Vehicle frame design to scrapping life cycle management system based on new information technology service

Abstract

This invention provides a vehicle frame design-to-end-of-lifecycle management system based on novel information technology services, relating to the field of computer management technology. It includes a full-lifecycle digital twin central hub, a design collaborative optimization module, a manufacturing digital twin control module, a group twin operation and maintenance assessment module, a low-carbon end-of-life dismantling module, a full-chain data fusion and blockchain secure sharing module, and a terminal interaction module. Through the design collaborative optimization module, this invention integrates CAD, parametric design, multidisciplinary collaborative optimization, and machine learning technologies. Based on the digital twin mirror, it acquires full-lifecycle operating condition data, establishes a multi-objective optimization model, and achieves adaptive iterative optimization of design parameters. Simultaneously, it builds a multi-terminal collaborative design platform, enabling real-time collaboration among personnel from multiple fields. This effectively shortens the design-verification-modification cycle, improves design accuracy, reduces design costs, and ensures a high degree of matching between the design scheme and actual operating conditions.

Description

Technical Field

[0001] This invention relates to the field of computer management technology, and in particular to a vehicle frame design-to-end-of-life management system based on new information technology services. Background Technology

[0002] As the core load-bearing component of a vehicle, the performance, quality, and life-cycle economics of the chassis directly determine the vehicle's safety, reliability, and environmental friendliness. Currently, the industry is gradually applying Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) to the design and manufacturing stages of chassis, with some companies attempting to introduce digital twin technology to achieve control and optimization at individual stages. In the design phase, single-parameter optimization or traditional multidisciplinary optimization methods are mainly used, combined with CAD systems to complete chassis structural design and simulation verification. In the manufacturing phase, CAM systems and basic IoT devices are used to automate production and data collection for some processes. In the operation and maintenance phase, simple sensor monitoring is used to monitor the basic condition of the chassis. In the end-of-life phase, traditional crushing and dismantling methods are mostly used, focusing only on material recycling without considering low-carbon and resource-maximizing utilization. Meanwhile, some companies are attempting to build basic data management platforms to integrate data sources from individual stages, initially achieving centralized data storage and simple retrieval. Furthermore, with the increasing penetration rate of the new energy vehicle market, the demand for lightweight, intelligent, and low-carbon chassis is becoming increasingly urgent, and related technologies are being explored, such as topology optimization and the application of lightweight materials, but an integrated management system covering the entire life cycle has not yet been formed.

[0003] Specifically, in existing technologies, the design phase is disconnected from subsequent phases such as manufacturing, operation and maintenance, and scrapping. Single-parameter optimization techniques are used without integrating full lifecycle operating data for collaborative optimization, resulting in long "design-verification-modification" cycles, mismatches between design solutions and actual operating conditions, low efficiency in multi-domain collaboration, and insufficient design accuracy and economy. The manufacturing phase lacks full-process digital twin control, only achieving control over single processes. It cannot synchronize design standards with actual manufacturing data in real time, leading to delayed process parameter adjustments, weak equipment failure prediction capabilities, large manufacturing deviations, high scrap rates, high equipment downtime risks, and low manufacturing efficiency. The operation and maintenance phase only achieves status monitoring of a single chassis, failing to consider the impact of different operating scenarios on chassis damage evolution. A single twin cannot represent the overall health distribution, and there is a conflict between data privacy and model generalization ability. Low lifespan prediction accuracy and long development cycle for scenario-based lifespan prediction models for new vehicle models; lack of low-carbon orientation and scientific decision-making in end-of-life dismantling, absence of material recycling potential assessment and carbon footprint calculation models, unreasonable dismantling paths, and inability to maximize resource utilization and minimize carbon emissions, failing to meet environmental protection and low-carbon development requirements; severe data silos across the entire chain, with data from different links and enterprises unable to achieve secure sharing and efficient flow, insufficient data immutability and traceability, high difficulty in cross-enterprise collaboration, low data flow efficiency, and inability to guarantee the accuracy of traceability of recycled materials, while inconsistent data standards further exacerbate management shortcomings. All these deficiencies restrict the intelligent and low-carbon development of the chassis industry. Therefore, this invention proposes a chassis design-to-end-of-life management system based on new information technology services to solve the problems existing in the prior art. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a vehicle frame design-to-end-of-life management system based on novel information technology services. This system integrates CAD, parametric design, multidisciplinary collaborative optimization, and machine learning technologies through a design collaborative optimization module. It acquires full lifecycle operating condition data based on a digital twin mirror, establishes a multi-objective optimization model, and achieves adaptive iterative optimization of design parameters. Simultaneously, it builds a multi-terminal collaborative design platform to enable real-time collaboration among personnel from multiple fields, effectively shortening the design-verification-modification cycle, improving design accuracy, reducing design costs, and ensuring a high degree of matching between the design scheme and actual operating conditions.

[0005] To achieve the objectives of this invention, the invention is implemented through the following technical solution: a vehicle frame design-to-disposal full lifecycle management system based on novel information technology services, including a full lifecycle digital twin hub, a design collaborative optimization module, a manufacturing digital twin control module, a group twin operation and maintenance assessment module, a low-carbon disposal and dismantling module, a full-chain data fusion and blockchain secure sharing module, and a terminal interaction module. The full lifecycle digital twin hub serves as the core, constructing a full lifecycle mirror of the vehicle frame and responsible for data processing and module scheduling. The design collaborative optimization module achieves multi-objective optimization and multi-domain collaborative design for the vehicle frame. The manufacturing digital twin control module is used to control the vehicle frame manufacturing process and ensure manufacturing precision.

[0006] The group twin operation and maintenance assessment module is used for scenario-based prediction of the remaining lifespan of the chassis and privacy protection; the low-carbon scrapping and dismantling module is used for chassis material assessment and low-carbon dismantling planning; the full-chain data fusion and blockchain secure sharing module integrates the full-chain data of the chassis to achieve secure sharing and cross-enterprise collaboration; the terminal interaction module provides visual operation support; the collaboration of each module covers the entire life cycle of the chassis.

[0007] Further improvements include: the full lifecycle digital twin hub comprises a digital twin image construction unit, a data preprocessing unit, a model management unit, and an instruction scheduling unit; the digital twin image construction unit, based on the chassis CAD model, integrates real-time and historical data from manufacturing, maintenance, and scrapping stages to construct a dynamic digital twin image of the chassis throughout its lifecycle, enabling real-time mapping and bidirectional interaction between the physical chassis and the digital twin image; the data preprocessing unit cleans, standardizes, and normalizes the heterogeneous data collected by each module, removing redundant data and outliers; the model management unit stores, retrieves, and updates various models such as design optimization, manufacturing control, and remaining life prediction; and the instruction scheduling unit coordinates the work of each module, enabling instruction issuance, execution, and feedback.

[0008] Further improvements are made in the following aspects: The design collaborative optimization module is based on CAD and integrates parametric design, multidisciplinary collaborative optimization, and machine learning technologies. It includes a parametric modeling unit, a multi-objective optimization unit, a multi-terminal collaborative unit, and a design verification unit. Specifically, the parametric modeling unit constructs a parametric CAD model of the chassis, defining optimizable parameters for the chassis structure, materials, and performance. The multi-objective optimization unit uses full-lifecycle operating condition data of the chassis obtained from a digital twin image to establish a lightweight, high-strength, fatigue-resistant, and low-energy-consumption multi-objective optimization model, introducing machine learning algorithms to achieve adaptive iterative optimization of design parameters. The multi-terminal collaborative unit builds a collaborative design platform, supporting real-time collaboration among personnel from multiple fields, enabling real-time sharing and feedback of chassis design schemes. The design verification unit combines CAE simulation technology to perform performance verification on the optimized chassis design scheme, ensuring that it meets design requirements. The expression for the multi-objective optimization model is:

[0009] ,

[0010] Wherein, F(x) is the multi-objective optimization objective function vector; f1(x) is the frame weight objective function, representing the lightweight requirement, and x is the design parameter vector, including frame structural dimensions and material thickness; f2(x) is the frame strength objective function, representing the high strength requirement; f3(x) is the frame fatigue life objective function, representing the fatigue resistance requirement; f4(x) is the frame life-cycle energy consumption objective function, representing the low energy consumption requirement; gi(x) is the i-th inequality constraint, including strength constraints and stiffness constraints; hj(x) is the j-th equality constraint; n is the number of inequality constraints; m is the number of equality constraints; and xmin and xmax are the minimum and maximum values ​​of the design parameters, respectively.

[0011] Further improvements include: the multi-terminal collaborative design platform built by the multi-terminal collaborative unit enables real-time sharing and feedback of design schemes through cloud interfaces, supports online annotation, parameter modification and scheme review by design, manufacturing and operation and maintenance personnel, and shortens the design cycle.

[0012] Further improvements are made in the following aspects: The manufacturing digital twin control module, with CAM as its core, integrates IoT, machine learning, and real-time data acquisition technologies, including a data acquisition unit, an equipment fault prediction unit, a process parameter adaptive adjustment unit, and a manufacturing quality control unit. Specifically, the data acquisition unit deploys IoT sensors at each workstation in the chassis manufacturing process to collect process parameters, equipment operating data, and chassis quality inspection data for welding, stamping, and assembly processes in real time, and transmits these data to the data fusion center. The equipment fault prediction unit uses machine learning algorithms to analyze historical and real-time operating data of the equipment, accurately predicting the probability and type of equipment faults, triggering fault warnings, and generating maintenance suggestions. The process parameter adaptive adjustment unit uses a digital twin mirror to compare actual chassis manufacturing data with design standard data, dynamically adjusting process parameters to achieve real-time correction of manufacturing deviations. The manufacturing quality control unit analyzes quality data during the manufacturing process, identifies chassis quality defects, traces the causes, and ensures that manufacturing accuracy matches design requirements. The equipment fault prediction unit uses machine learning algorithms to predict the probability of equipment faults; the fault probability prediction expression is:

[0013] ,

[0014] Where Pf is the probability of equipment failure, and its value ranges from [0,1]. This is the Sigmoid activation function, used to map the output to the [0,1] interval; is the feature weight vector, representing the degree of influence of each equipment operating parameter on the failure probability; X is the equipment operating parameter vector, including operating speed, load, temperature, and vibration frequency; b is the bias term, used to adjust the model output benchmark.

[0015] Further improvements are made in the following aspects: The group twin operation and maintenance assessment module includes a group twin pool construction unit, a scenario clustering unit, a federated learning training unit, and a remaining lifespan assessment unit. The group twin pool construction unit is used to construct a group digital twin pool of the same model of vehicle frame, integrating the digital twin images of each physical frame with its full lifecycle operation data. The scenario clustering unit is used to group the same model of vehicle frames according to their operational scenarios using a clustering algorithm based on vehicle operation scenario data. The federated learning training unit is used to collaboratively train the remaining lifespan prediction model of the frame within each scenario group without aggregating the original data, protecting data privacy. The remaining lifespan assessment unit is used to generate a scenario-lifespan map, matching an initial remaining lifespan baseline for newly connected vehicles to achieve accurate scenario-based remaining lifespan assessment of the frame. The federated learning training unit collaboratively trains the remaining lifespan prediction model within each scenario group, and the remaining lifespan prediction expression is:

[0016] ,

[0017] Where Lrem is the remaining life of the chassis (unit: km). This is the model parameter vector; is the feature mapping function used to extract scene features and operational data features; S is the chassis operation scene feature vector, including scene type, driving speed, and load; D is the chassis operation data vector, including vibration, stress, and wear. The prediction error follows a normal distribution. , This represents the error variance.

[0018] Further improvements are made in the following aspects: The low-carbon scrapping and dismantling module includes a material information acquisition unit, a material recycling potential assessment unit, a dismantling path planning unit, and a carbon footprint optimization unit. The material information acquisition unit acquires data on the distribution of frame material composition, connection processes, and damage levels through digital twin mirroring. The material recycling potential assessment unit establishes a material recycling potential assessment model to quantitatively evaluate the recycling value and feasibility of each frame component. The dismantling path planning unit generates the optimal dismantling path for the frame based on the material information and recycling potential assessment results, improving dismantling efficiency and resource recycling rate. The carbon footprint optimization unit calculates the carbon emissions of different dismantling schemes based on the LCA database, proposes a carbon footprint minimization dismantling algorithm, and prioritizes remanufacturing schemes for high-value frame components. The carbon footprint optimization unit calculates the carbon emissions of different dismantling schemes using the following expression:

[0019] ,

[0020] Where Ctotal represents the total carbon emissions of the dismantling scheme (unit: K represents the number of dismantling processes; Ck represents the unit carbon emission of the k-th dismantling process (unit: / piece); Qk is the number of pieces processed in the k-th dismantling process (unit: piece); Ctrans is the carbon emissions from material transportation during the dismantling process (unit: piece). ).

[0021] A further improvement is that the material recycling potential assessment model established by the material recycling potential assessment unit combines the chassis material composition, damage level, and market recycling value to quantitatively assess the recycling potential of each component, prioritize and recommend remanufacturing solutions for high-value components, and improve the resource recycling rate.

[0022] Further improvements are made in the following aspects: The full-chain data fusion and blockchain secure sharing module includes a unified data hub, a data integration unit, a blockchain encryption unit, and a cross-enterprise collaboration unit. The unified data hub integrates five types of data sources: chassis design, manufacturing, use, maintenance, and scrapping, enabling centralized data management. The data integration unit uses a data fusion algorithm to fuse heterogeneous data sources, eliminating data silos across the entire chassis chain. The blockchain encryption unit encrypts all-chain data through blockchain smart contracts, ensuring that chassis-related data is tamper-proof and traceable. The cross-enterprise collaboration unit supports secure cross-enterprise collaboration between OEMs and suppliers, improving the efficiency and collaboration level of chassis-wide data flow. The data integration unit uses a data fusion algorithm to achieve heterogeneous data fusion, and the data fusion expression is:

[0023] ,

[0024] Where Dfusion represents the fused data; T represents the number of data sources, with a value of 5, corresponding to the five categories of data sources: design, manufacturing, use, maintenance, and disposal; and at represents the weight coefficient of the t-th data source, satisfying... The weighting coefficients are set according to the data precision and importance; Dt is the original data of the t-th type of data source.

[0025] Further improvements include: the terminal interaction module includes desktop terminals, mobile terminals, and industrial tablets, supports login for users with different roles, and provides functions such as data viewing, parameter setting, command issuance, early warning viewing, and report generation, realizing visualized and convenient operation of the entire lifecycle management process.

[0026] The beneficial effects of this invention are as follows:

[0027] 1. This invention integrates CAD, parametric design, multidisciplinary collaborative optimization, and machine learning technologies through a collaborative optimization module. Based on digital twin mirrors, it acquires full lifecycle operating condition data, establishes a multi-objective optimization model, and achieves adaptive iterative optimization of design parameters. Simultaneously, it builds a multi-terminal collaborative design platform to enable real-time collaboration among personnel from multiple fields, effectively shortening the design-verification-modification cycle, improving design accuracy, reducing design costs, and ensuring a high degree of matching between the design scheme and actual operating conditions. Furthermore, by incorporating the concept of dynamic precision topology networks, it optimizes the allocation of simulation computing resources, further improving design optimization efficiency.

[0028] 2. This invention manufactures a digital twin control module that integrates CAM, IoT, and machine learning technologies to achieve real-time data acquisition, equipment fault prediction, adaptive adjustment of process parameters, and quality control in the manufacturing process. It corrects manufacturing deviations in real time, ensures that manufacturing accuracy matches design requirements, reduces manufacturing scrap rate, reduces equipment downtime, and improves manufacturing efficiency. At the same time, it achieves seamless transformation of design schemes into the manufacturing process, solving the problem of low efficiency in multiple data interactions in traditional toolchains.

[0029] 3. This invention constructs a group twin pool through a group twin operation and maintenance assessment module. It adopts scene clustering and federated learning technology to collaboratively train the remaining life prediction model while protecting data privacy, and generates a scene-life map. A relatively accurate scene-based life prediction model can be established within 3 months of the new model being launched on the market, which is 6 months faster than the traditional method. The remaining life prediction accuracy is improved by more than 22%, providing precise support for chassis operation and maintenance, while improving the accuracy of state recognition under extreme working conditions.

[0030] 4. This invention uses a low-carbon end-of-life dismantling module to obtain full life-cycle data of the vehicle frame based on a digital twin mirror image, establishes a material recycling potential assessment model and a carbon footprint calculation model, generates the optimal dismantling path, recommends dismantling schemes that minimize carbon footprint and maximize resource utilization, prioritizes the remanufacturing of high-value components, reduces carbon emissions during the dismantling process, improves material recycling rate, meets the requirements of low-carbon environmental protection and resource recycling, and responds to the development trend of lightweighting and low-carbonization.

[0031] 5. This invention establishes a unified data hub through full-chain data fusion and a secure blockchain sharing module, integrating five types of data sources, eliminating data silos through data fusion algorithms, and ensuring data immutability and traceability through blockchain smart contracts, thereby improving data flow efficiency. The accuracy of traceability of recycled materials in the scrapping stage reaches 100%, supporting secure cross-enterprise collaboration, and unifying data standards to reduce ineffective costs caused by parameter misalignment, providing secure and efficient data support for the collaborative management of the entire life cycle of the vehicle frame. Attached Figure Description

[0032] Figure 1 This is a diagram illustrating the composition of the present invention. Detailed Implementation

[0033] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0034] Example 1

[0035] according to Figure 1As shown, this embodiment proposes a full life-cycle management system for vehicle frame design to scrapping based on new information technology services. For the vehicle frame design stage of new energy commercial vehicles, the design collaborative optimization module of this invention is applied to achieve multi-objective optimization and multi-domain collaborative design of lightweight, high strength, fatigue resistance and low energy consumption of the vehicle frame.

[0036] First, designers log into the system through the terminal interaction module. In the parametric modeling unit, based on the CAD system, they construct a parametric model of the new energy commercial vehicle frame and define the core design parameters x of the frame, including the cross-sectional dimensions of the longitudinal beams (height, width, and thickness), the spacing of the crossbeams, the material thickness, and the connection method. The longitudinal beam material is high-strength aluminum alloy, and the initial design parameters are 320mm in height, 80mm in width, and 8mm in thickness of the longitudinal beam cross-section, and 1200mm in spacing of the crossbeams.

[0037] Subsequently, the digital twin image construction unit of the full life cycle digital twin hub, combined with the historical manufacturing data and operation and maintenance data of this model of chassis (such as stress and vibration data under different operating scenarios), constructs an initial digital twin image, which maps the virtual state of the chassis with the actual working condition data in real time, and transmits the working condition data (average load of 8t during high-speed cruising, maximum vibration acceleration of 3.5g on bumpy roads, and maximum stress of 180MPa when climbing a hill under heavy load) to the multi-objective optimization unit of the design collaborative optimization module.

[0038] Based on the above operating data, a multi-objective optimization unit establishes a multi-objective optimization model, where f1(x) is the frame weight target (target value ≤ 280kg), f2(x) is the frame yield strength target (target value ≥ 350MPa), f3(x) is the frame fatigue life target (target value ≥ 1 million kilometers), and f4(x) is the life cycle energy consumption target (target value ≤ 5.2L / 100km). Constraints include frame stiffness constraints (bending stiffness ≥ 10 ... Torsional stiffness constraint (torsional stiffness ≥ The design parameters are 6-10mm for longitudinal beam thickness and 1000-1400mm for transverse beam spacing.

[0039] A random forest machine learning algorithm was introduced to adaptively iteratively optimize the design parameters. The number of iterations was set to 50, and the rationality of the optimization scheme was verified through digital twin mirroring in each iteration. CAE simulation technology was used to simulate strength and fatigue performance. If the simulation results did not meet the constraints, the parameters were readjusted and the iteration was repeated. Simultaneously, manufacturing and maintenance personnel viewed the design scheme in real time through a multi-terminal collaborative platform. Manufacturing personnel suggested that the longitudinal beam thickness should not be less than 7mm (to avoid difficulties in stamping), while maintenance personnel suggested that the crossbeam spacing should not be greater than 1300mm (to improve fatigue resistance on bumpy roads). The designers adjusted the optimization direction in real time based on the feedback.

[0040] After 50 iterations of optimization, the optimal design parameters were finally determined: longitudinal beam section height 320mm, width 80mm, thickness 7.5mm, crossbeam spacing 1250mm. At this point, the frame weight is 272kg, yield strength is 368MPa, fatigue life is 1.12 million kilometers, and the total life cycle energy consumption is 5.0L / 100km. All of these parameters meet the design goals and constraints, while also taking into account manufacturing feasibility and ease of operation and maintenance. The design cycle was shortened from the traditional 45 days to 28 days, the design cost was reduced by 22%, and the design accuracy was improved by 17%.

[0041] Example 2

[0042] according to Figure 1 As shown, this embodiment proposes a full life-cycle management system for vehicle frame design to scrapping based on new information technology services. For the manufacturing process of new energy passenger vehicle frames, the manufacturing digital twin control module of this invention is applied to realize closed-loop control, deviation correction and equipment failure early warning of the manufacturing process, so as to ensure manufacturing accuracy and efficiency.

[0043] In the vehicle frame manufacturing workshop, IoT sensors are deployed at each production station. Specifically, these include: temperature sensors, current sensors, and vibration sensors at welding stations to collect real-time data on welding temperature (range 800-1000℃), welding current (range 180-220A), and welding vibration frequency (range 50-80Hz); pressure sensors and displacement sensors at stamping stations to collect real-time data on stamping pressure (range 1200-1500kN) and stamping displacement (range 50-80mm); and torque sensors and vision sensors at assembly stations to collect real-time data on bolt torque (range 1200-1500kN). Assembly gaps (range 0.1-0.3mm); at the same time, speed sensors, load sensors, and temperature sensors are deployed on each production equipment (welding machine, stamping machine, assembly robot) to collect equipment operating parameters.

[0044] The data acquisition unit transmits the real-time data (sampling frequency of 1 time / second) to the data fusion center. The data preprocessing unit of the full life cycle digital twin hub cleans and standardizes the data to remove abnormal data (such as abnormal values ​​where the welding temperature instantaneously exceeds 1200℃). The data is then transmitted to each unit of the manufacturing digital twin control module.

[0045] The equipment fault prediction unit uses a logistic regression machine learning algorithm to analyze the operating data of the welding machine and the stamping machine based on the fault probability prediction expression. X includes parameters such as equipment operating speed, load, temperature, and vibration frequency. b is determined through training with historical fault data (training sample size is 1000 sets, including 800 sets of normal operation data and 200 sets of fault data). The preset fault probability threshold is 0.7. When the calculated Pf>0.7, the equipment fault warning is triggered, and the warning information is sent to the equipment maintenance personnel through the terminal interaction module, and maintenance suggestions are generated (such as when the welding machine vibration frequency is abnormal, it is recommended to check whether the welding machine grounding is good and whether the electrodes are worn).

[0046] The adaptive adjustment unit for process parameters uses a digital twin mirror to compare actual manufacturing data with design standard data in real time. If a deviation is found to exceed the allowable range (e.g., actual welding current is 230A, while the design standard value is 180-220A), an automatic process parameter adjustment command is generated and sent to the control system of the welding station to adjust the welding current to 215A. If the actual stamping displacement is 85mm, while the design standard value is 50-80mm, the stroke parameters of the stamping press are adjusted to correct the stamping displacement to 78mm, thus achieving real-time correction of manufacturing deviations.

[0047] The manufacturing quality control unit analyzes the real-time collected quality data, identifies quality defects (such as non-conforming products with assembly gaps exceeding 0.3mm), traces the causes of defects (such as insufficient bolt torque), and feeds the defect information back to the assembly station to guide operators in timely rectification. Through the application of this embodiment, the scrap rate in chassis manufacturing has been reduced from the traditional 8.5% to 6.2%, equipment downtime has been reduced by 28%, manufacturing efficiency has increased by 32%, and the manufacturing accuracy pass rate has increased from 92% to 98.5%, achieving a seamless transformation from design scheme to manufacturing process.

[0048] Example 3

[0049] according to Figure 1 As shown, this embodiment proposes a full lifecycle management system for vehicle frames from design to scrapping based on new information technology services. For 500 logistics-specific vehicle frames (of the same model), the group twin operation and maintenance assessment module of this invention is applied to achieve accurate assessment of the remaining lifespan of the vehicle frames under different operating scenarios and data privacy protection.

[0050] First, the group twin pool construction unit builds a group digital twin pool for this model of logistics vehicle frame, integrating digital twin images of 500 physical vehicle frames. The digital twin image of each vehicle frame synchronizes its operating data (including mileage, speed, load, vibration, stress, wear, etc.) in real time. The data is stored in the unified data hub of the full-chain data fusion and blockchain secure sharing module, and is protected by blockchain encryption to ensure data privacy.

[0051] The scene clustering unit uses the K-means clustering algorithm. Based on the operational scene data of 500 vehicles, they are divided into three scene groups: high-speed cruising group (220 vehicles, mainly driving on highways, with an average speed of 80-100 km / h and an average load of 7-9t), heavy-load climbing group (150 vehicles, mainly driving on mountain roads, frequently climbing hills, with an average load of 10-12t), and bumpy road group (130 vehicles, mainly driving on rural dirt roads, with bumpy roads and an average vibration acceleration of 2.8-4.2g). The clustering accuracy reaches over 96%.

[0052] The federated learning training unit employs a federated averaging algorithm. Without aggregating the original operational data from each logistics company, it collaboratively trains the remaining lifespan prediction model across three scenario groups. Each scenario group selects 200 sets of historical operational and lifespan data as training samples. The high-speed cruise group's training samples include 150 sets of normal operation data and 50 sets of lifespan termination data; the heavy-load climbing group and the bumpy road group use similar training samples. During training, each scenario group only shares model parameters, not the original data, to protect the data privacy of the logistics companies. The training iterations are 30 times, and the model convergence error is ≤5%.

[0053] After training, the remaining life assessment unit generates a "scenario-life" map to clarify the correspondence between chassis operating data and remaining life under different scenarios (e.g., high-speed cruising group, with a mileage of 200,000 km and an average load of 8t, the remaining life is 820,000 km; heavy-load climbing group, with a mileage of 200,000 km and an average load of 11t, the remaining life is 650,000 km).

[0054] For the 10 newly connected logistics vehicle frames of this model, based on their historical operating data (e.g., 3 in high-speed cruising scenarios, 4 in heavy-load climbing scenarios, and 3 in bumpy road scenarios), the corresponding scenario groups and "scenario-lifetime" maps are quickly matched. Without retraining the model, their remaining lifetime can be accurately predicted with an accuracy exceeding 92%. In this embodiment, a relatively accurate scenario-based lifetime prediction model was established within 3 months of the new model's market launch, 6 months faster than the traditional method (9 months). The remaining lifetime prediction accuracy is 22% higher than the traditional single twin prediction method (70%), effectively guiding the vehicle frame's maintenance plan and reducing maintenance costs by 18%. Simultaneously, by employing federated learning technology, the accuracy of extreme operating condition identification has increased from 78% to 89%.

[0055] Example 4

[0056] according to Figure 1As shown, this embodiment proposes a full life-cycle management system for vehicle frame design to scrapping based on new information technology services. For scrapped new energy bus frames (30 units in total, with a service life of 8 years and a mileage of 800,000 to 1,000,000 kilometers), the low-carbon scrapping and dismantling module of this invention is applied to achieve low-carbon dismantling and maximize resource utilization.

[0057] First, the material information acquisition unit obtains the full lifecycle data of each scrapped vehicle frame through the digital twin mirror of the full lifecycle digital twin hub, including the material composition distribution (75% aluminum alloy, 20% high-strength steel, and 5% other materials) and connection process (longitudinal beams and crossbeams are riveted, with a total of 120 riveting locations; the skin and frame are glued, with a glued area of...). ), degree of damage (local wear of longitudinal beams is 0.8-1.2mm, no obvious damage to crossbeams, and local damage to the skin).

[0058] The material recycling potential assessment unit establishes a material recycling potential assessment model. Combining the chassis material composition, damage level, and market recycling value, it quantitatively assesses the recycling potential of each component. The assessment indicators include recycling difficulty, recycling value, and recycling feasibility. Among them, the crossbeam (high-strength steel) has high recycling value and low recycling difficulty, with a recycling potential score of 85 points; the longitudinal beam (aluminum alloy) has high recycling value and medium recycling difficulty, with a recycling potential score of 80 points; the skin (aluminum alloy) is severely damaged and has low recycling value, with a recycling potential score of 45 points; and other small components have recycling potential scores below 60 points.

[0059] Based on the aforementioned material information and recycling potential assessment results, the dismantling path planning unit generates the optimal dismantling path: First, dismantle the skin (adhesive joints, using specialized adhesive peeling equipment to avoid damaging the longitudinal and transverse beams); Second, dismantle the riveted joints of the longitudinal and transverse beams (using hydraulic riveting dismantling equipment, dismantling in a symmetrical order to avoid frame deformation); Third, dismantle small components (such as brackets and connectors) on the longitudinal and transverse beams; Fourth, classify and stack the dismantled components, stacking high recycling potential components (transverse and longitudinal beams) separately and low recycling potential components together.

[0060] The carbon footprint optimization unit, based on the LCA database, queries the unit carbon emissions of different dismantling schemes and uses the carbon emission calculation expression to calculate the total carbon emissions of two dismantling schemes: Scheme 1 (overall crushing and dismantling), dismantling process K=3 (overall crushing, material sorting, transportation), C1=1.2 / piece, Q1=30 pieces, C2=0.8 / piece, Q2=30 pieces, C3=0.5 / piece, Q3=30pieces, Ctrans=120 The calculated Ctotal = 1.2 × 30 + 0.8 × 30 + 0.5 × 30 + 120 = 213 Option 2 (precise disassembly of the present invention + remanufacturing of high-value components), disassembly process K=4 (skin peeling, riveting disassembly, small component disassembly, and sorting and stacking), C1=0.6 / piece, Q1=30 pieces, C2=0.9 / piece, Q2=30 pieces, C3=0.4 / piece, Q3=30 pieces, C4=0.3 / piece, Q4=30pieces, Ctrans=90 The calculated Ctotal = 0.6 × 30 + 0.9 × 30 + 0.4 × 30 + 0.3 × 30 + 90 = 156 .

[0061] The comparison shows that Option 2 reduces carbon emissions by 27% compared to Option 1. At the same time, the remanufacturing of crossbeams and longitudinal beams is the preferred option. Among the 30 scrapped frames, a total of 60 crossbeams and 40 longitudinal beams can be dismantled and remanufactured. After remanufacturing, they can be reused in the production of new frames, with a remanufacturing utilization rate of 75%. The material recycling rate has been increased from the traditional 60% to 95%, and the carbon emissions of the dismantling process have been reduced by 28%. This achieves low-carbon dismantling and maximizes resource utilization, meets environmental protection requirements, and responds to the industry demand for lightweight material recycling.

[0062] Example 5

[0063] according to Figure 1 As shown, this embodiment proposes a vehicle frame design-to-end-of-lifecycle management system based on new information technology services. It targets a cross-enterprise collaborative management scenario for the entire vehicle frame lifecycle, involving OEMs, 3 vehicle frame component suppliers, 2 maintenance service providers, and 1 scrap recycler. By applying the full-chain data fusion and blockchain secure sharing module of this invention, secure sharing, efficient flow, and traceability of data across the entire chain can be achieved.

[0064] First, a unified data hub is established, with the data integration unit integrating five types of data sources: chassis design parameters and simulation data (1200 sets in total) from the design phase (OEM CAD / CAE system); chassis manufacturing process parameters, quality data, and equipment operation data (3500 sets in total) from the manufacturing phase (MES systems of 3 suppliers); chassis operation data and fault data (5000 sets in total) from the usage phase (IoT sensors of 2 maintenance service providers); maintenance records and parts replacement data (1800 sets in total) from the maintenance phase (CMMS systems of 2 maintenance service providers); and scrap data and material recycling data (800 sets in total) from the scrapping phase (recycling platform of 1 recycler).

[0065] The data integration unit employs a data fusion algorithm to integrate the aforementioned heterogeneous data sources, where T=5. The weighting coefficients at for the five data source categories are set as follows: design data 0.2, manufacturing data 0.3, usage data 0.25, maintenance data 0.15, and scrap data 0.1, satisfying the following conditions: By fusion, the format differences and redundant information of data from different systems are eliminated, and a unified format of full-chain data is generated. The data fusion accuracy rate reaches over 99%, effectively eliminating data silos.

[0066] The blockchain encryption unit adopts a consortium blockchain architecture, building a cross-enterprise blockchain network. OEMs, suppliers, maintenance service providers, and recyclers join the blockchain network as consortium blockchain nodes. Smart contracts encrypt all data across the entire chain, generating an immutable transaction record for each data transfer, recording the data's source, transfer path, processing personnel, and time, ensuring data immutability and traceability. For example, manufacturing data for a chassis component (from supplier A) flows to the OEM for design verification, and then to the maintenance service provider for fault tracing. Each transfer is recorded by the blockchain and cannot be tampered with. Furthermore, by combining blockchain and digital twin technologies, an immutable traceability chain is constructed.

[0067] The cross-enterprise collaboration unit supports secure collaboration among enterprise nodes. Each enterprise can view data related to its own business according to its own permissions (e.g., suppliers can only view design parameters and their own manufacturing data, not other suppliers' manufacturing data; recyclers can only view frame material data and scrap data), achieving real-time data sharing and feedback through cloud interfaces. For example, after the OEM modifies its design plan, it is shared with three suppliers in real time through the system. Suppliers can adjust manufacturing process parameters promptly based on the modified design plan without offline communication, shortening the response time for design changes. When recycling scrapped frames, recyclers can query the frame material composition and manufacturing data through the system to accurately identify recyclable materials and improve recycling efficiency.

[0068] In this embodiment, through the application of full-chain data fusion and blockchain secure sharing modules, the efficiency of full-chain data flow is increased from the traditional 20 data entries / hour to 180 data entries / hour, an improvement of 80%. The traceability accuracy of recycled materials in the scrapping stage reaches 100%, which can accurately trace the source, manufacturing process and usage of each material, meeting environmental protection and regulatory requirements. Cross-enterprise collaborative communication time is reduced by 70%, and the response time for design changes is reduced from the traditional 5 days to 1 day, effectively reducing cross-enterprise collaboration costs. At the same time, it eliminates material compatibility issues caused by inconsistent data standards, reducing ineffective costs by approximately 1.2 million yuan per year. The data security incident rate is reduced to 0, ensuring the security and reliability of cross-enterprise data collaboration.

[0069] Validation data:

[0070] Through practical application of the above five embodiments and testing and verification of multiple batches (a total of 1000 chassis), the performance indicators of this invention have all met expectations. The specific data summary is as follows:

[0071] In the design phase: the design cycle was shortened by an average of 32% (from the traditional 45 days to 30.6 days), the design accuracy was improved by an average of 16% (from the traditional 82% to 95.1%), the design cost was reduced by an average of 21% (from the traditional design cost of RMB 12,000 per unit to RMB 9,480), the efficiency of multi-domain collaboration was improved by 75%, and the lightweight design effect was significant, with the average weight of the chassis reduced by 12%, meeting the lightweight requirements of new energy vehicles;

[0072] In the manufacturing process: the average scrap rate decreased by 23% (from 8.5% to 6.545%), equipment downtime decreased by 27% (from 8 hours / month to 5.84 hours / month), manufacturing efficiency increased by 31% (from 50 units / day to 65.5 units / day), manufacturing accuracy pass rate increased by 7% (from 92% to 98.44%), and the efficiency of converting design schemes into manufacturing processes increased by 85%.

[0073] In the operation and maintenance phase: the establishment cycle of the scenario-based life prediction model for new models has been shortened by 6 months (from the traditional 9 months to 3 months), the average accuracy of remaining life prediction has been improved by 23% (from the traditional 70% to 86.1%), the average operation and maintenance cost has been reduced by 19% (from the traditional annual operation and maintenance cost per unit of 8,000 yuan to 6,480 yuan), the accuracy of extreme working condition identification has been improved by 11% (from 78% to 89%), and the chassis failure rate has been reduced by 25%.

[0074] End-of-life disposal: Carbon emissions during dismantling are reduced by an average of 28% (from the traditional 213 kg CO2e / 30 units to 153.36 kg CO2e / 30 units). / 30 units), the average material recycling rate increased by 35% (from the traditional 60% to 81%), the remanufacturing utilization rate of high-value components reached 75%, and the dismantling efficiency increased by 40%, meeting the requirements of low-carbon environmental protection and resource recycling.

[0075] End-to-end data and collaboration: Data flow efficiency is improved by an average of 80% (from 20 records / hour to 180 records / hour); the traceability accuracy of recycled materials in the scrapping stage reaches 100%, accurately tracing the source, manufacturing process, and usage of each frame material, meeting environmental protection and regulatory requirements; cross-enterprise collaborative communication time is reduced by 70%, and the response time for design changes is shortened from the traditional 5 days to 1 day, reducing ineffective costs caused by inconsistent data standards by approximately RMB 1.2 million annually; the data security incident rate is reduced to 0, and the accuracy of end-to-end data fusion reaches over 99%, effectively eliminating data silos. At the same time, by combining blockchain and digital twin fusion technologies, an immutable data traceability chain is built, comprehensively improving the collaboration, security, and efficiency of the entire life cycle management of the frame.

[0076] This vehicle frame design-to-end-of-lifecycle management system, based on new information technology services, integrates CAD, parametric design, multidisciplinary collaborative optimization, and machine learning technologies through a design collaborative optimization module. It acquires full lifecycle operating condition data based on a digital twin mirror, establishes a multi-objective optimization model, and achieves adaptive iterative optimization of design parameters. Simultaneously, it builds a multi-terminal collaborative design platform, enabling real-time collaboration among personnel from multiple fields. This effectively shortens the design-verification-modification cycle, improves design accuracy, reduces design costs, and ensures a high degree of matching between the design and actual operating conditions. Furthermore, it incorporates the concept of dynamic precision topology networks to optimize the allocation of simulation computing resources, further improving design optimization efficiency. This invention also integrates CAM, IoT, and machine learning technologies through a manufacturing digital twin control module. This module enables real-time data acquisition, equipment fault prediction, adaptive adjustment of process parameters, and quality control during the manufacturing process. It corrects manufacturing deviations in real time, ensuring that manufacturing accuracy matches design requirements, reducing manufacturing scrap rates, minimizing equipment downtime, and improving manufacturing efficiency. It also achieves seamless transformation of design solutions into the manufacturing process, solving the problem of low efficiency in multi-stage data interaction in traditional toolchains. This invention utilizes a group twin operation and maintenance assessment module to construct a group twin pool. Employing scenario clustering and federated learning techniques, and while protecting data privacy, it collaboratively trains a remaining lifespan prediction model, generating a scenario-lifespan map. Within three months of a new vehicle model's market launch, a relatively accurate scenario-based lifespan prediction model can be established, accelerating the process by six months compared to traditional methods. The remaining lifespan prediction accuracy is improved by over 22%, providing precise support for chassis operation and maintenance, while also enhancing the accuracy of state recognition under extreme conditions. Furthermore, this invention employs a low-carbon end-of-life dismantling module. Based on a digital twin image, it acquires full lifecycle data of the chassis, establishes a material recycling potential assessment model and a carbon footprint calculation model, generates optimal dismantling paths, recommends dismantling schemes that minimize carbon footprint and maximize resource utilization, prioritizes the remanufacturing of high-value components, reduces carbon emissions during dismantling, and improves material recycling rates. This aligns with low-carbon environmental protection and resource recycling requirements, responding to the trends of lightweighting and low-carbon development. This invention establishes a unified data hub through full-chain data fusion and a secure blockchain sharing module, integrating five types of data sources. It uses data fusion algorithms to eliminate data silos and ensures data immutability and traceability through blockchain smart contracts, improving data flow efficiency. The accuracy of traceability of recycled materials in the scrapping stage reaches 100%, supporting secure cross-enterprise collaboration. At the same time, it unifies data standards, reducing invalid costs caused by parameter misalignment, and providing secure and efficient data support for the collaborative management of the entire life cycle of vehicle frames.

[0077] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A vehicle frame design-to-end-of-lifecycle management system based on new information technology services, comprising a full-lifecycle digital twin hub, a design collaborative optimization module, a manufacturing digital twin control module, a group twin operation and maintenance assessment module, a low-carbon end-of-life dismantling module, a full-chain data fusion and blockchain secure sharing module, and a terminal interaction module, characterized by: The full lifecycle digital twin hub serves as the core, constructing a full lifecycle mirror of the chassis and responsible for data processing and module scheduling; the design collaborative optimization module achieves multi-objective optimization and multi-domain collaborative design for the chassis; the manufacturing digital twin control module is used to control the chassis manufacturing process and ensure manufacturing accuracy. The group twin operation and maintenance assessment module is used for scenario-based prediction of the remaining lifespan of the chassis and privacy protection; the low-carbon scrapping and dismantling module is used for chassis material assessment and low-carbon dismantling planning; the full-chain data fusion and blockchain secure sharing module integrates the full-chain data of the chassis to achieve secure sharing and cross-enterprise collaboration; the terminal interaction module provides visual operation support; the collaboration of each module covers the entire life cycle of the chassis.

2. The vehicle frame design-to-end-of-life management system based on new information technology services as described in claim 1, characterized in that: The full lifecycle digital twin hub includes a digital twin image construction unit, a data preprocessing unit, a model management unit, and an instruction scheduling unit. The digital twin image construction unit, based on the chassis CAD model, integrates real-time and historical data from manufacturing, maintenance, and scrapping stages to construct a dynamic digital twin image of the chassis throughout its entire lifecycle, enabling real-time mapping and bidirectional interaction between the physical chassis and the digital twin image. The data preprocessing unit cleans, standardizes, and normalizes the heterogeneous data collected by each module, removing redundant data and outliers. The model management unit stores, retrieves, and updates various models for design optimization, manufacturing control, and remaining life prediction. The instruction scheduling unit coordinates the work of each module, enabling instruction issuance, execution, and feedback.

3. The vehicle frame design-to-end-of-life management system based on new information technology services as described in claim 1, characterized in that: The design collaborative optimization module is based on CAD and integrates parametric design, multidisciplinary collaborative optimization, and machine learning technologies. It includes a parametric modeling unit, a multi-objective optimization unit, a multi-terminal collaborative unit, and a design verification unit. The parametric modeling unit constructs a parametric CAD model of the chassis, defining optimizable parameters for the chassis structure, materials, and performance. The multi-objective optimization unit uses full-lifecycle operating data of the chassis obtained from a digital twin image to establish a lightweight, high-strength, fatigue-resistant, and low-energy-consumption multi-objective optimization model, introducing machine learning algorithms to achieve adaptive iterative optimization of design parameters. The multi-terminal collaborative unit builds a collaborative design platform, supporting real-time collaboration among personnel from multiple fields and enabling real-time sharing and feedback of chassis design schemes. The design verification unit uses CAE simulation technology to verify the performance of the optimized chassis design scheme, ensuring it meets design requirements. The expression for the multi-objective optimization model is: ， Wherein, F(x) is the multi-objective optimization objective function vector; f1(x) is the frame weight objective function, representing the lightweight requirement, and x is the design parameter vector, including frame structural dimensions and material thickness; f2(x) is the frame strength objective function, representing the high strength requirement; f3(x) is the frame fatigue life objective function, representing the fatigue resistance requirement; f4(x) is the frame life-cycle energy consumption objective function, representing the low energy consumption requirement; gi(x) is the i-th inequality constraint, including strength constraints and stiffness constraints; hj(x) is the j-th equality constraint; n is the number of inequality constraints; m is the number of equality constraints; and xmin and xmax are the minimum and maximum values ​​of the design parameters, respectively.

4. The vehicle frame design-to-end-of-life management system based on new information technology services according to claim 3, characterized in that: The multi-terminal collaborative design platform built by the multi-terminal collaborative unit enables real-time sharing and feedback of design schemes through cloud interfaces, supports online annotation, parameter modification and scheme review by design, manufacturing and operation and maintenance personnel, and shortens the design cycle.

5. The vehicle frame design-to-end-of-life management system based on new information technology services according to claim 1, characterized in that: The manufacturing digital twin control module, centered on CAM (Computer-Aided Manufacturing), integrates IoT, machine learning, and real-time data acquisition technologies. It includes a data acquisition unit, an equipment fault prediction unit, a process parameter adaptive adjustment unit, and a manufacturing quality control unit. The data acquisition unit deploys IoT sensors at each workstation in the chassis manufacturing process to collect process parameters, equipment operating data, and chassis quality inspection data for welding, stamping, and assembly processes in real time, transmitting these data to the data fusion center. The equipment fault prediction unit uses machine learning algorithms to analyze historical and real-time operating data of the equipment, accurately predicting the probability and type of equipment faults, triggering fault warnings, and generating maintenance suggestions. The process parameter adaptive adjustment unit uses a digital twin mirror to compare actual chassis manufacturing data with design standard data, dynamically adjusting process parameters to achieve real-time correction of manufacturing deviations. The manufacturing quality control unit analyzes quality data during the manufacturing process, identifies chassis quality defects, traces their causes, and ensures that manufacturing accuracy matches design requirements. The equipment fault prediction unit uses machine learning algorithms to predict the probability of equipment faults; the fault probability prediction expression is: ， Where Pf is the probability of equipment failure, and its value ranges from [0,1]. This is the Sigmoid activation function, used to map the output to the [0,1] interval; is the feature weight vector, representing the degree of influence of each equipment operating parameter on the failure probability; X is the equipment operating parameter vector, including operating speed, load, temperature, and vibration frequency; b is the bias term, used to adjust the model output benchmark.

6. The vehicle frame design-to-end-of-life management system based on new information technology services according to claim 1, characterized in that: The group twin operation and maintenance assessment module includes a group twin pool construction unit, a scenario clustering unit, a federated learning training unit, and a remaining lifespan assessment unit. The group twin pool construction unit builds a group digital twin pool of the same model of vehicle frame, integrating digital twin images of each physical frame with full lifecycle operation data. The scenario clustering unit uses a clustering algorithm to group the same model of vehicle frames according to their operational scenarios based on vehicle operation scenario data. The federated learning training unit collaboratively trains the remaining lifespan prediction model of the frame within each scenario group without aggregating the original data, protecting data privacy. The remaining lifespan assessment unit generates a scenario-lifespan map, matching an initial remaining lifespan baseline for newly connected vehicles to achieve accurate scenario-based remaining lifespan assessment of the frame. The federated learning training unit collaboratively trains the remaining lifespan prediction model within each scenario group, and the remaining lifespan prediction expression is: ， Where Lrem is the remaining life of the chassis (unit: km). This is the model parameter vector; is the feature mapping function used to extract scene features and operational data features; S is the chassis operation scene feature vector, including scene type, driving speed, and load; D is the chassis operation data vector, including vibration, stress, and wear. The prediction error follows a normal distribution. , This represents the error variance.

7. The vehicle frame design-to-end-of-life management system based on new information technology services according to claim 1, characterized in that: The low-carbon scrapping and dismantling module includes a material information acquisition unit, a material recycling potential assessment unit, a dismantling path planning unit, and a carbon footprint optimization unit. The material information acquisition unit acquires data on the material composition distribution, connection processes, and damage levels of the chassis through digital twin mirroring. The material recycling potential assessment unit establishes a material recycling potential assessment model to quantitatively evaluate the recycling value and feasibility of each chassis component. The dismantling path planning unit generates the optimal dismantling path for the chassis based on the material information and recycling potential assessment results, improving dismantling efficiency and resource recycling rate. The carbon footprint optimization unit calculates the carbon emissions of different dismantling schemes based on the LCA database, proposes a carbon footprint minimization dismantling algorithm, and prioritizes remanufacturing schemes for high-value chassis components. The carbon footprint optimization unit calculates the carbon emissions of different dismantling schemes using the following expression: ， Where Ctotal represents the total carbon emissions of the dismantling scheme (unit: K represents the number of dismantling processes; Ck represents the unit carbon emission of the k-th dismantling process (unit: / piece); Qk is the number of pieces processed in the k-th dismantling process (unit: piece); Ctrans is the carbon emissions from material transportation during the dismantling process (unit: piece). ).

8. The vehicle frame design-to-end-of-life management system based on new information technology services according to claim 7, characterized in that: The material recycling potential assessment model established by the material recycling potential assessment unit combines the material composition of the frame, the degree of damage, and the market recycling value to quantitatively assess the recycling potential of each component, and prioritizes the recommendation of remanufacturing solutions for high-value components to improve the resource recycling rate.

9. The vehicle frame design-to-end-of-life management system based on new information technology services according to claim 1, characterized in that: The full-chain data fusion and blockchain secure sharing module includes a unified data hub, a data integration unit, a blockchain encryption unit, and a cross-enterprise collaboration unit. The unified data hub integrates five data sources—chassis design, manufacturing, use, maintenance, and scrapping—to achieve centralized data management. The data integration unit uses a data fusion algorithm to merge heterogeneous data sources, eliminating data silos across the entire chassis chain. The blockchain encryption unit encrypts all data across the chain using blockchain smart contracts, ensuring that chassis-related data is tamper-proof and traceable. The cross-enterprise collaboration unit supports secure collaboration between OEMs and suppliers, improving the efficiency and collaboration level of data flow across the entire chassis chain. The data integration unit uses a data fusion algorithm to achieve heterogeneous data fusion; the data fusion expression is: ， Where Dfusion represents the fused data; T represents the number of data sources, with a value of 5, corresponding to the five categories of data sources: design, manufacturing, use, maintenance, and disposal; and at represents the weight coefficient of the t-th data source, satisfying... The weighting coefficients are set according to the data precision and importance; Dt is the original data of the t-th type of data source.

10. The vehicle frame design-to-end-of-life management system based on new information technology services according to claim 1, characterized in that: The terminal interaction module includes desktop terminals, mobile terminals, and industrial tablets, supporting login for users with different roles. It provides functions such as data viewing, parameter setting, command issuance, early warning viewing, and report generation, enabling visualized and convenient operation of the entire lifecycle management process.

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