present invention, wherein the product data management content of each stage is clarified, including the following steps:

[0012] The design stage includes managing the bill of materials, design drawings, technical documents, and review records of the product;

[0013] The product trial production stage includes managing process documents, trial production plans, experimental test reports, and review records of each process node;

[0014] The procurement stage includes managing supplier information, purchase orders, and material receipt records;

[0015] The maintenance stage includes managing maintenance plans, maintenance records, and fault analysis reports;

[0016] The mass production stage includes managing manufacturing BOM data, manufacturing process documents, packaging BOM data, and production record files.

[0017] As a preferred solution of the product management method for the entire life cycle described in the present invention, wherein AI technology is used to perform a preliminary analysis of the product data in the cache, check and mark abnormal situations of the product data, and generate a preliminary analysis report, including the following steps:

[0018] Use the automated data analysis technology of AI to check whether the product data of each stage contains necessary fields;

[0019] Define the criteria for anomaly detection based on historical product data;

[0020] Select the Isolation Forest algorithm, identify outliers by randomly partitioning the product data, detect abnormal data points, and mark the abnormal data items that exceed the standard;

[0021] Integrate all incomplete data items, missing fields, and marked abnormal data items to generate a preliminary analysis report.

[0022] As a preferred solution of the product management method for the entire life cycle described in the present invention, wherein, according to the root node number and version number of the product combined with the preliminary analysis report, recursively obtain the product data of each child node and classify it, including the following steps:

[0023] The sub - nodes refer to multiple hierarchical sub - nodes of a product in the product data life cycle;

[0024] Starting from the root node of the product, obtain the data of the root node, and then retrieve the cached product data according to the root node number and version number;

[0025] Use a recursive algorithm to call sub - nodes layer by layer, obtain all associated data of the sub - nodes, and combine with the preliminary analysis report to adopt processing strategies for different data states;

[0026] In each recursive call process, add the current sub - node data to the data set, and finally construct the complete hierarchical structure of the entire product;

[0027] Classify the product data obtained recursively into BOM files, design files, process files, maintenance files, and approval files.

[0028] As a preferred solution of the product management method for the whole life cycle described in the present invention, wherein, using digital twin technology combined with the classification results, construct a virtual simulation model to obtain simulation data, including the following steps:

[0029] Use the root node number and version number of the product in the classification results to create an initial virtual model of the product in the digital twin virtual simulation platform;

[0030] Import the BOM files obtained recursively into the initial virtual model, and define the hierarchical structure, bill of materials, component relationships, quantity, and cost of the product;

[0031] Import the design files into the initial virtual model to construct the geometric shape and physical model of the product;

[0032] Import the process files into the initial virtual model to define the manufacturing process flow of the product;

[0033] Import the maintenance files into the initial virtual model to construct the maintenance plan and health monitoring model of the product;

[0034] Import the review records and signature records in the approval files into the initial virtual model to construct the quality control and traceability mechanism of the product;

[0035] Integrate the initial virtual model after the import of product data is completed to form a virtual simulation model and obtain simulation data.

[0036] As a preferred solution of the product management method for the whole life cycle described in the present invention, wherein, through blockchain technology, conduct full - life - cycle tracking of product data packets from issuance to reception, and use AI technology for detection and warning, including the following steps:

[0037] After the product data packet is generated, it is uploaded to the blockchain network, and the generation time and publisher information of the product data packet are recorded;

[0038] When transmitting the product data packet, the smart contract will automatically require the recipient to verify it. After the verification passes, the receiving information is recorded;

[0039] Through the blockchain, the entire life cycle of the product data packet is traced from generation to final mass production;

[0040] During the tracing process, AI technology is used to detect whether there are deviations between the simulation data and the classified product data;

[0041] If there are no deviations, continue the tracing until the end of the product life cycle;

[0042] If there are deviations, re-collect product data, build a virtual simulation model, and use AI technology to detect until there are no deviations, then continue the tracing until the end of the product life cycle.

[0043] As a preferred solution of the product management method for the whole life cycle described in the present invention, when the product life cycle ends, all product data is automatically archived, marked with an end mark, and product data updates are stopped, including the following steps:

[0044] At the end of the product life cycle, all product-related data is packaged to form a complete archive package;

[0045] The archive package is compressed and stored in a secure long-term data cloud storage system;

[0046] Generate a hash value for the archive package, and record the archiving event and the hash value in the blockchain;

[0047] Update the life cycle status of the product to ended, record the status update in the blockchain, and use the smart contract to automatically lock the data update permission of the product after archiving.

[0048] In a second aspect, the present invention provides a product management system for the whole life cycle, including: a life cycle data management module, which is responsible for dividing the product life cycle into five stages: design, trial production, procurement, maintenance, and mass production, and clarifying the product data management content of each stage;

[0049] An automatic data collection and AI preliminary analysis module, which is responsible for automatically collecting product data after each life cycle stage is defined clearly, caching it according to the root node number and version number of the product, using AI technology to conduct a preliminary analysis of the product data in the cache, checking and marking abnormal product data situations, and generating a preliminary analysis report;

[0050] The recursive data acquisition and digital twin simulation module is responsible for recursively acquiring the product data of each sub-node according to the root node number and version number of the product in combination with the preliminary analysis report, classifying it, and using digital twin technology to construct a virtual simulation model based on the classification results to obtain simulation data;

[0051] The data packet integration and blockchain tracing module is responsible for integrating the simulation data and the classified product data to generate a product data packet, tracking the entire life cycle of the product data packet from issuance to reception through blockchain technology, and using AI technology for detection and warning;

[0052] The AI detection and data archiving module is responsible for automatically archiving all product data, marking it as ended, and stopping the product data update when the product life cycle ends.

[0053] Thirdly, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and wherein, when the computer program is executed by the processor, any step of the product management method for the entire life cycle as described in the first aspect of the present invention is implemented.

[0054] Fourthly, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and wherein, when the computer program is executed by the processor, any step of the product management method for the entire life cycle as described in the first aspect of the present invention is implemented.

[0055] The beneficial effects of the present invention are as follows: Using AI technology to monitor and automatically analyze product data can quickly detect abnormal situations in the data, reduce the cost of manual intervention, and improve the accuracy and efficiency of data processing. By recursively acquiring the data of each sub-node and constructing a virtual simulation model in combination with digital twin technology, the performance of the product in different life cycle stages can be simulated more precisely, further improving the reliability of product design and maintenance. By tracking the entire life cycle of the product data packet through blockchain technology, the transparency and traceability of the data are realized, ensuring that each link from data generation to archiving has high security and integrity, and significantly improving the overall efficiency and accuracy of product data management. Description of the Drawings

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0057] Figure 1Flowchart of the product management method for the entire life cycle in Embodiment 1.

[0058] Figure 2 Flowchart of the preliminary analysis results and optimization in Embodiment 1. Detailed implementation manners

[0059] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention with reference to the accompanying drawings of the specification.

[0060] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0061] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that are mutually exclusive of other embodiments.

[0062] Embodiment 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides a product management method for the entire life cycle, including the following steps:

[0063] S1. Divide the product life cycle into five stages: design, trial production, procurement, maintenance, and mass production. Clarify the product data management content for each stage, including the following steps:

[0064] The design stage includes managing the bill of materials (BOM) of the product, design drawings, technical documents, and review records;

[0065] The product trial production stage includes managing process documents, trial production plans, experimental test reports, and review records of each process node;

[0066] The procurement stage includes managing supplier information, purchase orders, and material receipt records;

[0067] The maintenance stage includes managing maintenance plans, maintenance records, and failure analysis reports;

[0068] The mass production stage includes managing manufacturing BOM data, manufacturing process documents, packaging BOM data, and production record files.

[0069] S2. After each lifecycle stage is clearly defined, start automatically collecting product data, caching it according to the root node number and version number of the product, and using AI technology to conduct a preliminary analysis of the product data in the cache, checking and marking abnormal situations of the product data, and generating a preliminary analysis report, including the following steps:

[0070] The root node number is assigned by the product data management system and is used to identify the main data node of the product, representing the basic information and structure of the entire product. The root node number usually does not change over time. Even if changes occur in the design, production, or maintenance stage of the product, the root node number remains unchanged;

[0071] The version number is used to identify different iterative versions of the product. Whenever changes occur in the product's design, manufacturing process, or maintenance plan, a new version number is generated. The version number changes throughout the product's lifecycle to distinguish product data in different stages;

[0072] Process the product data in the cache by removing noise, correcting format errors, and filling in missing values, etc.;

[0073] Use the automated data analysis technology of AI to check whether the product data in each stage contains necessary fields. For example, BOM data must contain fields such as material number, name, quantity, etc.;

[0074] Define the criteria for anomaly detection based on historical product data. For example, the material quantity must be a positive number and cannot exceed a certain range;

[0075] Select the Isolation Forest algorithm. By randomly partitioning the product data, identify outliers, detect abnormal data points, and mark the abnormal data items that exceed the standard;

[0076] It should be noted that the Isolation Forest algorithm is an unsupervised learning algorithm suitable for detecting abnormal data points; during the analysis process, select fields that can reflect product characteristics as input features; for example: material quantity, production time, and test data, etc.; the material quantity is used to judge whether the material exceeds the normal range; the production time is used to judge whether the production cycle is abnormal; the test data is such as pressure, temperature, etc., to judge whether it exceeds the expected range; the Isolation Forest continuously splits the data by randomly selecting features (such as material quantity, production time, etc.) and randomly determining the splitting points; if a data point can be separated alone with fewer splitting steps, then it is very likely to be an abnormal point. Because normal data points are usually closer to other points and require more splitting steps to be isolated. The Isolation Forest algorithm generates an anomaly score for each data point. The higher the score, the more likely the data point is to be an abnormal point. The algorithm classifies the data points into normal points and abnormal points according to the scores.

[0077] Integrate all incomplete data items, missing fields, and marked abnormal data items to generate a preliminary analysis report.

[0078] S3. Recursively obtain the product data of each sub-node according to the root node number and version number of the product in combination with the preliminary analysis report, classify it, and use digital twin technology to construct a virtual simulation model based on the classification results to obtain simulation data, including the following steps:

[0079] S3.1. A sub-node refers to multiple hierarchical sub-nodes of a product in the product data life cycle;

[0080] Starting from the root node of the product (B: product root node number, C: product version number), obtain the data of the root node, and then retrieve the cached product data according to the root node number and version number;

[0081] Use a recursive algorithm to call sub-nodes layer by layer, obtain all associated data of the sub-nodes (such as sub-BOM, sub-design files, sub-process files, etc.), and combine the preliminary analysis report to adopt different processing strategies for different data states (complete, missing, abnormal);

[0082] Specifically, if the product data is marked as complete and without abnormalities, the recursive acquisition of sub-node data can be normally triggered; if the product data is marked as incomplete but without abnormalities, the re-acquisition process is triggered, and after successful re-acquisition, the recursive processing of sub-nodes continues; if the product data is marked as incomplete and with abnormalities, it can be selected to skip this node, provided that this node is not important and has little impact on the overall product structure even if it is skipped; if the product data is marked as complete but with abnormalities, it is pushed to relevant personnel for manual correction. At this time, the recursive process will pause and continue after the data correction is completed;

[0083] In each recursive call process, add the current sub-node data to the data set, and finally construct the complete hierarchical structure of the entire product;

[0084] The termination condition of the recursive call is when a node has no more sub-nodes, stop the recursion and return the complete data set;

[0085] Furthermore, the product data obtained recursively may come from different systems and sources (such as PDM, ERP, MES, etc.), and the formats and structures of the data may be inconsistent. To ensure the availability of the data in the subsequent stage, the system needs to standardize this data to ensure that the data format is unified and complies with international standards (such as ISO 10303, STEP standard). The standardization process includes data classification, cleaning, format conversion, etc.;

[0086] Classify the product data obtained recursively into BOM files, design files, process files, maintenance files, and review files;

[0087] It should be noted that the BOM file includes information such as bill of materials, quantity, cost, etc.; the design file includes 2D drawings, 3D models, etc.; the process file includes information such as production process steps, man-hours, equipment usage, etc.; the maintenance file includes maintenance records, maintenance plans, etc.; the review and approval file includes review and signature records at each stage of design and manufacturing.

[0088] S3.2. Use the root node number and version number of the product in the classification result to create an initial virtual model of the product in the digital twin virtual simulation platform;

[0089] Import the recursively obtained BOM file into the initial virtual model, and define information such as the hierarchical structure, bill of materials, component relationship, quantity, cost, etc. of the product;

[0090] Specifically, parse the bill of materials information in the BOM file, create the hierarchical structure of the product, and add each component in the BOM to the initial virtual model, associate material information such as quantity and cost with each component, and construct the hierarchical structure of the product (such as parent-child relationship) according to the bill of materials and component relationship in the BOM file to ensure that the hierarchical structure of the virtual simulation model is consistent with that of the actual product;

[0091] Import the design file (such as 2D drawings, 3D models) into the initial virtual model to construct the geometric shape and physical model of the product;

[0092] Specifically, parse the geometric data in the design file (such as 3D model files, CAD drawings), add design information (such as geometric shape, material properties, etc.) to each component of the initial virtual model, and set the physical properties of each component, such as density, stiffness, strength, etc., through the material properties and structural information in the design file for subsequent physical simulation;

[0093] Import the process file into the initial virtual model to define the manufacturing process flow of the product, including information such as production steps, man-hours, equipment usage, etc.;

[0094] Specifically, parse the production steps and process routes in the process file, define the manufacturing process flow for each component in the initial virtual model, associate man-hours and equipment usage, and define a dynamic process flow for each component in the initial virtual model to ensure that during the simulation process, the system can simulate the production process of the product and track the execution of process steps;

[0095] Import the maintenance file into the initial virtual model to construct the maintenance plan and health monitoring model of the product;

[0096] Specifically, analyze the maintenance records and maintenance plans, define maintenance plans (such as regular maintenance, fault prediction, etc.) for each component in the initial virtual model, establish a health monitoring model of the product through maintenance data, simulate the wear and fault conditions of the product at different usage stages, and monitor the running state of the product in real time;

[0097] Import the review records and signature records in the signed and reviewed documents into the initial virtual model to construct a quality control and traceability mechanism for the product;

[0098] Specifically, analyze the signed and reviewed records to ensure that each design change and process adjustment is authorized, retain the review records in the virtual model, and ensure that the signed and reviewed data at each stage is traceable;

[0099] Integrate the initial virtual model after the product data import is completed to form a virtual simulation model and obtain simulation data;

[0100] It should be noted that after the product data import is completed, using digital twin technology, check whether the material properties (such as density, elastic modulus, hardness, etc.) in the initial virtual model are consistent with the material data in the design document. If inconsistencies are found, calibrate these physical properties. Check whether the geometric accuracy (such as curve radius, thickness, hole diameter, etc.) in the initial virtual model is consistent with the design document. If there are errors, adjust the geometric accuracy to ensure the accurate physical behavior of the initial virtual model. Calibrate the production process steps in the process document to ensure that the process flow in the initial stage is consistent with the actual product manufacturing process. Calibrate the man-hours and equipment usage of each process step to ensure that the subsequent manufacturing simulation can reflect the real production process. After the verification and calibration of the initial virtual model are completed, the virtual simulation model has been formed.

[0101] S4. Integrate the simulation data and the classified product data to generate a product data package, and through blockchain technology, track the entire life cycle of the product data package from issuance to receipt, and use AI technology for detection and warning, including the following steps:

[0102] Integrate the simulation data and the classified product data to form a standardized product data package (such as a JSON or XML file). The data package should have a general hierarchical structure and cover various types of data, such as BOM, geometric design, simulation data, process flow, maintenance records, etc.;

[0103] After the product data package is generated, upload it to the blockchain network and record the generation time and publisher information of the product data package;

[0104] It should be noted that upload the generated product data package to the blockchain network, record the generation time, publisher information and its hash value of the product data package. The immutability of the blockchain ensures the authenticity and security of the data package;

[0105] When transmitting the product data packet, the smart contract will automatically request the recipient to conduct verification. After the verification passes, the received information will be recorded;

[0106] It should be noted that during the transmission process, the smart contract will ensure that the recipient verifies the integrity and authenticity of the data packet within the specified time. The recipient needs to perform the following verification steps: The recipient calculates the hash value of the received data packet and compares it with the hash value recorded on the blockchain to ensure that the data packet has not been tampered with. Check the version number of the data packet to ensure that the latest version is received. After successful verification, the smart contract will automatically record the received information and record the reception status and time of the data packet into the blockchain;

[0107] Through the blockchain, the entire life cycle of the product data packet is traced from generation to final mass production;

[0108] During the tracing process, AI technology is used to detect whether there are deviations between the simulation data and the classified product data;

[0109] If there are no deviations, continue the tracing until the end of the product life cycle;

[0110] If there are deviations, re - collect the product data, build a virtual simulation model, and use AI technology for detection until there are no deviations, then continue the tracing until the end of the product life cycle.

[0111] S5. When the product life cycle ends, automatically archive all product data, mark it with an end flag, and stop product data updates, including the following steps:

[0112] When the product life cycle ends, pack all product - related data to form a complete archive package. The archive package should contain the complete product data tree structure to ensure convenient traceability for future queries;

[0113] Compress the archive package and store it in a secure long - term data cloud storage system to ensure that the data storage location has high availability and security;

[0114] Generate a hash value for the archive package to ensure that the integrity and authenticity of the data packet can be verified in the future. Record the archiving event and the hash value into the blockchain to ensure that the archiving information is tamper - proof and traceable;

[0115] Update the life cycle status of the product to ended, record the status update into the blockchain to ensure that the end status of the product life cycle is tamper - proof, and use the smart contract to automatically lock the data update permission of the product after archiving.

[0116] This embodiment also provides a product management system for the whole life cycle, including: a life cycle data management module, which is responsible for dividing the product life cycle into five stages: design, trial production, procurement, maintenance, and mass production, and clarifying the product data management content of each stage;

[0117] An automatic data collection and AI preliminary analysis module, which is responsible for automatically collecting product data after each life cycle stage is defined clearly, caching the product data according to the root node number and version number of the product, using AI technology to conduct a preliminary analysis of the product data in the cache, checking and marking abnormal situations of the product data, and generating a preliminary analysis report;

[0118] A recursive data acquisition and digital twin simulation module, which is responsible for recursively acquiring the product data of each sub-node according to the root node number and version number of the product in combination with the preliminary analysis report, classifying them, and using digital twin technology to construct a virtual simulation model based on the classification results to obtain simulation data;

[0119] A data packet integration and blockchain tracking module, which is responsible for integrating the simulation data and the classified product data to generate a product data packet, tracking the whole life cycle of the product data packet from issuance to reception through blockchain technology, and using AI technology for detection and warning;

[0120] An AI detection and data archiving module, which is responsible for automatically archiving all product data, marking an end tag, and stopping the update of product data when the product life cycle ends.

[0121] This embodiment also provides a computer device, which is applicable to the situation of the product management method for the whole life cycle, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the product management method for the whole life cycle proposed in the above embodiment.

[0122] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be implemented through WIFI, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads provided on the housing of the computer device. It can also be an external keyboard, touchpad, or mouse, etc.

[0123] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the product management method for the entire life cycle proposed in the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read-Only Memory (EPROM for short), Programmable Read-Only Memory (PROM for short), Read-Only Memory (ROM for short), magnetic memory, flash memory, magnetic disks, or optical discs.

[0124] In summary, through the present invention: The AI technology monitors and automatically analyzes product data in real time, can quickly detect abnormal situations in the data, reduces the cost of manual intervention, and improves the accuracy and efficiency of data processing. By recursively obtaining the data of each sub-node and combining with the digital twin technology to construct a virtual simulation model, it can more accurately simulate the performance of the product in different life cycle stages, further improving the reliability of product design and maintenance. By using blockchain technology to trace the entire life cycle of product data packets, it realizes the transparency and traceability of data, ensures a high degree of security and integrity in each link from data generation to archiving, and significantly improves the overall efficiency and accuracy of product data management.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A product management method for the entire life cycle, characterized in that Including: Divide the product life cycle into five stages: design, trial production, procurement, maintenance, and mass production, and clarify the product data management content for each stage; After each life cycle stage is clearly defined, start automatically collecting product data, cache it according to the root node number and version number of the product, and use AI technology to perform a preliminary analysis of the product data in the cache, check and mark abnormal product data situations, and generate a preliminary analysis report; Based on the root node number and version number of the product combined with the preliminary analysis report, recursively obtain the product data of each sub-node. The sub-node refers to multiple hierarchical sub-nodes of the product in the product data life cycle; starting from the root node of the product, obtain the data of the root node, and then retrieve the cached product data according to the root node number and version number; use a recursive algorithm to call sub-nodes layer by layer, obtain all associated data of the sub-nodes, and combine the preliminary analysis report to adopt different processing strategies for different data states; if the product data is marked as complete and without abnormalities, it can normally trigger the recursive acquisition of sub-node data; if the product data is marked as incomplete but without abnormalities, trigger the re-collection process, and after successful re-collection, continue to recursively process the sub-nodes; if the product data is marked as incomplete and with abnormalities, it can be selected to skip this node, provided that this node is not important and has little impact on the overall product structure even if it is skipped; if the product data is marked as complete but with abnormalities, push it to relevant personnel for manual correction, and the recursive process will pause and continue after the data correction is completed; In each recursive call process, add the current sub-node data to the data set, and finally construct the complete hierarchical structure of the entire product; the termination condition of the recursive call is when a certain node has no more sub-nodes, stop the recursion, return the complete data set, and classify it. Use digital twin technology combined with the classification result to construct a virtual simulation model and obtain simulation data; Integrate the simulation data and the classified product data to generate a product data packet, and use blockchain technology to track the entire life cycle of the product data packet from issuance to reception, and use AI technology for detection and warning; When the product life cycle ends, automatically archive all product data, mark it with an end mark, and stop product data updates.

2. The product management method for the whole life cycle according to claim 1, characterized in that Clarify the product data management content for each stage, including the following steps: The design stage includes managing the product's bill of materials, design drawings, technical documents, and review records; The product trial production stage includes managing process documents, trial production plans, experimental test reports, and review records of each process node; The procurement stage includes managing supplier information, purchase orders, and material receipt records; The maintenance stage includes managing maintenance plans, maintenance records, and failure analysis reports; The mass production stage includes managing manufacturing BOM data, manufacturing process documents, packaging BOM data, and production record files.

3. The product management method for the whole life cycle according to claim 2, characterized in that, Use AI technology to perform a preliminary analysis of the product data in the cache, check and mark abnormal product data situations, and generate a preliminary analysis report, including the following steps: Use the automated data analysis technology of AI to check whether the product data of each stage contains necessary fields; Define the criteria for anomaly detection based on historical product data; Select the Isolation Forest algorithm. By randomly partitioning the product data, identify outliers, detect abnormal data points, and mark the abnormal data items that exceed the criteria; Integrate all incomplete data items, missing fields, and marked abnormal data items to generate a preliminary analysis report.

4. The product management method for the whole life cycle according to claim 3, characterized in that, Based on the root node number and version number of the product and combined with the preliminary analysis report, recursively obtain the product data of each child node and classify it. The steps are as follows: In each recursive call, add the current child node data to the data set, and finally construct the complete hierarchical structure of the entire product; Classify the recursively obtained product data into BOM files, design files, process files, maintenance files, and approval files.

5. The product management method for the whole life cycle according to claim 4, characterized in that Utilize digital twin technology and combine with the classification results to construct a virtual simulation model and obtain simulation data. The steps are as follows: Use the root node number and version number of the product in the classification results to create an initial virtual model of the product in the digital twin virtual simulation platform; Import the recursively obtained BOM file into the initial virtual model to define the hierarchical structure, bill of materials, component relationships, quantities, and costs of the product; Import the design file into the initial virtual model to construct the geometric shape and physical model of the product; Import the process file into the initial virtual model to define the manufacturing process flow of the product; Import the maintenance file into the initial virtual model to construct the maintenance plan and health monitoring model of the product; Import the review records and signature records in the approval file into the initial virtual model to construct the quality control and traceability mechanism of the product; Integrate the initial virtual model after the import of product data is completed to form a virtual simulation model and obtain simulation data.

6. The product management method for the whole life cycle according to claim 5, characterized in that Through blockchain technology, conduct full-life cycle tracking of the product data packet from issuance to reception, and use AI technology for detection and warning. The steps are as follows: When the product data packet is generated, upload it to the blockchain network and record the generation time and publisher information of the product data packet; When transmitting the product data packet, the smart contract will automatically require the recipient to verify. After verification passes, record the reception information; Through the blockchain, track the entire life cycle of the product data packet from generation to final mass production; During the tracking process, use AI technology to detect whether there are deviations between the simulation data and the classified product data; If there are no deviations, continue the tracking until the end of the product life cycle; If there are deviations, re-collect product data, construct a virtual simulation model, and use AI technology for detection until there are no deviations, then continue the tracking until the end of the product life cycle.

7. The product management method for the whole life cycle according to claim 6, characterized in that, When the product life cycle ends, automatically archive all product data, mark the end, and stop product data updates. The steps are as follows: At the end of the product life cycle, pack all product-related data to form a complete archive package; Compress the archive package and store it in a secure long-term data cloud storage system; Generate a hash value for the archive package, and record the archiving event and the hash value in the blockchain; Update the lifecycle status of the product to ended, record the status update in the blockchain, and use a smart contract to automatically lock the product's data update permission after archiving.

8. A product management system for the entire life cycle, based on the product management method for the entire life cycle according to any one of claims 1 to 7, characterized in that, Including: A lifecycle data management module, responsible for dividing the product lifecycle into five stages: design, trial production, procurement, maintenance, and mass production, and clarifying the product data management content for each stage; An automatic data collection and AI preliminary analysis module, responsible for starting to automatically collect product data after each lifecycle stage is defined clearly, caching the product data according to the product's root node number and version number, using AI technology to perform preliminary analysis on the product data in the cache, checking and marking abnormal product data situations, and generating a preliminary analysis report; A recursive data acquisition and digital twin simulation module, responsible for recursively acquiring the product data of each sub-node according to the product's root node number and version number in combination with the preliminary analysis report, where the sub-node refers to multiple hierarchical sub-nodes of the product in the product data lifecycle; starting from the root node of the product, obtaining the data of the root node, and then retrieving the cached product data according to the root node number and version number; using a recursive algorithm to call sub-nodes layer by layer, obtaining all associated data of the sub-nodes, and combining the preliminary analysis report, taking different processing strategies for different data states; if the product data is marked as complete and without abnormalities, it can normally trigger the recursive acquisition of sub-node data; if the product data is marked as incomplete but without abnormalities, it triggers a re-collection process, and after successful re-collection, continues to recursively process the sub-nodes; if the product data is marked as incomplete and with abnormalities, it can choose to skip this node, provided that this node is not important and has little impact on the overall product structure even if skipped; if the product data is marked as complete but with abnormalities, it is pushed to relevant personnel for manual correction, and the recursive process will pause and wait for the data correction to be completed before continuing; In each recursive call process, add the current sub-node data to the data set, and finally construct the complete hierarchical structure of the entire product; the termination condition of the recursive call is when a certain node has no more sub-nodes, stop the recursion, return the complete data set, and perform classification, and use digital twin technology to combine the classification results to construct a virtual simulation model to obtain simulation data; A data packet integration and blockchain tracking module, responsible for integrating the simulation data and the classified product data to generate a product data packet, tracking the entire lifecycle of the product data packet from issuance to reception through blockchain technology, and using AI technology for detection and warning; An AI detection and data archiving module, responsible for automatically archiving all product data, marking it with an end mark, and stopping the product data update when the product lifecycle ends.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the product management method for the entire lifecycle described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the product management method for the entire lifecycle described in any one of claims 1 to 7.

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