Heavy engineering vehicle life prediction and whole vehicle attitude detection method, system and device and medium

Abstract

The invention provides a method, a system, equipment and a medium for predicting the service life of a heavy engineering vehicle and detecting the posture of the whole vehicle, and belongs to the technical field of medium and heavy trucks, the method comprises the following steps: carrying out simulation and fatigue test on the whole vehicle to determine the stress position of the vehicle, and arranging a sensing element at the stress position; constructing a life loss model and a whole vehicle attitude model; collecting vehicle running state data, impact data of key positions in real-time running of the vehicle and vehicle load data; analyzing the stress-strain condition of the key position according to the impact data, and matching with a life loss model to determine the loss condition of each key position; and according to the load data and the vehicle running state data, carrying out whole vehicle attitude calculation analysis and driving intention identification, and judging whether the vehicle has a rollover risk or not. Accurate prediction of the service life of the heavy engineering vehicle and real-time detection of the posture of the whole vehicle are achieved, the maintenance accuracy and efficiency are improved, and the maintenance cost and the safety risk of vehicle operation are reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of medium and heavy-duty trucks, and specifically relates to a method, system, equipment and medium for predicting the life of a heavy-duty engineering vehicle and detecting the posture of the entire vehicle. Background Art

[0002] Medium and heavy-duty trucks, such as muck trucks and mixer trucks, are important construction equipment. The life of their key parts is not only directly related to the safety and reliability of the vehicle, but also directly affects the vehicle's attendance rate and the owner's income. Such vehicles usually have long daily mileage and high attendance rates. They frequently enter and exit construction sites and unloading sites. The vehicles are frequently twisted and impacted, causing the frame, suspension, cab suspension and other key parts to be under tremendous pressure. For example, the frame of a muck truck is a key component that supports the entire vehicle. All assemblies are attached to the frame and fixed. Once the frame is damaged, it will cause immeasurable damage to the entire vehicle. However, traditional maintenance management methods rely on experience judgment or regular inspections, or only inspect a single location. It is difficult to accurately grasp the life and wear of the entire vehicle, resulting in high maintenance costs and low efficiency.

[0003] At present, there is no special life management method for engineering vehicles, which are medium and heavy-duty trucks. Common maintenance measures are limited to regular tightening of fuel and suspension bolts, or testing of single positions, and lack of comprehensive maintenance and wear detection of key positions. For example, key positions such as rear suspension thrust rods, frame crossbeams, cab suspension and cab flip axis directly affect the driving state of the vehicle, but the existing solutions cannot effectively monitor the status of these positions, which poses serious safety hazards. Summary of the invention

[0004] In a first aspect, the present application provides a method for predicting the life of a heavy engineering vehicle and detecting the posture of the vehicle, comprising the following steps: S1. Conduct simulation and fatigue tests on the vehicle, determine the stress position of the vehicle based on the simulation and fatigue test data, and arrange sensor elements at the stress position; S2. Construct a life loss model based on simulation and fatigue test data, and construct a vehicle posture model based on vehicle load data and vehicle operation status data; S3. Collecting vehicle operation status data, and collecting impact data and vehicle load data at key positions in real-time vehicle operation through sensor elements; S4. Analyze the stress and strain conditions at key positions based on the impact data, match the stress and strain analysis results at key positions of the vehicle with the life loss model, determine the loss conditions at each key position based on the matching results, and issue an alarm when the loss at a certain key position is lower than a threshold; S5. Calculate and analyze the vehicle posture based on the load data, identify the driving intention based on the vehicle operation status data, and then combine the calculation and analysis results of the vehicle posture with the driving intention to determine whether the vehicle has a rollover risk, and issue an alarm when there is a rollover risk.

[0005] Furthermore, the specific steps of step S1 are as follows: S11. Conduct simulation and fatigue tests on the main load-bearing parts and connectors of the vehicle, and record the simulation and fatigue test data; S12. Analyze the simulation and fatigue test data to determine the stress positions of the main load-bearing parts and connectors; S13. The stress-bearing positions of the main load-bearing parts and connecting parts are used as monitoring points, and sensor elements are arranged at the monitoring points.

[0006] Further, the main bearing components include a frame, a subframe, a suspension, a cab suspension and a V-shaped thrust rod; The connecting members include riding bolts, suspension bolts and frame longitudinal beam cross beam bolts; The sensing element includes a pressure sensor, a gyroscope and a strain gauge.

[0007] Furthermore, the specific steps of step S2 are as follows: S21. Determine the strain threshold that causes damage to each monitoring point based on simulation and fatigue test data ; S22. For monitoring points exceeding the strain threshold Strain The current cyclic stress life can be calculated using the following local strain method formula :

[0008] in, represents strain, represents the current cyclic stress life, represents the elastic modulus, represents the fatigue strength index, represents the fatigue strength coefficient, represents the fatigue continuation coefficient, Indicates the fatigue continuation coefficient corresponding to the fatigue life curve 2N=1 ordinate distance; S23. Based on the current cyclic stress life The equivalent damage of the monitoring point is calculated by the following equivalent damage formula And the cumulative equivalent damage D: ; ; in, The number of impacts under different loads, m is the strain threshold value exceeded by the monitoring point The number of times; S24. Based on equivalent damage The monitoring point life results are calculated by the following life calculation formula : ; S25. The local strain method formula, equivalent damage formula, and life calculation formula are used as life loss models; S26. Construct a vehicle center of mass calculation formula based on the vehicle load data, and construct a driver driving intention recognition model based on the vehicle operating status data.

[0009] Furthermore, the specific steps of step S26 are as follows: S261. Obtain chassis mass m1, chassis mass center lateral distance x1, chassis mass center longitudinal distance h1, cargo box mass m2, cargo box mass center lateral distance x2, cargo box mass center longitudinal distance h2, cargo mass m3, cargo mass center lateral distance x3, cargo mass center longitudinal distance h3, subframe and accessory mass m4, subframe and accessory mass center lateral distance x4, subframe and accessory mass center longitudinal distance h4, and vehicle total mass M from vehicle load data; S262. The calculation formula for the center of mass of the vehicle is as follows:

[0010]

[0011] Among them, X is the lateral distance of the vehicle's center of mass, and H is the longitudinal distance of the vehicle's center of mass; S263. Construct a driver's driving intention recognition model based on the real-time vehicle speed, acceleration, and accelerator pedal depth in the vehicle operation status data.

[0012] Furthermore, the specific steps of step S4 are as follows: S41. Obtain the strain of each monitoring point in the collected impact data , and determine whether the strain threshold is exceeded ; If yes, go to step S42; S42. Calculate the current cyclic stress life by the local strain method formula , combined with the number of impacts under different loads Calculate equivalent damage using the equivalent damage formula And the accumulated equivalent damage D, and finally the life calculation formula is used to calculate the life result of the monitoring point ; S43. Determine the monitoring point life results of the monitoring point Is it below the threshold? If so, an alarm is issued for the monitoring point below the threshold; If not, proceed to step S44; S44. Determine whether the monitoring point is a connector; If yes, go to step S45; If not, proceed to step S5; S45. Determine whether the strain value of the monitoring point is 0; If so, it is determined that the connection is loose, the preload force disappears, and an alarm is issued; If not, go to step S5.

[0013] Furthermore, the specific steps of step S5 are as follows: S51. Collect chassis mass m1, chassis mass center lateral distance x1, chassis mass center longitudinal distance h1, cargo box mass m2, cargo box mass center lateral distance x2, cargo box mass center longitudinal distance h2, cargo mass m3, cargo mass center lateral distance x3, cargo mass center longitudinal distance h3, subframe and accessory mass m4, subframe and accessory mass center lateral distance x4, subframe and accessory mass center longitudinal distance h4 and vehicle total mass M, and use vehicle mass center calculation formula to calculate X as vehicle mass center lateral distance, H as vehicle mass center longitudinal distance, and obtain real-time mass center position; S52. Collect real-time vehicle speed, acceleration, and accelerator pedal depth data from the vehicle operation status data, and use the driver's driving intention recognition model to predict driving intention; S53. Determine the change trend of the center of mass based on the predicted driving intention, and then determine whether the vehicle has a rollover risk in combination with the real-time center of mass position; If so, issue a warning for the rollover risk and end; If not, return to step S3.

[0014] In a second aspect, the embodiment of the present application further provides a heavy engineering vehicle life prediction and vehicle posture detection system, including: The force position determination module is used to perform simulation and fatigue test on the whole vehicle, determine the force position of the vehicle according to the simulation and fatigue test data, and arrange sensor elements at the force position; Model building module, used to build a life loss model based on simulation and fatigue test data, and build a vehicle posture model based on vehicle load data and vehicle operating status data; A data acquisition module is used to collect vehicle operation status data, and to collect impact data and vehicle load data at key positions in real-time vehicle operation through sensor elements; The life analysis module is used to analyze the stress and strain conditions at key positions based on the impact data, match the stress and strain analysis results at key positions of the vehicle with the life loss model, determine the loss conditions at each key position based on the matching results, and issue an alarm when the loss at a certain key position is lower than the threshold; The posture analysis module is used to calculate and analyze the posture of the entire vehicle based on the load data, identify the driving intention based on the vehicle operation status data, and then combine the calculation and analysis results of the vehicle posture with the driving intention to determine whether the vehicle has a rollover risk, and issue an alarm when there is a rollover risk.

[0015] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the steps of the heavy engineering vehicle life prediction and vehicle posture detection method as described in the first aspect are implemented.

[0016] In a fourth aspect, an embodiment of the present application further provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the heavy engineering vehicle life prediction and vehicle posture detection method as described in the first aspect.

[0017] It can be seen from the above technical solutions that the present invention has the following advantages: In the heavy-duty engineering vehicle life prediction and vehicle posture detection method, system, equipment and medium provided in the present application, the stress position is determined and the sensor elements are arranged through simulation and fatigue testing, and the life loss model and the vehicle posture model are combined to achieve accurate prediction of the heavy-duty engineering vehicle life and real-time detection of the vehicle posture. The vehicle posture prediction and life prediction results can also be fed back to the design engineer for vehicle design optimization; it not only improves the accuracy and efficiency of maintenance and reduces maintenance costs, but also effectively reduces the safety risks of vehicle operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 It is a flow chart of the heavy-duty engineering vehicle life prediction and vehicle posture detection method of the present invention.

[0020] Figure 2 It is a schematic diagram of the heavy-duty engineering vehicle life prediction and vehicle posture detection system of the present invention. DETAILED DESCRIPTION

[0021] In the specific steps of the heavy engineering vehicle life prediction and vehicle posture detection method described in detail below, various embodiments of the present disclosure will be described more comprehensively. The present disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives that fall within the spirit and scope of the various embodiments of the present disclosure.

[0022] For example, medium and heavy trucks, such as muck trucks and mixer trucks, play an indispensable role in the construction field. The service life of key components of these vehicles, such as frames, suspensions, and cab suspensions, not only affects the safety and reliability of the vehicles, but also directly affects the operational efficiency of the vehicles and the economic benefits of the owners. These vehicles have long daily mileage, frequent attendance, and often shuttle between construction sites and unloading sites. They experience frequent torsion and impact, which puts great pressure on key parts such as frames and suspensions. Taking muck trucks as an example, the frame is the supporting skeleton of the entire vehicle, and all assembly parts are attached to it. Once the frame is damaged, it will have an immeasurable impact on the performance and safety of the entire vehicle. However, traditional vehicle maintenance methods mostly rely on experience judgment or regular routine inspections, and often only test single components, and cannot fully and accurately grasp the life and wear of the entire vehicle. This not only leads to high maintenance costs, but also makes maintenance efficiency low.

[0023] At present, there is still a lack of special methods for the life management of medium and heavy-duty trucks. Common maintenance methods are limited to regular tightening of fuel systems, suspension bolts, etc., or individual inspections of individual components. However, key components such as rear suspension thrust rods, frame crossbeams, cab suspensions, and cab flip shafts have a direct impact on the vehicle's driving performance, but the existing maintenance solutions cannot effectively monitor these components. This lack of monitoring undoubtedly poses a hidden danger to the safe operation of the vehicle.

[0024] In summary, the life management and comprehensive maintenance of key components of medium and heavy-duty trucks are issues that need to be urgently addressed. Traditional maintenance methods can no longer meet the high requirements of modern construction vehicles for safety and reliability, and a more scientific and comprehensive maintenance management system needs to be developed to ensure the safe and efficient operation of vehicles.

[0025] In view of the above problems, this embodiment provides a method for predicting the life of a heavy-duty engineering vehicle and detecting the posture of the entire vehicle.

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] See also Figure 1 The figure is a flowchart of a method for predicting the life of a heavy engineering vehicle and detecting the posture of the whole vehicle in a specific embodiment, and the method comprises the following steps: S1. Conduct simulation and fatigue tests on the vehicle, determine the stress position of the vehicle based on the simulation and fatigue test data, and arrange sensor elements at the stress position; It should be noted that by simulating and fatigue testing the whole vehicle, the stress positions of the vehicle can be accurately determined; and by placing sensor elements at these stress positions, the stress conditions of the vehicle during operation can be monitored in real time, providing accurate data support for subsequent life prediction and vehicle posture detection; this method can more effectively ensure the safety and reliability of the vehicle than traditional experience judgment or regular maintenance; S2. Construct a life loss model based on simulation and fatigue test data, and construct a vehicle posture model based on vehicle load data and vehicle operation status data; It should be noted that the life loss model constructed based on simulation and fatigue test data can accurately reflect the life loss of key positions of the vehicle; at the same time, the vehicle posture model constructed based on vehicle load data and vehicle operation status data can monitor the vehicle's operation status in real time and provide technical support for preventing safety accidents such as vehicle rollover; the establishment of these two models provides a basis for subsequent life prediction and vehicle posture detection; S3. Collecting vehicle operation status data, and collecting impact data and vehicle load data at key positions in real-time vehicle operation through sensor elements; It should be noted that by collecting vehicle operation status data, impact data at key locations, and vehicle load data, we can fully understand the force conditions and operation status of the vehicle during operation, provide information for subsequent analysis and judgment, and help accurately predict the life of the vehicle and detect the posture of the entire vehicle; S4. Analyze the stress and strain conditions at key positions based on the impact data, match the stress and strain analysis results at key positions of the vehicle with the life loss model, determine the loss conditions at each key position based on the matching results, and issue an alarm when the loss at a certain key position is lower than a threshold; It should be noted that by analyzing the stress and strain conditions of key positions based on impact data, the stress state and loss conditions of key positions of the vehicle can be accurately determined; by matching the analysis results with the life loss model, the life of each key position can be accurately predicted, and an alarm can be issued when the loss of a key position is lower than the threshold, so as to promptly remind the owner to perform maintenance or replacement, thereby avoiding the occurrence of safety accidents; S5. Calculate and analyze the vehicle posture based on the load data, identify the driving intention based on the vehicle operation status data, and then combine the calculation and analysis results of the vehicle posture with the driving intention to determine whether the vehicle has a rollover risk, and issue an alarm when there is a rollover risk; It should be noted that by calculating and analyzing the vehicle posture based on load data and vehicle operating status data, the vehicle's operating posture can be monitored in real time to determine whether the vehicle is at risk of rollover. This method is more accurate and reliable than traditional experience-based judgment, and can promptly detect and prevent safety accidents such as vehicle rollovers, thereby ensuring the safety of vehicles and personnel.

[0028] This embodiment determines the stress position and arranges the sensor elements through simulation and fatigue testing, and combines the life loss model and the vehicle posture model to realize the prediction of the life of heavy-duty engineering vehicles and the real-time detection of the vehicle posture, thereby improving the accuracy and efficiency of maintenance and reducing maintenance costs.

[0029] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process in this embodiment, another heavy engineering vehicle life prediction and vehicle posture detection method is provided, which includes the following steps: S1. Perform simulation and fatigue test on the whole vehicle, determine the stress position of the vehicle according to the simulation and fatigue test data, and arrange sensor elements at the stress position; the specific steps of step S1 are as follows: S11. Conduct simulation and fatigue tests on the main load-bearing parts and connectors of the vehicle, and record the simulation and fatigue test data; S12. Analyze the simulation and fatigue test data to determine the stress positions of the main load-bearing parts and connectors; S13. The stress positions of the main bearing members and the connecting members are used as monitoring points, and sensing elements are arranged at the monitoring points; Exemplarily, the main load-bearing components include a frame, a subframe, a suspension, a cab suspension, and a V-type thrust rod; the connecting components include riding bolts, suspension bolts, and frame longitudinal beam cross beam bolts; the sensing elements include pressure sensors, gyroscopes, and strain gauges; It should be noted that by conducting simulation and fatigue tests on the main load-bearing parts and connectors of the vehicle and recording relevant data, the stress positions and stress conditions of these parts can be accurately determined; by arranging sensor elements at the stress positions, the stress changes of these parts during operation can be monitored in real time, providing accurate data support for subsequent analysis and judgment; thus, it helps to improve the safety and reliability of the vehicle and reduce maintenance costs; S2. Construct a life loss model based on simulation and fatigue test data, and construct a vehicle posture model based on vehicle load data and vehicle operation status data; the specific steps of step S2 are as follows: S21. Determine the strain threshold that causes damage to each monitoring point based on simulation and fatigue test data ; S22. For monitoring points exceeding the strain threshold Strain The current cyclic stress life can be calculated using the following local strain method formula :

[0030] in, represents strain, represents the current cyclic stress life, represents the elastic modulus, represents the fatigue strength index, represents the fatigue strength coefficient, represents the fatigue continuation coefficient, Indicates the fatigue continuation coefficient corresponding to the fatigue life curve 2N=1 ordinate distance; S23. Based on the current cyclic stress life The equivalent damage of the monitoring point is calculated by the following equivalent damage formula And the cumulative equivalent damage D: ; ; in, The number of impacts under different loads, m is the strain threshold value exceeded by the monitoring point The number of times; S24. Based on equivalent damage The monitoring point life results are calculated by the following life calculation formula : ; S25. The local strain method formula, equivalent damage formula, and life calculation formula are used as life loss models; S26. Construct a vehicle center of mass calculation formula based on vehicle load data, and construct a driver driving intention recognition model based on vehicle operation status data; It should be noted that by determining the strain threshold, calculating the current cyclic stress life, calculating the equivalent damage and cumulative equivalent damage, and calculating the life results of the monitoring points, the life loss of the key positions of the vehicle can be accurately predicted; at the same time, by constructing the vehicle center of mass calculation formula and the driver's driving intention recognition model, the vehicle's operating status can be monitored in real time, providing technical support for preventing safety accidents such as vehicle rollovers; making life prediction and vehicle posture detection more accurate and reliable; S3. Collecting vehicle operation status data, and collecting impact data and vehicle load data at key positions in real-time vehicle operation through sensor elements; S4. Perform stress and strain analysis at key positions based on impact data, match the stress and strain analysis results at key positions of the vehicle with the life loss model, determine the loss of each key position based on the matching results, and issue an alarm when the loss of a key position is lower than a threshold; the specific steps of step S4 are as follows: S41. Obtain the strain of each monitoring point in the collected impact data , and determine whether the strain threshold is exceeded ; If yes, go to step S42; S42. Calculate the current cyclic stress life by the local strain method formula , combined with the number of impacts under different loads Calculate equivalent damage using the equivalent damage formula And the accumulated equivalent damage D, and finally the life calculation formula is used to calculate the life result of the monitoring point ; S43. Determine the monitoring point life results of the monitoring point Is it below the threshold? If so, an alarm is issued for the monitoring point below the threshold; If not, proceed to step S44; S44. Determine whether the monitoring point is a connector; If yes, go to step S45; If not, proceed to step S5; S45. Determine whether the strain value of the monitoring point is 0; If so, it is determined that the connection is loose, the preload force disappears, and an alarm is issued; If not, proceed to step S5; It should be noted that by obtaining impact data, judging whether the strain exceeds the threshold, calculating the current cyclic stress life, calculating the equivalent damage and cumulative equivalent damage, judging whether the life of the monitoring point is lower than the threshold, etc., the stress state and loss of the key positions of the vehicle can be accurately judged; at the same time, special judgments are made on the connectors, and safety hazards such as loose connectors can be discovered in time, and the owner can be reminded to perform maintenance or replacement, making the life prediction more accurate and reliable; S5. Calculate and analyze the vehicle posture according to the load data and the vehicle operation status data, match the calculation and analysis results of the vehicle posture with the vehicle posture model, determine whether the vehicle has a rollover risk according to the matching results, and issue an alarm when there is a rollover risk; the specific steps of step S5 are as follows: S51. Collect chassis mass m1, chassis mass center lateral distance x1, chassis mass center longitudinal distance h1, cargo box mass m2, cargo box mass center lateral distance x2, cargo box mass center longitudinal distance h2, cargo mass m3, cargo mass center lateral distance x3, cargo mass center longitudinal distance h3, subframe and accessory mass m4, subframe and accessory mass center lateral distance x4, subframe and accessory mass center longitudinal distance h4 and vehicle total mass M, and use vehicle mass center calculation formula to calculate X as vehicle mass center lateral distance, H as vehicle mass center longitudinal distance, and obtain real-time mass center position; S52. Collect real-time vehicle speed, acceleration, and accelerator pedal depth data from the vehicle operation status data, and use the driver's driving intention recognition model to predict driving intention; S53. Determine the change trend of the center of mass based on the predicted driving intention, and then determine whether the vehicle has a rollover risk in combination with the real-time center of mass position; If so, issue a warning for the rollover risk and end; If not, return to step S3; For example, for a certain monitoring point, there is a monitoring point life result If the life span is below the threshold, the connection parts are loose, or there is a risk of rollover during driving, the warning information can be displayed on the central control screen of the vehicle instrument panel to remind the driver in time; It should be noted that by collecting vehicle load data and vehicle operating status data, calculating the real-time center of mass position, predicting the driver's driving intention, and judging whether the vehicle is at risk of rollover, the vehicle's operating status can be monitored in real time, and safety accidents such as vehicle rollover can be discovered and prevented in a timely manner; the refinement of these steps will help improve the accuracy and reliability of vehicle posture detection and ensure the safety of vehicles and personnel.

[0031] In an embodiment of the present invention, based on step S26, a possible embodiment is given below to illustrate its specific implementation scheme in a non-limiting manner.

[0032] The specific steps of step S26 are as follows: S261. Obtain chassis mass m1, chassis mass center lateral distance x1, chassis mass center longitudinal distance h1, cargo box mass m2, cargo box mass center lateral distance x2, cargo box mass center longitudinal distance h2, cargo mass m3, cargo mass center lateral distance x3, cargo mass center longitudinal distance h3, subframe and accessory mass m4, subframe and accessory mass center lateral distance x4, subframe and accessory mass center longitudinal distance h4, and vehicle total mass M from vehicle load data; S262. The calculation formula for the center of mass of the vehicle is as follows:

[0033]

[0034] Among them, X is the lateral distance of the vehicle's center of mass, and H is the longitudinal distance of the vehicle's center of mass; S263. Construct a driver's driving intention recognition model based on the real-time vehicle speed, acceleration, and accelerator pedal depth in the vehicle operation status data; Exemplarily, multiple sets of vehicle operation status data may be collected in advance, and driver driving intention labels may be pre-set according to real-time vehicle speed, acceleration, and accelerator pedal depth to obtain a data set; Divide the dataset into training and testing sets; Establish a BP neural network model, set the input layer, hidden layer, and output layer, and set the input layer to correspond to the real-time vehicle speed, acceleration, and accelerator pedal depth, set the output layer to correspond to the driver's driving intention, and set the activation function and number of iterations; The BP neural network model is iteratively trained through the training set, and the training is stopped when the prediction error is less than the threshold or the number of iterations is completed. Then the BP neural network model is tested through the test set, and the BP neural network model of the test set is used as the driver's driving intention recognition model; It should be noted that the lateral distance and longitudinal distance of each center of mass are based on the origin of the vehicle coordinates; by obtaining the vehicle load data and constructing the vehicle center of mass calculation formula, the vehicle's center of mass position can be accurately calculated; at the same time, a driver's driving intention recognition model is constructed based on the vehicle operating status data, which can monitor the driver's driving intention in real time and provide strong technical support for preventing safety accidents such as vehicle rollovers; thereby helping to improve the accuracy and reliability of vehicle posture detection.

[0035] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0036] like Figure 2As shown, the following is an embodiment of the heavy-duty engineering vehicle life prediction and vehicle posture detection system provided by the embodiment of the present disclosure. The system and the heavy-duty engineering vehicle life prediction and vehicle posture detection methods of the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiments of the heavy-duty engineering vehicle life prediction and vehicle posture detection system, reference can be made to the embodiments of the above-mentioned heavy-duty engineering vehicle life prediction and vehicle posture detection methods.

[0037] The system includes: The force position determination module is used to perform simulation and fatigue test on the whole vehicle, determine the force position of the vehicle according to the simulation and fatigue test data, and arrange sensor elements at the force position; Model building module, used to build a life loss model based on simulation and fatigue test data, and build a vehicle posture model based on vehicle load data and vehicle operating status data; A data acquisition module is used to collect vehicle operation status data, and to collect impact data and vehicle load data at key positions in real-time vehicle operation through sensor elements; The life analysis module is used to analyze the stress and strain conditions at key positions based on the impact data, match the stress and strain analysis results at key positions of the vehicle with the life loss model, determine the loss conditions at each key position based on the matching results, and issue an alarm when the loss at a certain key position is lower than the threshold; The posture analysis module is used to calculate and analyze the posture of the entire vehicle based on the load data, identify the driving intention based on the vehicle operation status data, and then combine the calculation and analysis results of the vehicle posture with the driving intention to determine whether the vehicle has a rollover risk, and issue an alarm when there is a rollover risk.

[0038] This embodiment can realize the life prediction of key positions of the vehicle and real-time monitoring of the whole vehicle posture. The system has the advantages of high accuracy, high reliability, and easy operation, and can provide comprehensive vehicle maintenance and management support for car owners, reduce maintenance costs, and improve vehicle safety and reliability.

[0039] The heavy engineering vehicle life prediction and vehicle posture detection method provided in the embodiment of the present application can be applied to electronic devices. Those skilled in the art will appreciate that the electronic device structure involved in the embodiment of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown, or combine certain components, or arrange different components. In an embodiment of the present invention, the electronic device includes but is not limited to a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0040] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, buttons, a camera, a display, and a SIM card interface, etc.

[0041] It is to be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the electronic device. In other embodiments of the present application, the electronic device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0042] The processor may include one or more processing units, for example, the processor may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated into one or more processors.

[0043] The processor can be the nerve center and command center of the electronic device. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0044] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. The memory may store instructions or data that the processor has just used or is cyclically used. If the processor needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor, and thus improves system efficiency.

[0045] The above-mentioned electronic equipment realizes the heavy-duty engineering vehicle life prediction and vehicle posture detection method of the present application, which simulates and fatigue tests the whole vehicle to determine the stress position of the vehicle, and arranges sensor elements at the stress position; constructs a life loss model and a whole vehicle posture model; collects vehicle operation status data, impact data of key positions in real-time operation of the vehicle, and vehicle load data; analyzes the stress and strain conditions of key positions based on the impact data, and matches them with the life loss model to determine the loss conditions of each key position; calculates and analyzes the whole vehicle posture based on the load data and vehicle operation status data, and matches them with the whole vehicle posture model to determine whether the vehicle has a rollover risk. The technical solution achieves accurate prediction of the life of heavy-duty engineering vehicles and real-time detection of the whole vehicle posture, improves the accuracy and efficiency of maintenance, reduces maintenance costs, and effectively reduces the safety risks of vehicle operation.

[0046] The storage medium provided in the present application stores a program product that can implement a method for predicting the life of a heavy-duty engineering vehicle and detecting the posture of the entire vehicle.

[0047] The life prediction and whole vehicle posture detection method of heavy engineering vehicles includes: simulating and fatigue testing the whole vehicle, determining the stress position of the vehicle according to the simulation and fatigue test data, and arranging sensor elements at the stress position; constructing a life loss model according to the simulation and fatigue test data, and constructing a whole vehicle posture model according to the vehicle load data and the vehicle operation status data; collecting the vehicle operation status data, and collecting the impact data and vehicle load data of the key positions in the real-time operation of the vehicle through the sensor elements; analyzing the stress and strain conditions of the key positions according to the impact data, matching the stress and strain condition analysis results of the key positions of the vehicle with the life loss model, determining the loss conditions of each key position according to the matching results, and giving an alarm when the loss of a certain key position is lower than a threshold; calculating and analyzing the whole vehicle posture according to the load data and the vehicle operation status data, matching the calculation and analysis results of the whole vehicle posture with the whole vehicle posture model, determining whether the vehicle has a rollover risk according to the matching results, and giving an alarm when there is a rollover risk.

[0048] In some possible implementations, the heavy-duty engineering vehicle life prediction and vehicle posture detection method disclosed herein can be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps described in the above "Exemplary Method" section of this specification according to various exemplary implementations of the present disclosure.

[0049] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0050] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the life of a heavy engineering vehicle and detecting the posture of the entire vehicle, characterized in that: The steps include: S1. Conduct simulation and fatigue tests on the vehicle, determine the stress position of the vehicle based on the simulation and fatigue test data, and arrange sensor elements at the stress position; S2. Construct a life loss model based on simulation and fatigue test data, and construct a vehicle posture model based on vehicle load data and vehicle operation status data; S3. Collecting vehicle operation status data, and collecting impact data and vehicle load data at key positions in real-time vehicle operation through sensor elements; S4. Analyze the stress and strain conditions at key positions based on the impact data, match the stress and strain analysis results at key positions of the vehicle with the life loss model, determine the loss conditions at each key position based on the matching results, and issue an alarm when the loss at a certain key position is lower than a threshold; S5. Calculate and analyze the vehicle posture based on the load data, identify the driving intention based on the vehicle operation status data, and then combine the calculation and analysis results of the vehicle posture with the driving intention to determine whether the vehicle has a rollover risk, and issue an alarm when there is a rollover risk.

2. The method for predicting the life of a heavy engineering vehicle and detecting the posture of the whole vehicle according to claim 1 is characterized in that: The specific steps of step S1 are as follows: S11. Conduct simulation and fatigue tests on the main load-bearing parts and connectors of the vehicle, and record the simulation and fatigue test data; S12. Analyze the simulation and fatigue test data to determine the stress positions of the main load-bearing parts and connectors; S13. The stress-bearing positions of the main load-bearing parts and connecting parts are used as monitoring points, and sensor elements are arranged at the monitoring points.

3. The method for predicting the life of a heavy engineering vehicle and detecting the posture of the whole vehicle according to claim 2 is characterized in that: The main bearing parts include a frame, a subframe, a suspension, a cab suspension and a V-shaped thrust rod; The connecting members include riding bolts, suspension bolts and frame longitudinal beam cross beam bolts; The sensing element includes a pressure sensor, a gyroscope and a strain gauge.

4. The method for predicting the life of a heavy engineering vehicle and detecting the posture of the whole vehicle according to claim 3 is characterized in that: The specific steps of step S2 are as follows: S21. Determine the strain threshold that causes damage to each monitoring point based on simulation and fatigue test data ; S22. For monitoring points exceeding the strain threshold Strain The current cyclic stress life can be calculated using the following local strain method formula : in, Indicates strain, represents the current cyclic stress life, represents the elastic modulus, represents the fatigue strength index, represents the fatigue strength coefficient, represents the fatigue continuation coefficient, Indicates the fatigue continuation coefficient corresponding to the fatigue life curve 2N=1 ordinate distance; S23. Based on the current cyclic stress life The equivalent damage of the monitoring point is calculated by the following equivalent damage formula And the cumulative equivalent damage D: ； ； in, The number of impacts under different loads, m is the strain threshold value exceeded by the monitoring point The number of times; S24. Based on equivalent damage The monitoring point life results are calculated by the following life calculation formula : ； S25. The local strain method formula, equivalent damage formula, and life calculation formula are used as life loss models; S26. Construct a vehicle center of mass calculation formula based on the vehicle load data, and construct a driver driving intention recognition model based on the vehicle operating status data.

5. The method for predicting the life of a heavy engineering vehicle and detecting the posture of the whole vehicle according to claim 4 is characterized in that: The specific steps of step S26 are as follows: S261. Obtain chassis mass m1, chassis mass center lateral distance x1, chassis mass center longitudinal distance h1, cargo box mass m2, cargo box mass center lateral distance x2, cargo box mass center longitudinal distance h2, cargo mass m3, cargo mass center lateral distance x3, cargo mass center longitudinal distance h3, subframe and accessory mass m4, subframe and accessory mass center lateral distance x4, subframe and accessory mass center longitudinal distance h4, and vehicle total mass M from vehicle load data; S262. The calculation formula for the center of mass of the vehicle is as follows: Among them, X is the lateral distance of the vehicle's center of mass, and H is the longitudinal distance of the vehicle's center of mass; S263. Construct a driver's driving intention recognition model based on the real-time vehicle speed, acceleration, and accelerator pedal depth in the vehicle operation status data.

6. The method for predicting the life of a heavy engineering vehicle and detecting the posture of the whole vehicle according to claim 5 is characterized in that: The specific steps of step S4 are as follows: S41. Obtain the strain of each monitoring point in the collected impact data , and determine whether the strain threshold is exceeded ; If yes, go to step S42; S42. Calculate the current cyclic stress life by the local strain method formula , combined with the number of impacts under different loads Calculate equivalent damage using the equivalent damage formula And the accumulated equivalent damage D, and finally the life calculation formula is used to calculate the life result of the monitoring point ; S43. Determine the monitoring point life results of the monitoring point Is it below the threshold? If so, an alarm is issued for the monitoring point below the threshold; If not, proceed to step S44; S44. Determine whether the monitoring point is a connector; If yes, go to step S45; If not, proceed to step S5; S45. Determine whether the strain value of the monitoring point is 0; If so, it is determined that the connection is loose, the preload force disappears, and an alarm is issued; If not, go to step S5.

7. The method for predicting the life of a heavy engineering vehicle and detecting the posture of the whole vehicle according to claim 6 is characterized in that: The specific steps of step S5 are as follows: S51. Collect chassis mass m1, chassis mass center lateral distance x1, chassis mass center longitudinal distance h1, cargo box mass m2, cargo box mass center lateral distance x2, cargo box mass center longitudinal distance h2, cargo mass m3, cargo mass center lateral distance x3, cargo mass center longitudinal distance h3, subframe and accessory mass m4, subframe and accessory mass center lateral distance x4, subframe and accessory mass center longitudinal distance h4 and vehicle total mass M, and use vehicle mass center calculation formula to calculate X as vehicle mass center lateral distance, H as vehicle mass center longitudinal distance, and obtain real-time mass center position; S52. Collect real-time vehicle speed, acceleration, and accelerator pedal depth data from the vehicle operation status data, and use the driver's driving intention recognition model to predict driving intention; S53. Determine the change trend of the center of mass based on the predicted driving intention, and then determine whether the vehicle has a rollover risk in combination with the real-time center of mass position; If so, issue a warning for the rollover risk and end; If not, return to step S3.

8. A heavy engineering vehicle life prediction and vehicle posture detection system, characterized in that: include: The force position determination module is used to perform simulation and fatigue test on the whole vehicle, determine the force position of the vehicle according to the simulation and fatigue test data, and arrange sensor elements at the force position; Model building module, used to build a life loss model based on simulation and fatigue test data, and build a vehicle posture model based on vehicle load data and vehicle operating status data; A data acquisition module is used to collect vehicle operation status data, and to collect impact data and vehicle load data at key positions in real-time vehicle operation through sensor elements; The life analysis module is used to analyze the stress and strain conditions at key positions based on the impact data, match the stress and strain analysis results at key positions of the vehicle with the life loss model, determine the loss conditions at each key position based on the matching results, and issue an alarm when the loss at a certain key position is lower than the threshold; The posture analysis module is used to calculate and analyze the posture of the entire vehicle based on the load data, identify the driving intention based on the vehicle operation status data, and then combine the calculation and analysis results of the vehicle posture with the driving intention to determine whether the vehicle has a rollover risk, and issue an alarm when there is a rollover risk.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for predicting the life of a heavy engineering vehicle and detecting the posture of the whole vehicle as claimed in any one of claims 1 to 7 when executing the program.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the heavy engineering vehicle life prediction and vehicle posture detection method as described in any one of claims 1 to 7 are implemented.

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