A vehicle frame fatigue prediction and monitoring method and system based on vehicle networking twinning

Abstract

The application discloses a kind of based on vehicle networking twinborn frame fatigue prediction and monitoring method and system.The method comprises: based on vehicle networking historical data analysis vehicle use scene and working condition proportion;Carrying out frame real vehicle load test and combining static loading calibration, strain-time history is converted into load-time history;According to working condition proportion, each load history is weighted and combined, and weighted composite load-time history is obtained;Based on finite element analysis, reconstruct each measuring point stress-time history, and adopt rain flow counting method to construct stress cycle spectrum;Combined with stress-life method and Miner linear damage criterion, the fatigue life of frame is calculated;Corresponding frame twin model is constructed in the cloud to the real vehicle, real-time access vehicle operation data, dynamically update stress history and fatigue damage, calculate residual fatigue life, and trigger early warning when life is less than threshold value.The application can realize the accurate evaluation and online monitoring of frame fatigue life, and is suitable for new energy commercial vehicle durability design and structure health management.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of new energy vehicle structure durability evaluation and health monitoring, and particularly relates to a vehicle frame fatigue life prediction and detection method based on Internet of Vehicles data and digital twinning, which is suitable for fatigue life evaluation, residual life early warning and operation and maintenance management of new energy commercial vehicle frames under actual service conditions. BACKGROUND

[0002] In recent years, with the rapid development of e-commerce logistics, express delivery and other new formats, commercial vehicles gradually show the operating characteristics of high frequency, heavy load and multiple working conditions, which puts forward higher requirements for vehicle structure durability and service safety. As an important load-bearing structure of commercial vehicles, the fatigue life of the frame not only relates to the safety of the whole vehicle operation, but also directly affects the operation cost and life cycle economy of the vehicle. Therefore, how to accurately predict the fatigue life of the frame under real operating conditions and monitor its service state in real time has become a technical problem to be solved in the field of commercial vehicle structure design and health management.

[0003] The existing fatigue life prediction method of commercial vehicle frame mainly relies on the load spectrum obtained by standardized test conditions or accelerated durability test in the laboratory. This kind of method is usually based on limited sample data or assumed conditions, and it is difficult to fully and accurately represent the vehicle load distribution and use characteristics under real operating conditions, resulting in a large deviation between the evaluation results and the actual service conditions. Although some studies attempt to correct the load spectrum by using actual road spectrum data, they generally lack systematic identification and full utilization of actual vehicle operating conditions, making it difficult to effectively solve the problems of insufficient condition representation and poor generalization, and unable to meet the increasingly diversified and personalized engineering application requirements. In addition, the current fatigue life prediction technology generally lacks real-time feedback mechanism with the actual operating state of the vehicle, and cannot realize dynamic monitoring and closed-loop management of the structure service state, which restricts the intelligent development of vehicle life cycle structure health management.

[0004] With the rapid development and popularization of Internet of Vehicles technology, the speed, load, acceleration, steering signal, road condition information and other data in the vehicle operation process can be collected in real time and form a large-scale data resource, which fully covers different regions, road types and various operating modes, and can more truly reflect the driving behavior and load condition characteristics of users, providing a reliable data basis for constructing fatigue load spectrum with better representativeness and accuracy. At the same time, the development of digital twinning technology makes it possible to monitor the fatigue condition of the vehicle structure online, providing technical support for realizing real-time monitoring of the service state of the frame structure, dynamic prediction of the residual life and closed-loop early warning management. SUMMARY

[0005] To solve the above technical problems, the application provides a vehicle frame fatigue prediction and monitoring method and system based on vehicle networking twinning.

[0006] Specifically, the technical solutions provided by the application are as follows:

[0007] A vehicle frame fatigue prediction and monitoring method based on vehicle networking twinning comprises the following steps:

[0008] S1, based on vehicle networking historical data, analyzing and counting the use scenarios and working condition proportions of vehicles;

[0009] S2, according to the use scenarios and working condition proportions, performing vehicle frame load real vehicle testing to obtain strain response signals of each measuring point of the vehicle frame, and recording the strain-time history;

[0010] S3, performing static loading calibration testing on the vehicle frame, and converting the strain-time history obtained under each test condition into the corresponding load-time history;

[0011] S4, combining the load-time histories under each test condition by weighting to obtain a weighted composite load-time history;

[0012] S5, performing finite element analysis on the vehicle frame to obtain the static stress response of each measuring point under unit load, and superimposing the unit response according to the weighted composite load-time history proportion to reconstruct the stress-time history of each measuring point;

[0013] S6, based on the stress-time history of each measuring point, using the rain flow counting method to identify cycles and statistically analyze amplitudes, extracting the cycle number distribution in different stress amplitude intervals, and constructing a stress cycle spectrum of the vehicle frame structure;

[0014] S7, based on the constructed stress cycle spectrum of each measuring point, using the stress-life method combined with the Miner linear cumulative damage criterion to evaluate the fatigue life of the vehicle frame;

[0015] S8, constructing a digital twin model of the vehicle frame, calculating and displaying the remaining fatigue life mileage of the vehicle frame in real time according to the current vehicle networking operation data of the vehicle, and triggering an early warning when the remaining fatigue life mileage reaches a threshold.

[0016] Further, in step S1,

[0017] The vehicle networking historical data includes a vehicle identification code, a timestamp, a vehicle latitude and longitude, a driving speed, an acceleration, a steering signal, load information, and road condition information; the obtained historical vehicle networking data is preprocessed, including: unifying the timestamp and position information format, constructing a mapping relationship between the vehicle trajectory and the time sequence; eliminating invalid data; and performing smoothing processing on the cleaned data.

[0018] Further, in step S1:

[0019] Firstly, multi-dimensional working condition variables are processed by dimension reduction, the multi-dimensional working condition variables including driving speed, acceleration, steering signal, road condition information and load information in the vehicle networking historical data; then, clustering analysis is performed on the dimension reduction result to identify the use scenarios and working conditions of the vehicle, and the proportions of each use scenario and working condition are counted, the use scenarios including urban roads, expressways and highways, and the working conditions including uniform speed working condition, acceleration working condition, braking working condition, turning working condition, and vehicle load condition.

[0020] Further, in step S3:

[0021] According to the strain data collected in the static loading process and the corresponding load value , the calibration coefficients of each measuring point in X, Y and Z directions are calculated ; based on the calibration coefficients , the strain-time history under each test working condition is converted into the corresponding load-time history.

[0022] Further, in step S4:

[0023] Based on the analysis and statistical results of the use scenarios and working condition proportions, the frame load-time history obtained under each test working condition is weighted and combined according to the proportions of each type of road, running working condition and load state, to obtain a weighted composite load-time history.

[0024] Further, in step S7:

[0025] According to the stress-life curve of the frame material, the allowable cycle life corresponding to each stress amplitude level is calculated N i , and combined with the actual cycle number under each amplitude n i , the cumulative fatigue damage value is obtained D ; according to the Miner linear cumulative damage criterion, when D ≥1, it is considered that the structure reaches the fatigue failure threshold; according to the cumulative fatigue damage value D , the fatigue life cycle number is obtained N ; according to the distance traveled by the vehicle in the weighted composite load-time history Lc , the fatigue life mileage of the vehicle frame is calculated L ; wherein, , , .

[0026] Further, based on the fatigue life evaluation result, an equivalent life cloud map is visually displayed in the finite element analysis model of the vehicle frame, and the fatigue life distribution characteristics and damage accumulation trend of each region of the vehicle frame structure are intuitively presented.

[0027] Further, step S8 comprises:

[0028] a digital twin model of the vehicle frame corresponding to the actual vehicle structure is constructed in the cloud for each vehicle in operation;

[0029] During the operation of the vehicle, the uploaded Internet of Vehicles operation data is continuously collected, and the current working condition state is identified in real time;

[0030] The identification result is used as an index to call the load-time history under the corresponding working condition constructed in S3, and the standard load history is scaled in amplitude and adjusted in period according to the actual operation parameters of the vehicle, so as to construct an approximate load-time history reflecting the intensity and continuity of the current working condition;

[0031] Based on the approximate load-time history, the digital twin model maps the stress-time history of each measuring point, and the rainflow counting method is used to extract the stress cycle spectrum in real time;

[0032] The incremental fatigue damage value in the current operation cycle is calculated D inc , and is superimposed with the historical cumulative damage value D his , to continuously update the total fatigue damage at the current time D tot = D his + D inc , and then the remaining life proportion of the current vehicle frame is calculated R =1- D tot , when D tot ≥1, the vehicle frame structure is considered to reach the fatigue limit;

[0033] According to the remaining life proportion R , the remaining fatigue life mileage of the vehicle frame is calculated and updated in real time L rem = L × R .

[0034] A vehicle frame fatigue prediction and monitoring system based on the above method, comprising a vehicle networking data acquisition module, a vehicle frame fatigue life prediction module and a residual fatigue life monitoring module;

[0035] The vehicle networking data acquisition module is used to acquire vehicle networking historical data and vehicle continuously uploaded real-time vehicle networking data, and to pre-process the acquired data;

[0036] The vehicle frame fatigue life prediction module is used to evaluate the fatigue life mileage of the vehicle, and to visually display in the form of equivalent life cloud map in the finite element analysis model of the vehicle frame, so as to intuitively present the fatigue life distribution characteristics and damage accumulation trend of each region of the vehicle frame structure;

[0037] The residual fatigue life monitoring module is used to update the residual fatigue life mileage of the vehicle frame in real time, and to automatically trigger a warning mechanism and send a life warning information when the residual fatigue life mileage reaches a preset threshold.

[0038] Further, the system further comprises a vehicle frame digital twin module; the digital twin module constructs a vehicle frame digital twin model corresponding to the structure of each running vehicle in the cloud, and each twin model is bound to the corresponding vehicle through a vehicle unique identification code.

[0039] Compared with the prior art, the present application has at least the following beneficial effects:

[0040] (1) The precise characterization and scientific weight distribution of the user's real running condition are realized: relying on the large-scale vehicle networking historical data of the electric commercial vehicle, the present application extracts multi-dimensional running parameters and performs feature dimension reduction and clustering analysis, constructs typical use scenarios and running state space, accurately describes the running condition of the vehicle in the actual service environment, and reasonably distributes the weight according to the proportion of each type of working condition, thereby providing a more representative working condition input model for fatigue life prediction, and overcoming the problems of insufficient generalization and applicability of the standardized working condition in the traditional method.

[0041] (2) A high-credibility load data measurement and conversion method is proposed: the present application obtains the three-dimensional strain history of the key parts of the vehicle frame by real vehicle test, and establishes an accurate strain-load conversion relationship combined with static loading calibration test, so as to obtain fatigue load data input with high physical authenticity and engineering credibility on the basis of real measurement, which is significantly superior to the traditional load acquisition method relying on numerical simulation or virtual iteration, thereby providing a solid guarantee for the accuracy and reliability of subsequent fatigue response calculation.

[0042] (3) A closed-loop mechanism for online monitoring and fatigue life early warning of digital twins in vehicle-cloud collaboration was constructed: The present invention relies on the cloud platform to construct a digital twin model corresponding to the physical structure of each vehicle. The model integrates the finite element model, fatigue analysis parameters and vehicle unique identification information (VIN), accesses the operation data uploaded by the vehicle in real time and dynamically reconstructs the load input under the current working condition, 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 decreases to the preset threshold, an early warning is automatically triggered, realizing online perception and intelligent feedback control of the health status of the frame structure, and improving the safety of the whole vehicle operation and the management level of structural durability. Attached Figure Description

[0043] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0044] Figure 1 This is a flowchart illustrating a method for predicting and monitoring chassis fatigue in one embodiment of the present invention.

[0045] Figure 2(a) shows the load-time history curve of a measuring point at the left front hanger of the front suspension of an electric truck under a full-load constant speed of 60km / h.

[0046] Figure 2(b) shows the load-time history curve of a measuring point at the left front hanger of the front suspension of an electric truck under a full-load constant speed condition of 80km / h.

[0047] Figure 2(c) shows the load-time history curve of a measuring point at the left front hanger of the front suspension of an electric truck under a full-load constant speed of 100km / h.

[0048] Figure 2(d) shows the load-time history curve of a measuring point at the left rear hanger of the rear suspension of an electric truck under a constant speed of 60km / h and half-load conditions.

[0049] Figure 2(e) shows the load-time history curve of a measuring point at the left rear hanger of the rear suspension of an electric truck under a constant speed of 80 km / h and half load conditions.

[0050] Figure 2(f) shows the load-time history curve of a measuring point at the left rear hanger of the rear suspension of an electric truck under a constant speed of 100km / h and half-load conditions.

[0051] Figure 3 This is a schematic diagram of the combination of weighted composite load-time history in one embodiment of the present invention;

[0052] Figure 4 This is a finite element cloud diagram illustrating the fatigue life distribution of the vehicle frame in one embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0054] Embodiment one

[0055] The embodiment provides a vehicle frame fatigue prediction and monitoring method based on Internet of Vehicles twinning, so as to improve the accuracy and real-time performance of fatigue life prediction and in-service monitoring of a commercial vehicle frame.

[0056] As shown in Figure 1 The method first acquires the Internet of Vehicles big data generated by a new energy commercial vehicle under actual running conditions, carries out real measurement, calibration and combination of road loads by identifying and counting real use scenarios and running conditions, constructs a high-representative weighted composite load-time history, and realizes accurate evaluation of the fatigue life of the frame under real service conditions in combination with finite element analysis. On this basis, a frame digital twinning model is established for each vehicle in the cloud, real-time running data uploaded by the vehicle are acquired and combined with standard loads, the residual fatigue life state of the frame is dynamically updated through a Miner damage accumulation method. When the residual life reaches a set warning threshold, a warning information can be automatically triggered.

[0057] For the convenience of description, a certain type of electric cargo vehicle is taken as an example to describe the specific implementation process of the method in detail.

[0058] I. Identification and statistical analysis of frame use scenarios and running conditions

[0059] (1) Internet of Vehicles historical data acquisition

[0060] In this embodiment, the Internet of Vehicles running data of 500 electric cargo vehicles of a certain type of a commercial vehicle manufacturing enterprise from 2021 to 2024 is collected as an analysis sample. The collected data includes vehicle identification code, time stamp, vehicle latitude and longitude, speed, acceleration, steering signal, load information and road condition information and other multi-dimensional running parameters.

[0061] (2) Internet of Vehicles data preprocessing

[0062] To improve the data quality and ensure the accuracy of subsequent analysis, the collected historical data of Internet of Vehicles is systematically preprocessed. The specific operations include: unifying the timestamp and location information format, constructing the mapping relationship between vehicle trajectory and time series; detecting and removing invalid data caused by sensor failure, communication packet loss or signal drift; to further suppress high-frequency noise and retain data trends, Savitzky-Golay filter is used to smooth the cleaned data, ensuring that the retained information has good physical interpretation and continuity.

[0063] (3) Identification and statistical analysis of use scenarios and operating conditions

[0064] The driving speed, acceleration, steering signal, road condition information and load information are selected as the operating condition representation variables. First, principal component analysis (PCA) is used to reduce the dimension of the multi-dimensional operating condition variables to construct a comprehensive operating condition feature space; then, the density-based spatial clustering algorithm (DBSCAN) is used for unsupervised clustering analysis of the dimension reduction results to identify the typical use scenarios and operating conditions of electric cargo vehicles, and the proportion of each type of operating condition is counted. The use scenario and operating condition distribution extracted in this embodiment are shown in Tables 1, 2 and 3.

[0065] Table 1 Operating condition proportion

[0066]

[0067] Table 2 Use scenario proportion

[0068]

[0069] Table 3 Load proportion

[0070]

[0071] II. Frame multi-condition fatigue load measurement and modeling

[0072] (1) Determination of frame load measurement point position

[0073] According to the structural arrangement characteristics of the test vehicle, load measurement points are arranged at the key stress parts of the frame. The number of single-sided measurement points is usually 6-10, and the measurement points are symmetrically arranged on the left and right sides of the frame. In this embodiment, 8 measurement points are arranged on the left and right sides of the frame, including: cab front connection point, cab rear connection point, front suspension front lug connection point, front suspension rear lug connection point, battery compartment front connection point, battery compartment rear connection point, rear suspension front lug connection point, and rear suspension rear lug connection point.

[0074] (2) Strain gauge pasting and measurement equipment connection

[0075] To reduce the interference of non-target direction load on the measurement results and improve the accuracy of three-axis strain measurement, a strain gauge should be pasted on the upper side, middle and lower side of each measuring point of the vehicle frame longitudinal beam along the longitudinal direction (X direction) of the vehicle frame. Each measuring point is connected to the strain measurement system using the 1 / 4 bridge connection method to simplify the measurement wiring and improve the signal channel configuration efficiency. All strain signals are connected to the dynamic signal test analysis system to realize the synchronous acquisition and recording of the three-dimensional strain response of the measuring point position.

[0076] (3) Fatigue load real vehicle test of vehicle frame

[0077] According to the use scenarios and operating conditions obtained by statistical analysis of the electric cargo vehicle Internet of Vehicles historical data, a vehicle frame load real vehicle test scheme is developed, and the test scheme of the embodiment is shown in Table 4. According to the test working condition combination listed in Table 4, real vehicle test is carried out in the vehicle test field. During the test, the vehicle travels according to the preset operating condition, and the strain response signals of each measuring point of the vehicle frame are synchronously collected by the measurement system, and the strain-time history is recorded, which provides high-quality basic data support for subsequent load inversion and fatigue load spectrum construction.

[0078] Table 4 Vehicle frame dynamic load test scheme

[0079]

[0080] (4) Calibration and reduction of multi-condition load

[0081] After completing the real vehicle test, the strain-time history of the vehicle frame under each typical test condition has been obtained. In order to convert the strain response into load-time history, a static loading calibration experiment needs to be carried out to establish the strain-load conversion relationship.

[0082] During the calibration process, the pasting method and wiring arrangement of the strain gauge should be consistent with the road test to ensure the consistency of the measurement conditions. The calibration method of the X direction load is as follows: mechanical jack loading is used at the left and right sides of the front end of the vehicle frame, and the loading values are 500 kg, 1000 kg and 1500 kg, respectively. The loading direction is consistent with the centroid axis of the vehicle frame longitudinal beam section; the Y direction calibration is carried out by lateral loading near the 16 measuring points on the outside of the longitudinal beam, and the loading values are 200 kg, 400 kg and 600 kg, respectively; the Z direction calibration is carried out by vertical loading near the 16 measuring points above the longitudinal beam, and the loading values are also 200 kg, 400 kg and 600 kg. According to the strain data collected during the loading process and the corresponding load value , the calibration coefficients of each measuring point in the X, Y and Z directions are calculated :

[0083]

[0084] Based on the calibration coefficients described above , 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, and the load-time history curve of the vehicle frame under typical test conditions obtained is shown in Figures 2(a) to 2(f) .

[0085] Table 5 Calibration coefficients of the vehicle frame in each direction

[0086]

[0087] III. Fatigue life simulation prediction based on weighted load

[0088] (1) Construction of weighted composite load-time history

[0089] Based on the typical use scenarios and operating condition statistics obtained from historical data analysis of the Internet of Vehicles, the vehicle frame load-time history obtained under each test condition is weighted and combined according to the proportion of each type of road, operating condition (such as uniform speed, acceleration, braking, and turning), and load state (full load, half load, and empty load), thereby constructing a weighted composite load-time history based on real user conditions. This history is used as the input load for subsequent fatigue life analysis. The combination in this embodiment is shown in Figure 3 .

[0090] (2) Establishment of vehicle frame finite element model and stress response analysis

[0091] Based on the three-dimensional geometric model and material attribute information of the electric cargo vehicle frame, a finite element analysis model is established, the mesh is reasonably divided, and the constraints are set using the inertia release method. Unit loads (usually 1 kN) are applied at each measuring point location, and are independently loaded in the X, Y, and Z directions to obtain static stress response results. According to the linear elastic superposition principle, the unit response is superimposed in proportion to the weighted composite load-time history to reconstruct the stress-time history of each measuring point, which is used as the input for fatigue life assessment.

[0092] (3) Construction of stress cycle spectrum

[0093] Based on the stress-time history data of each measuring point in the X, Y, and Z directions, the rainflow counting method is used to identify cycles and count amplitudes, and the cycle number distribution in different stress amplitude intervals is extracted to construct the stress cycle spectrum of the vehicle frame structure under real user conditions. This stress cycle spectrum accurately reflects the fatigue stress response characteristics of the key parts of the vehicle frame under actual conditions and is a direct basis for life calculation.

[0094] (4) Fatigue life calculation

[0095] Based on the stress cycle spectrum established at each measuring point, the fatigue life of the chassis is assessed using the stress-life method (S-N method) combined with the Miner linear cumulative damage criterion. According to the S-N curve of the chassis material, the withstandable cycle life corresponding to each stress amplitude level is calculated. N i Then, combined with the actual number of cycles at each amplitude value n i Calculate the cumulative fatigue damage value. D :

[0096]

[0097] According to the Miner criterion, when D A value ≥1 indicates that the structure has reached the fatigue failure threshold. This allows us to estimate the fatigue life cycle count of the chassis under actual user operating conditions. N for:

[0098]

[0099] Furthermore, based on the distance traveled by the test vehicle during the weighted composite load-time history... L c It can calculate the fatigue life mileage of the chassis under real-world user operating conditions. L for:

[0100] L = N × L c

[0101] In this embodiment, the fatigue life cycle count of the weak point of the frame is... N 2.924×10 6 The distance traveled by the test vehicle during the weighted composite load-time history. L c The fatigue life mileage of the chassis under actual user operating conditions is calculated to be 0.386 km. L It is 1,128,660 kilometers.

[0102] (5) Visualization and evaluation of lifetime distribution results:

[0103] Based on the fatigue life analysis results, the fatigue life distribution characteristics and damage accumulation trends of each region of the chassis structure are visualized in the finite element model using an equivalent life cloud map. This provides a clear overview of the fatigue life distribution and damage accumulation trends in different areas of the chassis structure. In this embodiment, the fatigue life distribution of the chassis under actual user operating conditions is as follows: Figure 4 As shown.

[0104] IV. Online monitoring and early warning of remaining lifetime based on digital twins

[0105] (1) Construction and deployment of digital twin model of vehicle frame

[0106] A digital twin model of vehicle frame corresponding to the structure of each new energy commercial vehicle in operation is constructed in the cloud. The twin model is based on the finite element model established in the third part, and integrates the fatigue analysis parameters of the vehicle frame (such as unit load static stress response, material S–N curve, damage accumulation criterion, etc.) to form a calculation module that can be used for fatigue state simulation and life evolution analysis. Each twin model is bound to the corresponding vehicle through the vehicle identification number (VIN), ensuring the accuracy and consistency of the individualized structure model.

[0107] (2) Real-time working condition recognition and standard load reconstruction

[0108] During vehicle operation, the vehicle internet operation data uploaded by the vehicle is continuously collected, including driving speed, acceleration, steering signal, load information, and road condition information, etc. Based on the above multi-dimensional operation parameters, the current working condition state is identified in real time. The identification result will be used as an index to dynamically call the load–time history templates of each working condition constructed by experiments. To improve the individualized adaptability of load input, the standard load history is scaled in amplitude and adjusted in period according to the current actual operation parameters of the vehicle, thereby constructing an approximate load–time history reflecting the current working condition intensity and continuity, providing continuous and accurate load input for subsequent fatigue damage calculation.

[0109] (3) Online damage accumulation and life updating

[0110] 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 in real time using the rainflow counting method. Combined with the S–N curve of the vehicle frame material and the Miner linear cumulative damage theory, the incremental fatigue damage value D inc in the current operation period is calculated D his , and is superimposed with the historical cumulative damage value D tot :

[0111] D tot = D his + D inc

[0112] According to the fatigue failure criterion, when D tot ≥1, the structure is considered to reach the fatigue limit. Then the remaining life ratio of the current vehicle frame can be calculated R :

[0113] R =1 - D tot

[0114] the fatigue life mileage of the vehicle frame under the real working condition of the user calculated in combination with the third part L , the remaining fatigue life mileage of the vehicle frame can be updated in real time L rem :

[0115] L rem = L × R

[0116] On the basis of each period of vehicle uploaded Internet of Vehicles data, the above-mentioned calculation logic is periodically executed at a set frequency, and the fatigue state of the vehicle frame is dynamically evaluated.

[0117] (4) Life threshold early warning and feedback push

[0118] When the remaining fatigue life mileage L rem reaches a preset early warning threshold (such as the remaining life being less than 20% of the original life), the system automatically triggers an early warning mechanism, and pushes life early warning information and dangerous position data to the vehicle management platform, the operator and the design and development end, realizing real-time monitoring and closed-loop feedback control of the health state of the vehicle frame structure.

[0119] Through the above steps, the present application not only realizes the precise evaluation of the fatigue life of the electric cargo vehicle frame structure under actual service conditions, but also, based on Internet of Vehicles data and digital twin models, constructs a fatigue state real-time monitoring and remaining life dynamic updating mechanism for vehicles in service. It provides reliable technical support and intelligent solutions for the durability design, service safety management and whole life cycle health maintenance of the vehicle frame structure.

[0120] Embodiment Two

[0121] Based on the above method, the present embodiment provides a vehicle frame fatigue prediction and monitoring system based on Internet of Vehicles twin, which mainly includes an Internet of Vehicles data acquisition module, a vehicle frame fatigue life prediction module and a remaining fatigue life monitoring module. Among them, the Internet of Vehicles data acquisition module is used to acquire Internet of Vehicles historical data and vehicle continuously uploaded Internet of Vehicles real-time data, and to preprocess the acquired data. The vehicle frame fatigue life prediction module is used to evaluate the fatigue life mileage of the vehicle, and to visually display the equivalent life cloud map in the finite element analysis model of the vehicle frame, so as to intuitively present the fatigue life distribution characteristics and damage accumulation trend of each region of the vehicle frame structure. The remaining fatigue life monitoring module is used to update the remaining fatigue life mileage of the vehicle frame in real time, and when the remaining fatigue life mileage reaches a preset threshold, an early warning mechanism is automatically triggered, and life early warning information is sent.

[0122] In some embodiments, the system further comprises a vehicle frame digital twin module configured to build a vehicle frame digital twin model corresponding to the structure of each vehicle in operation in the cloud, each twin model being bound to the corresponding vehicle by a unique vehicle identification code.

[0123] The system described above can perform the vehicle frame fatigue prediction and monitoring method described in embodiment one, has the corresponding functional modules and beneficial effects of the method, and the technical details not described in detail in this embodiment can be referred to the vehicle frame fatigue prediction and monitoring method provided in embodiment one of the present application.

[0124] From the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general hardware platform, and of course, it can also be implemented by hardware. Based on such understanding, the above technical solutions or the part that contributes to the related art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some part of the embodiments.

[0125] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; under the idea of the present application, 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 changes of the different aspects of the present application as described above. In order to be brief, they are not provided in detail; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A vehicle frame fatigue prediction and monitoring method based on vehicle networking twinning, characterized in that, Comprise: S1, based on the history of Internet of Vehicles data on the use of vehicle scene and working condition proportion analysis statistics; S2, according to the use of scene and working condition proportion, the frame load real vehicle test to obtain the strain response signal of each measuring point, record strain-time history; S3, static loading calibration test is carried out on the frame, and the strain-time history obtained under each test condition is converted into corresponding load-time history; S4, the load-time history under each test condition is combined by weighting, and the weighted composite load-time history is obtained; S5, finite element analysis is carried out on the frame, the static stress response under unit load of each measuring point is obtained, and the unit response is superimposed according to the proportion of weighted composite load-time history, and the stress-time history of each measuring point is reconstructed; S6, based on the stress-time history of each measuring point, the rain flow counting method is used to identify the cycle and amplitude statistics of stress history, the cycle number distribution in different stress amplitude interval is extracted, and the stress cycle spectrum of frame structure is constructed; S7, based on the stress cycle spectrum of each measuring point, the stress-life method is used to combine the Miner linear cumulative damage criterion to evaluate the fatigue life of the frame: According to the stress-life curve of the frame material, the cycle life of each stress amplitude level is calculated respectively N i , and the actual cycle number of each amplitude is combined n i , the cumulative fatigue damage value is calculated D ; according to the Miner linear cumulative damage criterion, when D ≥1, it is considered that the structure reaches the fatigue failure threshold According to the accumulated fatigue damage value D Obtaining the fatigue life cycle number N ; according to the distance traveled by the vehicle in the weighted complex load-time history L c , the fatigue life mileage of the vehicle frame is calculated L ; wherein, , , ; S8, the digital twin model of the frame is constructed, the current Internet of Vehicles operation data of the vehicle is calculated and real-time displayed, and the residual fatigue life mileage of the frame is calculated and real-time displayed, and the residual fatigue life mileage of the frame is calculated and real-time displayed, and the residual fatigue life mileage of the frame is calculated and real-time displayed, and the residual fatigue life mileage of the frame is calculated and real-time displayed, and the residual fatigue life mileage of the frame is calculated and real-time displayed, and the residual fatigue life mileage of the frame is calculated and real-time displayed, and the residual fatigue life 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fatigue life mileage of the frame is calculated and real-time displayed, and the residual fatigue life mileage of the frame is calculated and real-time displayed, and the residual fatigue life mileage of the frame is calculated and real-time displayed, and the ​ ​ ​ ​ The incremental fatigue damage value in the current running cycle is calculated D inc The historical cumulative damage value is superimposed D his The total fatigue damage at the current time is continuously updated D tot = D his + D inc The remaining life ratio of the current vehicle frame is calculated R =1- D tot When D tot ≥1, the vehicle frame structure is considered to reach the fatigue limit According to the remaining life proportion R , the remaining fatigue life mileage of the vehicle frame is calculated and updated in real time L rem = L × R .

2. The method of frame fatigue prediction and monitoring of claim 1, wherein, ​ ​ 3. The method of frame fatigue prediction and monitoring of claim 1, wherein, ​ ​ 4. The method of frame fatigue prediction and monitoring of claim 1, wherein, ​ According to the strain data collected in the static loading process With the corresponding load value , the calibration coefficient of each measuring point in X, Y, Z direction is calculated ; Based on the calibration coefficient Convert the strain-time history under each test condition to the corresponding load-time history.

5. The method of frame fatigue prediction and monitoring of claim 1, wherein, ​ Based on the analysis and statistical results of the use scenarios and working condition proportions, the vehicle frame load-time history obtained under each test working condition is weighted and combined according to the proportions of various road types, running working conditions and load states, to obtain a weighted composite load-time history.

6. The method of frame fatigue prediction and monitoring of claim 1, wherein, Based on the fatigue life evaluation results, the equivalent life cloud map form is used for visual display in the finite element analysis model of the vehicle frame, so as to intuitively present the fatigue life distribution characteristics and damage accumulation trend of each region of the vehicle frame structure.

7. A system for frame fatigue prediction and monitoring based on the method of any one of claims 1 to 6, characterized by The system comprises a vehicle networking data acquisition module, a vehicle frame fatigue life prediction module and a residual fatigue life monitoring module. The vehicle networking data acquisition module is used for acquiring vehicle networking historical data and vehicle networking real-time data continuously uploaded by vehicles, and pre-processing the acquired data. The vehicle frame fatigue life prediction module is used for evaluating the fatigue life mileage of the vehicle, and visually displaying the equivalent life cloud map form in the finite element analysis model of the vehicle frame, so as to intuitively present the fatigue life distribution characteristics and damage accumulation trend of each region of the vehicle frame structure. The residual fatigue life monitoring module is used for updating the residual fatigue life mileage of the vehicle frame in real time, and automatically triggering a warning mechanism and sending life warning information when the residual fatigue life mileage reaches a preset threshold. The system further comprises a vehicle frame digital twin module; the digital twin module constructs a vehicle frame digital twin model corresponding to the structure of 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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