Vehicle frame fatigue prediction and monitoring method and system based on twinning of Internet of Vehicles

Abstract

The invention discloses a method and a system for predicting and monitoring frame fatigue based on twinning of the Internet of Vehicles. The method comprises the following steps: analyzing a vehicle use scene and a working condition ratio based on historical data of the Internet of Vehicles; carrying out a frame real vehicle load test, and converting a strain-time history into a load-time history in combination with static loading calibration; performing weighted combination on each load history according to a working condition ratio to obtain a weighted composite load-time history; reconstructing a stress-time history of each measuring point based on finite element analysis, and constructing a stress cyclic spectrum by adopting a rain flow counting method; calculating the fatigue life of the frame by combining a stress-life method and a Miner linear damage criterion; and constructing a frame twinborn model corresponding to the real vehicle at the cloud, accessing vehicle operation data in real time, dynamically updating a stress process and fatigue damage, calculating the residual fatigue life, and triggering early warning when the life is lower than a threshold value. The method can achieve the precise evaluation and online monitoring of the fatigue life of the vehicle frame, and is suitable for the durability design and structural health management of new energy commercial vehicles.

Description

Technical Field

[0001] The present invention belongs to the technical field of structural durability assessment and health monitoring of new energy vehicles, and in particular relates to a frame fatigue life prediction and detection method based on Internet of Vehicles data and digital twins. The method is suitable for fatigue life assessment, remaining life warning and operation and maintenance management of new energy commercial vehicle frames under actual service conditions. Background Art

[0002] In recent years, with the rapid development of new business models such as e-commerce logistics and express delivery, commercial vehicles have gradually adopted high-frequency, heavy-load, and multi-operational operating characteristics, placing higher demands on vehicle structural durability and service safety. As a key load-bearing structure of commercial vehicles, the fatigue life of the frame not only affects the operational safety of the entire vehicle but also directly impacts the vehicle's operating costs and lifecycle economics. Therefore, accurately predicting the frame's fatigue life under real-world operating conditions and monitoring its service status in real time have become urgent technical challenges in the field of commercial vehicle structural design and health management.

[0003] Existing commercial vehicle frame fatigue life prediction methods mainly rely on load spectra obtained from standardized test conditions or accelerated endurance tests in the laboratory. Such methods are usually based on limited sample data or assumed conditions, and it is difficult to fully and accurately characterize the vehicle load distribution and usage characteristics under real operating conditions, resulting in a large deviation between the evaluation results and the actual service conditions. Although some studies have attempted to correct the load spectrum through actual road spectrum data, there is a general lack of systematic identification and full utilization of actual vehicle operating conditions, making it difficult to effectively solve the problems of insufficient representativeness and poor generalization of working conditions, and unable to meet the increasingly diverse and personalized engineering application needs. In addition, current fatigue life prediction technologies generally lack a real-time feedback mechanism with the actual operating status of the vehicle, and cannot achieve dynamic monitoring and closed-loop management of the structural service status, which restricts the intelligent development of vehicle structural health management throughout its life cycle.

[0004] With the rapid development and widespread application of connected vehicle technology, data such as vehicle speed, load, acceleration, turn signals, and road conditions can be efficiently collected in real time and transformed into a large-scale data resource. This comprehensive data resource covers different regions, road types, and diverse operating modes, more realistically reflecting user driving behavior and load conditions, providing a reliable data foundation for constructing more representative and accurate fatigue load spectra. Furthermore, the development of digital twin technology enables online monitoring of vehicle structural fatigue conditions, providing technical support for real-time monitoring of the vehicle frame's service status, dynamic prediction of its remaining life, and closed-loop early warning management. Summary of the Invention

[0005] To address the aforementioned technical issues, the present invention provides a vehicle frame fatigue prediction and monitoring method and system based on the Internet of Vehicles (IoV) twin. This method combines big data analysis of actual operating conditions, load measurement and modeling, precise fatigue life prediction, and real-time online monitoring and life warning mechanisms. This enables high-precision life prediction and real-time monitoring of the vehicle frame's in-service status under real-world operating conditions, effectively enhancing the intelligent design of vehicle structural durability and operational safety management.

[0006] Specifically, the technical solutions provided by the present invention are as follows: A vehicle frame fatigue prediction and monitoring method based on Internet of Vehicles twins, including: S1. Analyze and compile statistics on vehicle usage scenarios and operating conditions based on historical data from the Internet of Vehicles. S2. Conduct a frame load test on a real vehicle based on the usage scenario and working condition ratio to obtain the strain response signal of each measuring point on the frame and record the strain-time history; S3. Perform a static loading calibration test on the vehicle frame, and convert the strain-time history obtained under each test condition into the corresponding load-time history; S4. Performing a weighted combination of the load-time histories under each test condition to obtain a weighted composite load-time history; S5. Perform finite element analysis on the vehicle frame to obtain the static stress response of each measuring point under unit load, and superimpose the unit responses according to the weighted composite load-time history ratio to reconstruct the stress-time history of each measuring point; S6. Based on the stress-time history of each measuring point, the rain flow counting method is used to perform cycle identification and amplitude statistics on the stress history, extract the cycle number distribution within different stress amplitude intervals, and construct the stress cycle spectrum of the frame structure; S7. Based on the stress cycle spectrum constructed at each measuring point, the fatigue life of the frame is evaluated using the stress-life method combined with the Miner linear cumulative damage criterion; S8. Build a digital twin model of the vehicle frame. Based on the vehicle's current Internet of Vehicles operating data, calculate and display the remaining fatigue life mileage of the frame in real time. When the remaining fatigue life mileage reaches the threshold, trigger an early warning.

[0007] Furthermore, in step S1: The historical IoV data includes vehicle identification code, timestamp, vehicle latitude and longitude, driving speed, acceleration, turn signal, load information and road condition information; the acquired historical IoV data is preprocessed, including: unifying the timestamp and location information format, constructing a mapping relationship between vehicle trajectory and time series; eliminating invalid data; and smoothing the cleaned data.

[0008] Furthermore, in step S1: First, dimensionality reduction processing is performed on the multidimensional operating condition variables, which include driving speed, acceleration, turn signals, road conditions and load information in the historical data of the Internet of Vehicles; then, cluster analysis is performed on the dimensionality reduction results to identify the vehicle's usage scenarios and operating conditions, and the proportion of each usage scenario and operating condition is counted. The usage scenarios include urban roads, expressways, and highways, and the operating conditions include uniform speed conditions, acceleration conditions, braking conditions, turning conditions, and vehicle load conditions.

[0009] Furthermore, in step S3: According to the strain data collected during static loading Corresponding load value , calculate the calibration coefficients of each measuring point in the X, Y, and Z directions ; Based on calibration coefficient Convert the strain-time history under each test condition into the corresponding load-time history.

[0010] Furthermore, in step S4: Based on the statistical analysis results of usage scenarios and operating condition proportions, the frame load-time history obtained under each test condition is weighted and combined according to the proportion of various road types, operating conditions and load states to obtain a weighted composite load-time history.

[0011] Furthermore, in step S7: According to the stress-life curve of the frame material, the corresponding cycle life of each stress amplitude level is calculated. N i , and combined with the actual number of cycles under each amplitude n i , calculate the cumulative fatigue damage value D ; According to Miner's linear cumulative damage criterion, when D When ≥1, the structure is considered to have reached the fatigue failure threshold; according to the cumulative fatigue damage value D Get the number of fatigue life cycles N ; Based on the distance traveled by the vehicle in the weighted composite load-time history L c , calculate the fatigue life mileage of the frame L ;in, , , .

[0012] Furthermore, based on the fatigue life assessment results, a visual display is performed in the form of an equivalent life cloud diagram in the finite element analysis model of the frame, intuitively presenting the fatigue life distribution characteristics and damage accumulation trends of various areas of the frame structure.

[0013] Furthermore, step S8 includes: Build a digital twin model of the vehicle frame corresponding to the actual vehicle structure for each operating vehicle in the cloud; During vehicle operation, the system continuously collects the uploaded vehicle network operation data and identifies the current working status in real time; Using the recognition result as an index, the load-time history of the corresponding working condition constructed in S3 is called. The standard load history is then scaled and adjusted in amplitude and period based on the vehicle's current actual operating parameters to construct an approximate load-time history that reflects the intensity and duration of the current working condition. Based on the approximate load-time history, the digital twin model maps the stress-time history of each measuring point and extracts the stress cycle spectrum using the rain flow counting method in real time; Calculate the incremental fatigue damage value during the current operating cycle D inc and compared with the historical cumulative damage value D his Superimpose and continuously update the total fatigue damage at the current moment D tot = D his + D inc , and then calculate the remaining life ratio of the current frame R =1- D tot ,when D tot When ≥1, the frame structure is considered to have reached the fatigue limit; According to the remaining life ratio R , calculate and update the remaining fatigue life mileage of the frame in real time L rem = L × R .

[0014] A vehicle frame fatigue prediction and monitoring system based on the above method includes a vehicle network data acquisition module, a vehicle frame fatigue life prediction module, and a remaining fatigue life monitoring module; The IoV data acquisition module is used to acquire IoV historical data and IoV real-time data continuously uploaded by vehicles, and pre-process the acquired data; The frame fatigue life prediction module is used to evaluate the fatigue life mileage of the vehicle and visualize it in the form of an equivalent life cloud diagram in the finite element analysis model of the frame, intuitively presenting the fatigue life distribution characteristics and damage accumulation trends of various regions of the frame structure; The remaining fatigue life monitoring module is used to update the remaining fatigue life mileage of the frame in real time, and automatically trigger the early warning mechanism and send life warning information when the remaining fatigue life mileage reaches a preset threshold.

[0015] Furthermore, the system also includes a frame digital twin module; the digital twin module constructs a frame digital twin model corresponding to the actual vehicle structure for each running vehicle in the cloud, and each twin model is bound to the corresponding vehicle through a unique vehicle identification code.

[0016] Compared with the prior art, the present invention has at least the following beneficial effects: (1) Accurate characterization and scientific weight allocation of users' real operating conditions: The present invention relies on large-scale Internet of Vehicles historical data of electric commercial vehicles, extracts multi-dimensional operating parameters and performs feature dimensionality reduction and cluster analysis, constructs typical usage scenarios and operating state space, accurately depicts the operating conditions of vehicles in actual service environments, and makes reasonable weight allocation according to the proportion of various operating conditions, providing a more representative operating condition input model for fatigue life prediction, overcoming the problem of insufficient generalization and applicability of standardized operating conditions in traditional methods.

[0017] (2) A highly reliable load data measurement and conversion method is proposed: The present invention uses actual vehicle tests to obtain the three-dimensional strain history of key parts of the frame, and combines it with static loading calibration tests to establish an accurate strain-load conversion relationship, thereby obtaining fatigue load data input with high physical authenticity and engineering credibility based on real measurements. This method is significantly superior to traditional load acquisition methods that rely on numerical simulation or virtual iteration, and provides a solid guarantee for the accuracy and reliability of subsequent fatigue response calculations.

[0018] (3) A closed-loop mechanism for online monitoring and fatigue life warning of digital twins in collaboration with the vehicle and the cloud has been established: The present invention relies on the cloud platform to build a digital twin model corresponding to the physical structure of each vehicle. The model integrates the finite element model, fatigue analysis parameters and the vehicle's unique identification information (VIN), accesses the operating data uploaded by the vehicle in real time and dynamically reconstructs the load input under the current working conditions, quickly maps the structural stress response, and continuously completes the dynamic update of fatigue damage accumulation and remaining life; when the remaining fatigue life of the frame is reduced to the preset threshold, the warning is automatically triggered, realizing the online perception and intelligent feedback control of the health status of the frame structure, and improving the safety of the vehicle operation and the management level of structural durability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0020] Figure 1 is a flow chart of a frame fatigue prediction and monitoring method according to one embodiment of the present invention; Figure 2(a) shows the load-time history curve of the left front hanging ear of the front suspension of an electric truck under the condition of full load and uniform speed of 60 km / h. Figure 2(b) shows the load-time history curve of the left front hanging ear of the front suspension of an electric truck under the condition of full load and uniform speed of 80 km / h. Figure 2(c) shows the load-time history curve of the left front hanging ear of the front suspension of an electric truck under the condition of full load and uniform speed of 100 km / h. Figure 2(d) shows the load-time history curve of the left rear hanging eye of the rear suspension of an electric truck under a half-load condition at a constant speed of 60 km / h. Figure 2(e) is a load-time history curve of the left rear eye of the rear suspension of an electric truck under a half-load condition at a constant speed of 80 km / h. Figure 2(f) shows the load-time history curve of the left rear hanging eye of the rear suspension of an electric truck under a half-load condition at a uniform speed of 100 km / h. Figure 3 1 is a schematic diagram of a weighted composite load-time history working condition combination structure in one embodiment of the present invention; Figure 4 This is a finite element cloud diagram showing the fatigue life distribution of the frame in one embodiment of the present invention. DETAILED DESCRIPTION

[0021] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, other embodiments obtained by ordinary technicians in this field without making creative efforts are all within the scope of protection of the present invention.

[0022] Example 1 This embodiment provides a frame fatigue prediction and monitoring method based on the Internet of Vehicles twin, so as to improve the accuracy and real-time performance of commercial vehicle frame fatigue life prediction and in-service monitoring.

[0023] like Figure 1As shown, this method first relies on the big data generated by the Internet of Vehicles (IoV) under actual operating conditions of new energy commercial vehicles. By identifying and statistically analyzing real-world usage scenarios and operating conditions, it conducts field measurement, calibration, and combination of road loads to construct a highly representative weighted composite load-time history. This is combined with finite element analysis to accurately assess the fatigue life of the vehicle frame under real-world service conditions. Based on this, a digital twin model of the vehicle frame is created in the cloud. Uploaded operational data is collected in real time and combined with standard loads. The Miner damage accumulation method is used to dynamically update the remaining fatigue life of the frame. When the remaining life reaches the set warning threshold, an alert is automatically triggered.

[0024] For ease of explanation, a certain type of electric truck is taken as an example to describe the specific implementation process of the method in detail.

[0025] 1. Identification and statistical analysis of vehicle frame usage scenarios and operating conditions (1) Collection of historical data of Internet of Vehicles This example collects IoV operational data from 500 electric trucks of a certain model from a commercial vehicle manufacturer between 2021 and 2024 as an analysis sample. The collected data includes multi-dimensional operational parameters such as vehicle identification numbers, timestamps, vehicle latitude and longitude, speed, acceleration, turn signals, load information, and road condition information.

[0026] (2) Internet of Vehicles data preprocessing To improve data quality and ensure the accuracy of subsequent analysis, the collected historical IoV data undergoes systematic preprocessing. This includes standardizing the timestamp and location information formats, mapping vehicle trajectories to time series, detecting and removing invalid data due to factors such as sensor failure, communication packet loss, or signal drift, and smoothing the cleaned data using a Savitzky–Golay filter to further suppress high-frequency noise and preserve data trends, ensuring that the retained information has good physical interpretability and continuity.

[0027] (3) Identification and statistical analysis of usage scenarios and operating conditions Driving speed, acceleration, turn signals, road conditions, and load information were selected as variables representing operating conditions. Principal component analysis (PCA) was first used to reduce the dimensionality of these multidimensional operating condition variables and construct a comprehensive operating condition feature space. Unsupervised cluster analysis was then performed on the reduced dimensionality using the density-based spatial clustering algorithm (DBSCAN). This analysis identified typical usage scenarios and operating conditions for electric trucks and calculated the proportion of each type of operating condition. The usage scenarios and operating condition distributions extracted in this example are shown in Tables 1, 2, and 3.

[0028] Table 1 Proportion of each operating condition

[0029] Table 2 Proportion of each usage scenario

[0030] Table 3 Proportion of each load condition

[0031] 2. Measurement and Modeling of Multi-condition Fatigue Loads on Vehicle Frames (1) Determination of the position of the frame load measurement point Based on the structural layout characteristics of the test vehicle, load measurement points are placed at key stress-bearing locations on the vehicle frame. The number of measurement points on a single side is typically set at 6 to 10, and the measurement points are symmetrically arranged on the left and right sides of the vehicle frame. In this embodiment, eight measurement points are set on each side of the vehicle frame, specifically including: the cab front connection point, the cab rear connection point, the front suspension front lifting eye connection point, the front suspension rear lifting eye connection point, the battery compartment front connection point, the battery compartment rear connection point, the rear suspension front lifting eye connection point, and the rear suspension rear lifting eye connection point.

[0032] (2) Strain gauge bonding and connection to measuring equipment To reduce interference from non-target loads on measurement results and improve the accuracy of triaxial strain measurements, a strain gauge is attached to each measuring point on the frame's longitudinal rail, along the upper, middle, and lower sides, along the longitudinal (X) direction of the frame. Each measuring point is connected to the strain measurement system using a quarter-bridge method to simplify measurement wiring and improve signal channel configuration efficiency. All strain signals are connected to a dynamic signal testing and analysis system to simultaneously collect and record the three-dimensional strain responses at each measuring point.

[0033] (3) Frame fatigue load actual vehicle test Based on the usage scenarios and operating conditions derived from statistical analysis of historical data from the Internet of Vehicles (IoV) for electric trucks, a real-vehicle test plan for frame loads was developed. The test plan for this embodiment is shown in Table 4. Real-vehicle testing was conducted at a vehicle proving ground using the test condition combinations listed in Table 4. During the test, the vehicle drove according to the preset operating conditions. The measurement system simultaneously collected strain response signals from various measurement points on the frame, recording the strain-time history, providing high-quality basic data support for subsequent load inversion and fatigue load spectrum construction.

[0034] Table 4 Frame dynamic load test plan

[0035] (4) Calibration and restoration of multi-condition loads After completing the vehicle testing, the strain-time history of the vehicle frame under various typical test conditions was obtained. To convert the strain response into a load-time history, a static loading calibration experiment was conducted to establish the strain-load conversion relationship.

[0036] During the calibration process, the sticking method and wiring arrangement of the strain gauges should be kept consistent with the road test to ensure the consistency of the measurement conditions. The calibration method for the X-direction load is: mechanical jacks are used to load the left and right longitudinal beams at the front end of the frame, with loading values ​​of 500 kg, 1000 kg and 1500 kg respectively, and the loading direction is consistent with the centroidal axis of the longitudinal beam section of the frame; the Y-direction calibration is to perform lateral loading near the 16 measuring points on the outside of the longitudinal beam, with loading values ​​of 200 kg, 400 kg and 600 kg respectively; the Z-direction calibration is to perform vertical loading near the 16 measuring points above the longitudinal beam, with loading values ​​of 200 kg, 400 kg and 600 kg. According to the strain data collected during the loading process Corresponding load value , calculate the calibration coefficients of each measuring point in the X, Y, and Z directions :

[0037] Based on the above calibration coefficient , the strain-time history under each test condition can be converted into the corresponding load-time history. The calibration coefficients of each measuring point calculated in this embodiment are shown in Table 5. The load-time history curve of the frame under typical test conditions is shown in Table 5. Figure 2(a) to Figure 2(f) shown.

[0038] Table 5 Calibration coefficients of the frame in each direction

[0039] 3. Fatigue life simulation prediction based on weighted load (1) Weighted composite load-time history construction Based on the statistical results of typical usage scenarios and operating conditions obtained from the analysis of historical data of the Internet of Vehicles, the frame load-time history obtained under the above test conditions is weighted and combined according to the proportion of various road types, operating conditions (such as constant speed, acceleration, braking, steering) and load states (full load, half load, no load), so as to construct a weighted composite load-time history based on the user's actual working conditions. This history serves as the input load for subsequent fatigue life analysis. The combination of this embodiment is as follows Figure 3 shown.

[0040] (2) Frame finite element model establishment and stress response analysis Based on the three-dimensional geometric model and material property information of the electric truck frame, a finite element analysis model was established, with a reasonable mesh division and constraints set using the inertia release method. A unit load (typically 1 kN) was applied to each measuring point independently in the X, Y, and Z directions to obtain static stress responses. Based on the linear elastic superposition principle, the unit responses were superimposed according to the weighted composite load-time history ratio to reconstruct the stress-time history at each measuring point, which served as input for fatigue life assessment.

[0041] (3) Construction of stress cycle spectrum Based on the stress-time history data at each measuring point in the X, Y, and Z directions, the rainflow counting method is used to identify cycles and calculate amplitudes in the stress history. The distribution of cycles within different stress amplitude ranges is extracted, and a stress cycle spectrum of the frame structure under real-world user operating conditions is constructed. This stress cycle spectrum accurately reflects the fatigue stress response characteristics of key frame components under actual operating conditions and provides a direct basis for life calculations.

[0042] (4) Fatigue life calculation Based on the stress cycle spectrum constructed at each measuring point, the stress-life method (S-N method) combined with Miner's linear cumulative damage criterion is used to evaluate the fatigue life of the frame. According to the S-N curve of the frame material, the corresponding cycle life of each stress amplitude level is calculated. N i , combined with the actual number of cycles at each amplitude n i , calculate the cumulative fatigue damage value D :

[0043] According to Miner's criterion, when D When ≥1, the structure is considered to have reached the fatigue failure threshold. The number of fatigue life cycles of the frame under the user's actual working conditions can then be inferred. N for:

[0044] Furthermore, according to the distance traveled by the test vehicle in the weighted composite load-time history L c , the fatigue life mileage of the frame under the user's actual working conditions can be calculated L for: L = N × L c In this embodiment, the fatigue life cycle number of the weak point of the frame N is 2.924×10 6, the distance traveled by the test vehicle in the weighted composite load-time history L c It is 0.386 km, so the fatigue life mileage of the frame under the user's actual working conditions is calculated L It is 1.12866 million kilometers.

[0045] (5) Visualization and evaluation of lifespan distribution results: Based on the fatigue life analysis results, the equivalent life cloud diagram is used to visualize the fatigue life distribution characteristics and damage accumulation trends of each area of ​​the frame structure. In this embodiment, the fatigue life distribution of the frame under the user's actual working conditions is as follows: Figure 4 shown.

[0046] 4. Online Monitoring and Early Warning of Remaining Lifespan Based on Digital Twins (1) Construction and deployment of the digital twin model of the vehicle frame A digital twin model of the vehicle frame, corresponding to the actual vehicle structure, is constructed in the cloud for each operating new energy commercial vehicle. This twin model, based on the finite element model established in Part 3, incorporates frame fatigue analysis parameters (such as static stress response per unit load, material S–N curves, and damage accumulation criteria) to form a computational module capable of simulating fatigue states and analyzing lifespan evolution. Each twin model is tied to its corresponding vehicle through its unique vehicle identification number (VIN), ensuring the accuracy and consistency of the personalized structural model.

[0047] (2) Real-time working condition identification and standard load reconstruction During vehicle operation, the system continuously collects data uploaded by the vehicle's connected vehicle network, including speed, acceleration, turn signals, load information, and road conditions. The system then uses these multi-dimensional operating parameters to identify the current operating condition in real time. The identification results serve as an index to dynamically invoke the load-time history templates for each operating condition constructed experimentally. To enhance the personalized adaptability of load input, the standard load history is scaled and periodically adjusted based on the vehicle's current operating parameters. This creates an approximate load-time history that reflects the intensity and duration of the current operating condition, providing continuous and accurate load input for subsequent fatigue damage calculations.

[0048] (3) Online damage accumulation and life update Based on the constructed approximate load-time history, the digital twin model maps the stress-time history of each measuring point and extracts the stress cycle spectrum using the rain flow counting method in real time. Combining the S-N curve of the frame material with Miner's linear cumulative damage theory, the incremental fatigue damage value within the current operating cycle is calculated. D inc and compared with the historical cumulative damage value D hisSuperimpose and continuously update the total fatigue damage at the current moment D tot : D tot = D his + D inc According to the fatigue failure criterion, when D tot When ≥1, the structure is considered to have reached the fatigue limit. The remaining life ratio of the current frame can then be calculated. R : R =1 - D tot Combined with the fatigue life mileage of the frame calculated in the third part under the user's actual working conditions L , which can update the remaining fatigue life mileage of the frame in real time L rem : L rem = L × R Based on the Internet of Vehicles data uploaded by the vehicle in each period, the above calculation logic is executed periodically at a set frequency to dynamically evaluate the fatigue status of the frame.

[0049] (4) Lifespan threshold warning and feedback push When the remaining fatigue life mileage L rem When the preset warning threshold is reached (for example, the remaining life is less than 20% of the original life), the system automatically triggers the warning mechanism and pushes life warning information and dangerous location data to the vehicle management platform, operator, and design and development end, realizing real-time monitoring and closed-loop feedback control of the health status of the frame structure.

[0050] Through the above steps, the present invention not only accurately assesses the fatigue life of electric truck frame structures under actual service conditions, but also establishes a real-time fatigue status monitoring and dynamic remaining life update mechanism for in-service vehicles based on Internet of Vehicles data and digital twin models. This provides reliable technical support and intelligent solutions for frame structure durability design, service safety management, and lifecycle health maintenance.

[0051] Example 2 Based on the above method, this embodiment provides a frame fatigue prediction and monitoring system based on the Internet of Vehicles twin, which mainly includes an Internet of Vehicles data acquisition module, a frame fatigue life prediction module and a remaining fatigue life monitoring module. Among them, the Internet of Vehicles data acquisition module is used to obtain the Internet of Vehicles historical data and the Internet of Vehicles real-time data continuously uploaded by the vehicle, and pre-process the acquired data. The frame fatigue life prediction module is used to evaluate the fatigue life mileage of the vehicle, and visualize it in the form of an equivalent life cloud map in the finite element analysis model of the frame, intuitively presenting the fatigue life distribution characteristics and damage accumulation trends of each area of ​​the frame structure. The remaining fatigue life monitoring module is used to update the remaining fatigue life mileage of the frame in real time, and when the remaining fatigue life mileage reaches a preset threshold, it automatically triggers the early warning mechanism and sends life warning information.

[0052] In some embodiments, the system also includes a frame digital twin module, which is used to build a frame digital twin model corresponding to the actual vehicle structure for each running vehicle in the cloud, and each twin model is bound to the corresponding vehicle through a unique vehicle identification code.

[0053] The above-mentioned system can execute the frame fatigue prediction and monitoring method described in Example 1, and has the corresponding functional modules and beneficial effects of the method. For technical details not described in detail in this embodiment, please refer to the frame fatigue prediction and monitoring method provided in Example 1 of the present invention.

[0054] Through the description of the above embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a general hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the relevant technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Under the idea of ​​the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the present invention as described above. For the sake of simplicity, they are not provided in detail. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in this field should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of this application.

Claims

1. A frame fatigue prediction and monitoring method based on Internet of Vehicles twin, characterized by: include: S1. Analyze and compile statistics on vehicle usage scenarios and operating conditions based on historical data from the Internet of Vehicles. S2. Conduct a real-vehicle frame load test based on the usage scenario and working condition ratio to obtain the strain response signal of each measuring point on the frame and record the strain-time history; S3. Perform a static loading calibration test on the vehicle frame, and convert the strain-time history obtained under each test condition into the corresponding load-time history; S4. Performing a weighted combination of the load-time histories under each test condition to obtain a weighted composite load-time history; S5. Perform finite element analysis on the vehicle frame to obtain the static stress response of each measuring point under unit load, and superimpose the unit responses according to the weighted composite load-time history ratio to reconstruct the stress-time history of each measuring point; S6. Based on the stress-time history of each measuring point, the rain flow counting method is used to perform cycle identification and amplitude statistics on the stress history, extract the cycle number distribution within different stress amplitude intervals, and construct the stress cycle spectrum of the frame structure; S7. Based on the stress cycle spectrum constructed at each measuring point, the fatigue life of the frame is evaluated using the stress-life method combined with the Miner linear cumulative damage criterion; S8. Build a digital twin model of the vehicle frame. Based on the vehicle's current Internet of Vehicles operating data, calculate and display the remaining fatigue life mileage of the frame in real time. When the remaining fatigue life mileage reaches the threshold, trigger an early warning.

2. The frame fatigue prediction and monitoring method according to claim 1, characterized in that: In step S1: The historical IoV data includes vehicle identification code, timestamp, vehicle latitude and longitude, driving speed, acceleration, turn signal, load information and road condition information; the acquired historical IoV data is preprocessed, including: unifying the timestamp and location information format, constructing a mapping relationship between vehicle trajectory and time series; eliminating invalid data; and smoothing the cleaned data.

3. The frame fatigue prediction and monitoring method according to claim 1, characterized in that: In step S1: First, dimensionality reduction processing is performed on the multidimensional operating condition variables, which include driving speed, acceleration, turn signals, road conditions and load information in the historical data of the Internet of Vehicles; then, cluster analysis is performed on the dimensionality reduction results to identify the vehicle's usage scenarios and operating conditions, and the proportion of each usage scenario and operating condition is counted. The usage scenarios include urban roads, expressways, and highways, and the operating conditions include uniform speed conditions, acceleration conditions, braking conditions, turning conditions, and vehicle load conditions.

4. The frame fatigue prediction and monitoring method according to claim 1, wherein: In step S3: According to the strain data collected during static loading Corresponding load value , calculate the calibration coefficients of each measuring point in the X, Y, and Z directions ; Based on calibration coefficient Convert the strain-time history under each test condition into the corresponding load-time history.

5. The frame fatigue prediction and monitoring method according to claim 1, wherein: In step S4: Based on the statistical analysis results of usage scenarios and operating condition proportions, the frame load-time history obtained under each test condition is weighted and combined according to the proportion of various road types, operating conditions and load states to obtain a weighted composite load-time history.

6. The frame fatigue prediction and monitoring method according to claim 1, wherein: In step S7: According to the stress-life curve of the frame material, the corresponding cycle life of each stress amplitude level is calculated. N i , and combined with the actual number of cycles under each amplitude n i , calculate the cumulative fatigue damage value D ; According to Miner's linear cumulative damage criterion, when D When ≥1, the structure is considered to have reached the fatigue failure threshold; According to the cumulative fatigue damage value D Get the number of fatigue life cycles N ; Based on the distance traveled by the vehicle in the weighted composite load-time history L c , calculate the fatigue life mileage of the frame L ;in, , , .

7. The frame fatigue prediction and monitoring method according to claim 1, wherein: Based on the fatigue life assessment results, an equivalent life cloud diagram is used in the finite element analysis model of the frame to visually display the fatigue life distribution characteristics and damage accumulation trends of various areas of the frame structure.

8. The frame fatigue prediction and monitoring method according to claim 6, wherein: Step S8 includes: Build a digital twin model of the vehicle frame corresponding to the actual vehicle structure for each operating vehicle in the cloud; During vehicle operation, the system continuously collects the uploaded vehicle network operation data and identifies the current working status in real time; Using the recognition result as an index, the load-time history of the corresponding working condition constructed in S3 is called. The standard load history is then scaled and adjusted in amplitude and period based on the vehicle's current actual operating parameters to construct an approximate load-time history that reflects the intensity and duration of the current working condition. Based on the approximate load-time history, the digital twin model maps the stress-time history of each measuring point and extracts the stress cycle spectrum using the rain flow counting method in real time; Calculate the incremental fatigue damage value during the current operating cycle D inc and compared with the historical cumulative damage value D his Superimpose and continuously update the total fatigue damage at the current moment D tot = D his + D inc , and then calculate the remaining life ratio of the current frame R =1- D tot ,when D tot When ≥1, the frame structure is considered to have reached the fatigue limit; According to the remaining life ratio R , calculate and update the remaining fatigue life mileage of the frame in real time L rem = L × R .

9. A vehicle frame fatigue prediction and monitoring system based on the method according to any one of claims 1 to 8, characterized in that: It includes the Internet of Vehicles data acquisition module, the frame fatigue life prediction module and the remaining fatigue life monitoring module; The IoV data acquisition module is used to acquire IoV historical data and IoV real-time data continuously uploaded by vehicles, and pre-process the acquired data; The frame fatigue life prediction module is used to evaluate the fatigue life mileage of the vehicle and visualize it in the form of an equivalent life cloud diagram in the finite element analysis model of the frame, intuitively presenting the fatigue life distribution characteristics and damage accumulation trends of various regions of the frame structure; The remaining fatigue life monitoring module is used to update the remaining fatigue life mileage of the frame in real time, and automatically trigger the early warning mechanism and send life warning information when the remaining fatigue life mileage reaches a preset threshold.

10. The frame fatigue prediction and monitoring system according to claim 9, wherein: The system also includes a frame digital twin module; the digital twin module builds a frame digital twin model corresponding to the actual vehicle structure for each running vehicle in the cloud, and each twin model is bound to the corresponding vehicle through a unique vehicle identification code.

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