Method, device, monitoring apparatus, medium and vehicle for monitoring a state of a vehicle component

Abstract

The application relates to the technical field of vehicle components, and provides a vehicle component state monitoring method and device, a monitoring equipment, a medium and a vehicle. The method comprises the following steps: acquiring acceleration data collected by an acceleration sensor in a vehicle; determining whether at least one component in the vehicle has a fault risk based on a preset component stress and acceleration data representing acceleration corresponding relationship; when it is determined that at least one component in the vehicle has a fault risk, displaying a virtual graph corresponding to the component with the fault risk, and showing prompt information for detecting the component with the fault risk. The embodiment of the application can detect the component with the fault risk in real time, and through the display of the corresponding virtual graph and the exhibition of the prompt information, the user can intuitively perceive the possible fault risk of the component in the current vehicle, and the user experience is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle component technology, and more specifically, to a method, apparatus, monitoring equipment, medium, and vehicle for monitoring the condition of vehicle components. Background Technology

[0002] Vehicles are powered and primarily used for transporting people and / or goods, and for towing vehicles carrying people and / or goods. With economic development, vehicles play a vital role in people's lives; however, accidents caused by vehicle malfunctions occur frequently, seriously threatening people's lives and health. Therefore, it is crucial to be aware of potential vehicle problems in advance and to understand the failure risks of each component during vehicle use, in order to effectively reduce the probability of accidents caused by inherent vehicle issues.

[0003] In the existing technology, if users want to understand the problems of their vehicles, they need to send the vehicles to the repair department for inspection. However, this method cannot allow users to understand the potential failure risks of the current vehicle parts in a real time and intuitively, and the inspection cost is high. Summary of the Invention

[0004] This application addresses the shortcomings of existing methods by proposing a method, device, monitoring equipment, medium, and vehicle for monitoring the status of vehicle components, thereby solving the technical problem in the prior art that users cannot understand the current failure risk of vehicle components in real time and intuitively.

[0005] In the first aspect, embodiments of this application provide a method for monitoring the state of vehicle components, including: acquiring acceleration data collected by an acceleration sensor in the vehicle; determining, based on a preset correspondence between component stress and the acceleration represented by the acceleration data, whether at least one component in the vehicle has a failure risk; when it is determined that at least one component in the vehicle has a failure risk, displaying a virtual graphic corresponding to the component with the failure risk, and displaying a prompt message indicating that the component with the failure risk needs to be detected.

[0006] Optionally, determining whether at least one component in the vehicle chassis has a failure risk based on the correspondence between preset component stress and the acceleration represented by the acceleration data includes: inputting the acceleration data into a monitoring model to obtain the stress information of the component output by the monitoring model; the monitoring model stores data representing the correspondence between preset acceleration and preset component stress; if the stress value at at least one location of at least one component in the stress information exceeds the stress limit value, then it is determined that the component has a failure risk.

[0007] Optionally, the steps for establishing the monitoring model include: acquiring first correlation data between load and component stress calculated through simulation analysis based on first preset load data; acquiring second correlation data between acceleration and component stress obtained through bench testing based on second preset load data; establishing third correlation data between acceleration, load, and component stress based on the first and second correlation data; and establishing a monitoring model based on the third correlation data.

[0008] Optionally, the first correlation data includes correlation data between the load magnitude and the stress limit value of the component stress, and correlation data between the load direction and the distribution location of the component stress; the second correlation data includes correlation data between the acceleration magnitude and the strain magnitude of the component stress, and correlation data between the acceleration direction and the distribution location of the component stress; the third correlation data includes correlation data between the acceleration magnitude, the load magnitude and the strain magnitude of the component stress, and correlation data between the acceleration direction, the load direction and the distribution location of the component stress.

[0009] Optionally, after establishing the monitoring model based on the third correlation data, the method further includes: during a road condition test on an actual road surface, acquiring acceleration data collected by an acceleration sensor in a test vehicle and corresponding stress data collected by a strain gauge; the strain gauge is installed at the component; determining fourth correlation data between acceleration and component stress based on the acceleration data from the acceleration sensor and the corresponding stress data from the strain gauge; and correcting the monitoring model based on the fourth correlation data.

[0010] Optionally, the step of correcting the monitoring model based on the fourth correlation data includes: comparing the fourth correlation data with the third correlation data to determine whether the component stress is consistent under the same acceleration; if they are inconsistent, obtaining the fifth correlation data calibrated on the test bench, and correcting the third correlation data based on the fifth correlation data so that the component stress is consistent under the same acceleration when the fourth correlation data is compared with the third correlation data.

[0011] Optionally, acquiring the fifth correlation data calibrated on the bench includes: acquiring the fifth correlation data between the load and the component stress determined based on the first test data and the second test data during bench calibration; the first test data includes test data between the load magnitude and the component stress in the Z direction obtained by stretching and / or compressing the first test component with strain gauges symmetrically arranged on both sides of the first test component; the second test data includes test data between the load magnitude and the component stress in the X and Y directions obtained by bending the second test component along the X and Y directions with strain gauges arranged on the second test component and using a tensile testing machine.

[0012] Optionally, the components include chassis components, the first test component includes a shock absorber rod, and the second test component includes a ball joint rod; the chassis components include wheel hubs, steering knuckles, rods, castings, and sheet metal parts.

[0013] Secondly, embodiments of this application also provide a vehicle component status monitoring device, comprising: an acquisition module for acquiring acceleration data collected by an acceleration sensor in the vehicle; a determination module for determining, based on a preset correspondence between component stress and the acceleration represented by the acceleration data, whether at least one component in the vehicle has a failure risk; and a display module for displaying, when it is determined that at least one component in the vehicle has a failure risk, a virtual graphic corresponding to the component with failure risk, and displaying a prompt message indicating that the component with failure risk needs to be detected.

[0014] Thirdly, embodiments of this application also provide a vehicle component status monitoring device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, it implements the vehicle component status monitoring method described in the first aspect.

[0015] Fourthly, embodiments of this application also provide a vehicle, including: the monitoring device described in the third aspect, various components constituting the vehicle, and an acceleration sensor; the monitoring device is communicatively connected to the acceleration sensor.

[0016] Optionally, the acceleration sensor includes a three-dimensional acceleration sensor mounted on the wheel hub of the vehicle chassis; the component includes a chassis component; the chassis component includes a wheel hub, a steering knuckle, and a linkage.

[0017] Fifthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a monitored device, implements the method for monitoring the state of vehicle components as described in the first aspect.

[0018] The beneficial technical effects of this application are:

[0019] This application acquires acceleration data collected by acceleration sensors in a vehicle. Based on a preset correspondence between component stress and the acceleration represented by the acceleration data, it can determine whether at least one component in the vehicle is at risk of failure. When at least one component is identified as having a failure risk, a virtual graphic corresponding to the component with the failure risk is displayed, along with a prompt message indicating that the component with the failure risk needs to be detected. This application can detect components with current failure risks in real time, and by displaying the corresponding virtual graphic and prompt message, it helps users intuitively perceive the potential failure risks of components in the current vehicle, improving the user experience.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0022] Figure 1 This is a flowchart of a method for monitoring the state of vehicle components according to an embodiment of this application;

[0023] Figure 2 This is a flowchart illustrating the steps in the method of this application embodiment that determine whether at least one component in the vehicle has a failure risk based on the correspondence between preset component stress and acceleration data characterized by acceleration.

[0024] Figure 3 This is a flowchart of a method for establishing a monitoring model in an embodiment of this application;

[0025] Figure 4 This is another flowchart illustrating the method for establishing the monitoring model in this application embodiment;

[0026] Figure 5 This is a flowchart of the steps included in the step of correcting the monitoring model based on the fourth correlation data in the embodiments of this application;

[0027] Figure 6 This is a schematic diagram of a vehicle component status monitoring device implemented in this application;

[0028] Figure 7 This is a schematic diagram of a vehicle component status monitoring device in an embodiment of this application. Detailed Implementation

[0029] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present application.

[0030] like Figure 1 As shown, this application provides a method for monitoring the status of vehicle components. The vehicle includes any power-driven vehicle, such as new energy vehicles, electric vehicles, and non-rail-borne vehicles. The method of this application includes:

[0031] S101: Acquire acceleration data collected by the acceleration sensor in the vehicle.

[0032] Optionally, an accelerometer is a sensor capable of measuring acceleration, typically composed of a mass block, a damper, an elastic element, a sensing element, and an adaptation circuit. In this embodiment, the accelerometer senses acceleration and converts it into a usable output signal (acceleration data). Acceleration data includes data characterizing the magnitude and / or direction of acceleration. In this embodiment, trigger conditions for acquiring acceleration data can be set, such as setting a timed task to periodically acquire acceleration data; for example, if vehicle start-up, emergency braking, or collision is detected, the acquisition of acceleration data is triggered synchronously. The setting scheme for the trigger conditions can be adjusted according to the actual application situation, and this application does not limit it.

[0033] S102: Based on the correspondence between preset component stress and acceleration data representing acceleration, determine whether at least one component in the vehicle has a risk of failure.

[0034] Optionally, pre-stored data on the correspondence between component stress and acceleration is used. When acceleration data is acquired, the acceleration represented by the acceleration data is compared with the pre-stored acceleration to determine the closest pre-stored acceleration. Then, based on the pre-stored correspondence between component stress and acceleration, the corresponding component stress is obtained as the basis for determining whether at least one component in the vehicle has a failure risk. The preset component stress includes the magnitude and / or distribution location of the preset component stress, and further includes the stress limit value. The stress limit value is related to the component grade; different grades correspond to different stress limit values.

[0035] Optionally, assuming that when a vehicle is involved in a collision, the acceleration data measured by the acceleration sensor is obtained. Based on the acceleration data and the data representing the relationship between the preset acceleration and the preset component stress, it can be known that when the peak acceleration is X, the stress value of a certain part of a certain component exceeds the stress limit value. At this time, it is determined that at least one component in the vehicle has a risk of failure.

[0036] S103: When it is determined that at least one component in the vehicle has a failure risk, the virtual graphic corresponding to the component with failure risk is displayed, and a prompt message is shown indicating that the component with failure risk needs to be detected.

[0037] Optionally, when it is determined that at least one component in the vehicle is at risk of failure, a virtual graphic corresponding to that component will be displayed on the interface, along with a prompt message indicating that the component at risk of failure needs to be detected. The virtual graphic can be pre-stored data or real-time captured data, showing the component at risk of failure and / or a specific part of that component where a failure is possible. The virtual graphic can be displayed as an image or video, and the location of the component at risk of failure can be highlighted to draw the user's attention during the display. The prompt message indicating that the component at risk of failure needs to be detected includes displaying the prompt message through images and / or issuing a voice prompt.

[0038] This application acquires acceleration data from acceleration sensors in a vehicle. Based on a pre-defined correspondence between component stress and acceleration data representing acceleration, it can determine whether at least one component in the vehicle is at risk of failure. When at least one component is identified as having a failure risk, a virtual graphic corresponding to the component with the failure risk is displayed, along with a prompt indicating that the component needs to be detected. This application can detect components with current failure risks in real time, and by displaying the corresponding virtual graphic and prompting information, it helps users intuitively perceive the potential failure risks of components in the current vehicle, improving the user experience.

[0039] In one embodiment, such as Figure 2 As shown, step S102, based on the pre-defined correspondence between component stress and acceleration data representing acceleration, determines whether at least one component in the vehicle has a failure risk, including:

[0040] S201: Input acceleration data into the monitoring model to obtain stress information of the components output by the monitoring model; the monitoring model stores data representing the correspondence between preset acceleration and preset component stress.

[0041] Optionally, this application embodiment pre-establishes a monitoring model, which stores data characterizing the correspondence between preset acceleration and preset component stress. The correspondence between preset acceleration and preset component stress includes the correspondence between acceleration magnitude, acceleration direction, component stress value, and stress limit value. Optionally, the stress information of the component output by the monitoring model includes the component stress value, whether the stress value exceeds the stress limit value, and the distribution location of the component stress (mainly the location where the stress distribution is most concentrated). Optionally, considering reducing the computational complexity of the equipment, the monitoring model only outputs information about stress exceeding the stress limit value at a certain location in a certain component when it is determined that the stress value exceeds the stress limit value.

[0042] S202: If the stress value at at least one location of at least one component in the stress information exceeds the stress limit value, then the component is determined to be at risk of failure.

[0043] Optionally, the judgment process in step S202 can be completed by a monitoring model, and the stress information of the component output by the monitoring model includes information on whether the current stress value exceeds the stress limit value; the judgment process in step S202 can also be completed by a computer program other than the monitoring model, which, after outputting the stress information of the component, compares the stress value in the stress information of the component with the pre-stored stress limit value to determine whether the component exceeds the stress limit value. When the stress value at at least one location of at least one component exceeds the stress limit value, it is determined that the component has a risk of failure.

[0044] Optionally, after determining in step S202 that a component has a failure risk, step S103 can obtain a corresponding virtual graphic based on the stress information of the component output by the monitoring model. For example, based on the component name indicated by the stress information and the location of the component with failure risk, the corresponding target graphic can be obtained from the pre-stored graphic data, and then the location of failure risk can be marked on the target graphic to generate a virtual graphic. The pre-stored graphic data includes the three-dimensional data of each component that makes up the vehicle.

[0045] In one embodiment, such as Figure 3 As shown, the process of establishing the monitoring model includes:

[0046] S301: Obtain the first correlation data between the load and the component stress, which is calculated by simulation analysis based on the first preset load data.

[0047] Optionally, simulation analysis can be performed using ABAQUS software. Taking a chassis component as an example, the chassis model (such as a subframe mesh model), the nonlinear parameters of the chassis component, and the first preset load data can be input into the software. The explcit module of the display dynamics analysis can then be called to calculate the magnitude (stress value) of the stress and strain of the chassis component under different loads, as well as the location of stress concentration distribution in the component. The first preset load data includes load data for different working conditions, such as load values ​​in the Z direction, load values ​​in the X direction, load values ​​in the Y direction, load values ​​in both Z and X directions, load values ​​in both Z and Y directions, load values ​​in both X and Y directions, and load values ​​in Z, X, and Y directions, etc., where the Z direction is perpendicular to the ground, the X direction is longitudinal along the front and rear of the vehicle, and the Y direction is lateral along the left and right wheels. The first correlation data between load and component stress characterizes the magnitude (stress value) of the component stress and strain and the location of stress concentration distribution corresponding to the load data for different working conditions.

[0048] S302: Obtain the second correlation data between acceleration and component stress obtained from bench tests based on the second preset load data.

[0049] Optionally, the bench test is a process of simulating vehicle operation to test the vehicle. For example, the object tested on the bench is the chassis suspension system. After installing acceleration sensors at the wheel hubs and attaching strain gauges to key parts of various chassis components, the chassis suspension system is mounted on the MTS bench, and then a second preset load data is applied to the wheel center. The second preset load data includes loads of different directions and magnitudes, corresponding to various bench test schemes, such as vertical wheel bounce. Through the bench test of vertical wheel bounce, acceleration curves and actual strain data of the components can be obtained. In step S302, through the bench test, second correlation data between acceleration and component stress can be obtained.

[0050] Optionally, in the embodiments of this application, there is no need to limit the order of execution of step S301 and step S302. The two steps can be executed simultaneously, or step S301 can be executed first and then step S302 can be executed, or step S302 can be executed first and then step S301 can be executed.

[0051] S303: Based on the first and second correlation data, establish the third correlation data among acceleration, load, and component stress.

[0052] Optionally, the first correlation data represents the correspondence between load and component stress, and the second correlation data represents the second correlation between acceleration and component stress. The load magnitude in the first correlation data is correlated with the acceleration magnitude in the second correlation data, and the acceleration direction in the second correlation data is correlated with the load direction in the first correlation data to generate third correlation data. Establishing the third correlation data among acceleration, load, and component stress can be understood as correcting the first correlation data based on the second correlation data. The second correlation data is obtained from bench tests, while the first correlation data is obtained from simulation analysis. Comparatively, the second correlation data is more relevant to real-world application scenarios. It can be used as a basis to correct potential errors in the simulation analysis of the first correlation data, improving the accuracy of the monitoring model. This correction can be understood as correlating the load magnitude with the acceleration magnitude based on the same strain magnitude for both, and correlating the load direction with the acceleration direction based on the same stress distribution location for both.

[0053] S304: Establish a monitoring model based on third-party correlation data.

[0054] Optionally, the monitoring model is constructed based on the third associated data. For example, after acquiring the input acceleration data, the real-time acquired acceleration data is compared with the acceleration data in the third associated data (at this time, the third associated data can be regarded as preset data). The target acceleration data corresponding to the real-time acceleration data is obtained from the third associated data, and then the component stress data corresponding to the target acceleration data is obtained to generate the component stress information output by the monitoring model. Optionally, the corresponding component stress data can be directly output as the component stress information, or the component stress data can be processed to generate the component stress information output (e.g., based on the component stress data, it is determined whether the stress value exceeds the stress limit value; if so, the stress value combined with the stress distribution location is output as stress information; otherwise, a null value is output).

[0055] Alternatively, the monitoring model can also be trained using third-party related data as sample data.

[0056] In one embodiment, the first correlation data includes correlation data between the load magnitude and the stress limit value of the component stress, and correlation data between the load direction and the distribution location of the component stress; the second correlation data includes correlation data between the acceleration magnitude and the strain magnitude of the component stress, and correlation data between the acceleration direction and the distribution location of the component stress; the third correlation data includes correlation data between the acceleration magnitude, the load magnitude and the strain magnitude of the component stress, and correlation data between the acceleration direction, the load direction and the distribution location of the component stress.

[0057] Optionally, regarding the correlation data between the load magnitude and the stress limit value of the component, which includes the first correlation data, for example: Assume the component is a chassis steering knuckle, and the current tensile limit value of the chassis steering knuckle is 600 MPa. The tensile limit value is one type of stress limit value, and it is related to the chassis steering knuckle grade; different grades correspond to different tensile limit values. During the simulation, the first preset load data is applied to the chassis steering knuckle. For example, if the applied load is x Newtons, simulation analysis shows that the load of x Newtons causes the maximum stress in the chassis steering knuckle to be y MPa. Then, y MPa is compared with the tensile limit value. If y is greater than or equal to 600, it is determined that the chassis steering knuckle has a risk of failure. Through simulation analysis, the stress values ​​corresponding to various vehicle components under different load magnitudes, and the correlation data between whether these stress values ​​exceed the stress limit value, can be obtained. For different components or different locations of the same component, the stress limit value may be the tensile limit value, the compressive limit value, etc.

[0058] Optionally, for the correlation data between the load direction and the distribution location of component stress included in the first correlation data, for example: assuming the component is a chassis steering knuckle, in actual vehicle use, the chassis steering knuckle is connected to the wheel. When the wheel is driving on the road, the situation shown in Table 1 below may occur. The simulation analysis is explained in conjunction with Table 1.

[0059] Table 1

[0060]

[0061] Using the actual situation shown in Table 1 above as the principle of simulation analysis, load data of different working conditions are applied to the steering knuckle. Simulation analysis can determine the distribution of stress in the chassis steering knuckle when it is subjected to different load directions.

[0062] Optionally, although the simulation analysis process of loading load magnitude and load direction on the chassis steering knuckle is described separately above, the simulation process includes loading the load magnitude and load direction included in the first preset load data onto the chassis steering knuckle for simulation analysis. That is, the correlation data between load magnitude and stress limit value of component stress, and the correlation data between load direction and distribution location of component stress are data that can be obtained simultaneously in one simulation.

[0063] Optionally, the second correlation data includes correlation data between the magnitude of acceleration and the magnitude of stress and strain in the component, and correlation data between the direction of acceleration and the distribution location of stress in the component. Considering that the second correlation data is obtained from bench tests, and that in actual production, due to the limited testing methods available in bench tests, the monitoring model established solely using the second correlation data is very coarse, and the accuracy of the stress information output by the generated monitoring model may be very low, this embodiment uses the second correlation data as correction data to correct the first correlation data and generate third correlation data (that is, establishing a correlation between the first and second correlation data). This overcomes the limitations of bench tests and improves the accuracy of the monitoring model generated based on the third correlation data.

[0064] In one embodiment, a monitoring model can be directly established using the first correlation data (using load magnitude to represent acceleration magnitude and load direction to represent acceleration direction), and then the established monitoring model can be corrected using the second correlation data.

[0065] In one embodiment, such as Figure 4 As shown, the process of establishing the monitoring model, in addition to steps S301 to S304 mentioned above, also includes the following steps:

[0066] S305: During road condition testing on actual road surfaces, acquire acceleration data collected by the acceleration sensor in the test vehicle and corresponding stress data collected by the strain gauge; the strain gauge is installed at the component.

[0067] Optionally, considering that the relationship between acceleration and component stress during actual vehicle driving on a real road is not a simple linear relationship as in the laboratory and may have deviations, the third correlation data is also corrected based on the data obtained from road condition tests in this embodiment of the application, that is, the existing monitoring model is corrected to improve the accuracy of the monitoring model.

[0068] For example, before road testing of chassis components, acceleration sensors are installed on the wheel hubs of the test vehicle, and strain gauges are installed on the struts. Then, the test vehicle is driven on a real road surface (the real road surface here refers to the road surface in the test track, which includes reinforced durability road surface and abuse condition road surface) to obtain the acceleration data collected by the acceleration sensors and the corresponding stress data collected by the strain gauges during the road test.

[0069] Alternatively, the strain gauge is an element used to measure strain, consisting of a sensitive grid or the like.

[0070] S306: Based on the acceleration data from the accelerometer and the corresponding stress data from the strain gauge, determine the fourth correlation data between acceleration and component stress.

[0071] Optionally, acceleration data and stress data collected at the same time can be correlated based on time to generate a fourth correlation data between acceleration and component stress. Alternatively, acceleration data and stress data collected during a specific abuse condition can be correlated to generate a fourth correlation data.

[0072] S307: Correct the monitoring model based on the fourth correlation data.

[0073] Optionally, correcting the monitoring model based on the fourth correlation data can be understood as correcting the third correlation data based on the fourth correlation data, determining whether the component stress is consistent under the same acceleration in the two data sets, and identifying any possible deviation data in the third correlation data.

[0074] In one embodiment, such as Figure 5 As shown, step S307 corrects the monitoring model based on the fourth correlation data, including:

[0075] S501: Compare the fourth correlation data with the third correlation data to determine whether the stress of the components is consistent under the same acceleration.

[0076] Optionally, the same acceleration includes both the magnitude and direction of the acceleration; whether the stress in the components is consistent includes both the magnitude of the strain and the location of the stress distribution. If, under the same acceleration, only the magnitude or location of the strain in the component stress is consistent, it cannot be determined that they are consistent. Only when the magnitude and direction of the acceleration, the magnitude of the strain in the component stress, and the location of the stress distribution are all consistent can they be determined to be consistent.

[0077] S502: If there is a discrepancy, the fifth correlation data calibrated by the test bench is obtained, and the third correlation data is corrected based on the fifth correlation data so that the stress of the component is consistent under the same acceleration when the fourth correlation data is compared with the third correlation data.

[0078] Optionally, considering that the fourth correlation data generally shows good consistency with the third correlation data and the possibility of data deviation is small, but in certain situations, such as low-frequency road surfaces or vehicle body tortuous road surfaces, the accuracy of the acceleration data collected by the acceleration sensor is lower. This results in the stress of the component in the fourth correlation data being higher than the stress of the component in the third correlation data (larger strain magnitude and more concentrated stress distribution). In this case, the fifth correlation data, which has been calibrated on the test bench, is used to correct the third correlation data, so that when the fourth correlation data is compared with the third correlation data, the component stress is consistent under the same acceleration. Here, test bench calibration involves inferring the component load data from the component stress data.

[0079] In one embodiment, step S502, obtaining the fifth association data calibrated by the test bench, includes:

[0080] Obtain the fifth correlation data between the load and component stress determined based on the first and second test data during bench calibration.

[0081] The first test data includes test data on the relationship between the load magnitude and the component stress in the Z direction obtained by stretching and / or compressing the first test component with strain gauges symmetrically arranged on both sides of the first test component and using a tensile testing machine; the second test data includes test data on the relationship between the load magnitude and the component stress in the X and Y directions obtained by bending the second test component along the X and Y directions with strain gauges arranged on the second test component and using a tensile testing machine.

[0082] Optionally, to illustrate the bench calibration process, for the rods in the chassis components, consider the following: First, symmetrically attach strain gauges to the shock absorber rods near the shock absorber tower. Since the shock absorber itself is subjected to vertical force, its force mode is tension or compression, which can establish a tension and compression relationship with the vehicle in the Z-direction. Performing tension and / or compression treatment on a tensile testing machine, the correspondence between load magnitude and strain magnitude in the Z-direction can be obtained. Second, attach strain gauges to the ball joint short rod connecting the control arm and the steering knuckle. The ball joint short rod is subjected to bending along the entire vehicle in the X and Y directions, which can establish a bending relationship with the vehicle in the X and Y directions. Bending along the rod in the X and Y directions on a tensile testing machine, the correspondence between load magnitude and strain magnitude in the X and Y directions can be obtained. Then, based on the correspondence between load magnitude and strain magnitude in the three directions, the functional relationship A = F(x) is obtained, where A represents strain and x represents load. After establishing the functional relationship, the shock absorber rod and the ball joint rod were installed on the test vehicle and tested at the test site. The stress data A collected by the strain gauges was used to inversely deduce the corresponding load x through the functional relationship.

[0083] Optionally, in step S502, correcting the third correlation data based on the fifth correlation data includes: correcting the third correlation data based on the fifth correlation data under the same stress data. Since inconsistencies between the third and fourth correlation data are rare, when such inconsistencies occur and the third correlation data is corrected using the fifth correlation data, multiple repeated corrections are required to further improve the accuracy of the monitoring model.

[0084] In one embodiment, the components include chassis components, a first test component includes a shock absorber rod, and a second test component includes a ball joint rod; the chassis components include wheel hubs, steering knuckles, rods, castings, and sheet metal parts.

[0085] Based on the same inventive concept, such as Figure 6 As shown in the figure, this application embodiment also provides a vehicle component status monitoring device 600, including: an acquisition module 601, a determination module 602, and a display module 603.

[0086] The acquisition module 601 is used to acquire acceleration data collected by the acceleration sensor in the vehicle.

[0087] The determination module 602 is used to determine whether there is at least one component in the vehicle that has a risk of failure, based on the correspondence between the preset component stress and acceleration data characterized by acceleration.

[0088] The display module 603 is used to display a virtual graphic corresponding to the component with the risk of failure when it is determined that at least one component in the vehicle has a risk of failure, and to display a prompt message indicating that the component with the risk of failure needs to be detected.

[0089] Optionally, the determining module 602 is further configured to: input acceleration data into the monitoring model, obtain stress information of the component output by the monitoring model; the monitoring model stores data characterizing the correspondence between preset acceleration and preset component stress; if the stress value of at least one position of at least one component in the stress information exceeds the stress limit value, then the component is determined to have a failure risk.

[0090] Optionally, the monitoring device 600 also includes:

[0091] The simulation data acquisition module is used to acquire the first correlation data between the load and the component stress, which is calculated by simulation analysis based on the first preset load data.

[0092] The test data acquisition module is used to acquire second correlation data between acceleration and component stress obtained from bench tests based on second preset load data.

[0093] The correlation module is used to establish a third correlation data between acceleration, load, and component stress based on the first and second correlation data.

[0094] Establish a module for building a monitoring model based on third-party related data.

[0095] Optionally, the first correlation data includes correlation data between the load magnitude and the stress limit value of the component stress, and correlation data between the load direction and the distribution location of the component stress;

[0096] The second set of correlation data includes correlation data between the magnitude of acceleration and the magnitude of strain in the component stress, as well as correlation data between the direction of acceleration and the distribution location of the component stress.

[0097] The third set of correlation data includes correlation data between acceleration magnitude, load magnitude, and strain magnitude of component stress, as well as correlation data between acceleration direction, load direction, and distribution location of component stress.

[0098] Optionally, the monitoring device 600 also includes:

[0099] The road condition data acquisition module is used to acquire acceleration data collected by the acceleration sensor in the test vehicle and corresponding stress data collected by the strain gauge during road condition tests on actual road surfaces; the strain gauge is installed at the component.

[0100] The road condition data determination module is used to determine the fourth correlation data between acceleration and component stress based on the acceleration data from the accelerometer and the corresponding stress data from the strain gauge.

[0101] The correction module is used to correct the monitoring model based on the fourth correlation data.

[0102] Optionally, the correction module is also used to compare the fourth correlation data with the third correlation data to determine whether the component stress is consistent under the same acceleration; if they are inconsistent, the fifth correlation data calibrated by the test bench is obtained, and the third correlation data is corrected based on the fifth correlation data so that the component stress is consistent under the same acceleration when the fourth correlation data is compared with the third correlation data.

[0103] Optionally, the correction module includes:

[0104] The calibration data acquisition unit is used to acquire the fifth correlation data between the load and component stress determined based on the first test data and the second test data during bench calibration. The first test data includes test data on the relationship between the load magnitude and component stress in the Z direction obtained by symmetrically arranging strain gauges on both sides of the first test component and performing tensile and / or compression treatment on the first test component using a tensile testing machine. The second test data includes test data on the relationship between the load magnitude and component stress in the X and Y directions obtained by arranging strain gauges on the second test component and performing bending treatment along the X and Y directions of the second test component using a tensile testing machine.

[0105] Optionally, the components include chassis components, the first test component includes a shock absorber rod, and the second test component includes a ball joint rod; the chassis components include wheel hubs, steering knuckles, rods, castings, and sheet metal parts.

[0106] The vehicle component status monitoring device of this application embodiment can execute the vehicle component status monitoring method provided in the embodiments of this application. The implementation principle is similar. The actions performed by each module in the vehicle component status monitoring device in each embodiment of this application correspond to the steps in the vehicle component status monitoring method in each embodiment of this application. For detailed functional descriptions of each module of the vehicle component status monitoring device, please refer to the descriptions of the corresponding vehicle component status monitoring methods shown above, which will not be repeated here.

[0107] Based on the same inventive concept, such as Figure 7 As shown in the embodiment of this application, a vehicle component status monitoring device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, it implements the above-mentioned vehicle component status monitoring method.

[0108] like Figure 7 As shown, Figure 7 The monitoring device 700 shown includes a processor 701 and a memory 703. The processor 701 and the memory 703 are connected, for example, via a bus 702. Optionally, the monitoring device 700 may also include a transceiver 704. It should be noted that in practical applications, the transceiver 704 is not limited to one type, and the structure of this monitoring device 700 does not constitute a limitation on the embodiments of this application.

[0109] Processor 701 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this application. Processor 701 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. Processor 701 executes the methods shown in the above embodiments by invoking computer operation instructions.

[0110] Bus 702 may include a pathway for transmitting information between the aforementioned components. Bus 702 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 702 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0111] The memory 703 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0112] The memory 703 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 701. The processor 701 is used to execute the application code stored in the memory 703 to implement the content shown in the foregoing method embodiments.

[0113] The monitoring equipment includes, but is not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and vehicle terminals (such as vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 7 The monitoring device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0114] Based on the same inventive concept, this application also provides a vehicle, including the aforementioned monitoring device, various components constituting the vehicle, and an acceleration sensor; the monitoring device and the acceleration sensor are communicatively connected.

[0115] In one embodiment, the acceleration sensor includes a triaxial acceleration sensor mounted on the wheel hub of the vehicle chassis; the components include chassis components; the chassis components include wheel hubs, steering knuckles, rods, castings, and sheet metal parts.

[0116] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium storing a computer program, which is used to implement the above-mentioned method for monitoring the state of vehicle components when executed by the monitored device.

[0117] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0118] It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0119] The aforementioned computer-readable medium may be included in the aforementioned monitoring device; or it may exist independently and not assembled into the monitoring device.

[0120] The aforementioned computer-readable medium carries one or more programs, which, when executed by the monitoring device, cause the monitoring device to perform the method shown in the above embodiments.

[0121] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0122] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0123] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0124] Those skilled in the art will understand that each block in these structural diagrams and / or block diagrams and / or flow diagrams, as well as combinations of blocks in these structural diagrams and / or block diagrams and / or flow diagrams, can be implemented using computer program instructions. Those skilled in the art will also understand that these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing method for implementation, thereby enabling the processor of the computer or other programmable data processing method to execute the schemes specified in the blocks or multiple blocks of the structural diagrams and / or block diagrams and / or flow diagrams disclosed in this application.

[0125] Those skilled in the art will understand that the steps, measures, and solutions in the various operations, methods, and processes discussed in this application can be alternated, modified, combined, or deleted. Furthermore, other steps, measures, and solutions in the various operations, methods, and processes discussed in this application can also be alternated, modified, rearranged, decomposed, combined, or deleted. Furthermore, steps, measures, and solutions in the prior art that are similar to those disclosed in this application can also be alternated, modified, rearranged, decomposed, combined, or deleted.

[0126] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for monitoring the condition of vehicle components, characterized in that, include: Acquire acceleration data collected by the acceleration sensors in the vehicle; Based on the correspondence between preset component stress and acceleration represented by the acceleration data, it is determined whether at least one component in the vehicle has a failure risk, including: inputting the acceleration data into a monitoring model, obtaining the component stress information output by the monitoring model; the monitoring model stores data representing the correspondence between preset acceleration and preset component stress; if the stress value at at least one location of at least one component in the stress information exceeds the stress limit value, then it is determined that the component has a failure risk. When it is determined that at least one component in the vehicle is at risk of failure, a virtual graphic corresponding to the component at risk of failure will be displayed, and a prompt message indicating that the component at risk of failure needs to be detected will be shown. The steps for establishing the monitoring model include: acquiring first correlation data between load and component stress calculated through simulation analysis based on first preset load data; acquiring second correlation data between acceleration and component stress obtained through bench testing based on second preset load data; establishing third correlation data between acceleration, load, and component stress based on the first and second correlation data; and establishing a monitoring model based on the third correlation data.

2. The method according to claim 1, characterized in that: The first correlation data includes correlation data between the load magnitude and the stress limit value of the component stress, and correlation data between the load direction and the distribution location of the component stress; The second correlation data includes correlation data between the magnitude of acceleration and the magnitude of strain of component stress, and correlation data between the direction of acceleration and the distribution location of component stress; The third set of correlation data includes correlation data between acceleration magnitude, load magnitude, and strain magnitude of component stress, as well as correlation data between acceleration direction, load direction, and distribution location of component stress.

3. The method according to claim 1, characterized in that, After establishing the monitoring model based on the third correlation data, the method further includes: During road condition testing on actual road surfaces, acceleration data collected by the acceleration sensor in the test vehicle and corresponding stress data collected by the strain gauge are obtained; the strain gauge is installed at the component. Based on the acceleration data from the accelerometer and the corresponding stress data from the strain gauge, a fourth correlation data between acceleration and component stress is determined. The monitoring model is revised based on the fourth set of related data.

4. The method according to claim 3, characterized in that, The step of correcting the monitoring model based on the fourth correlation data includes: The fourth correlation data is compared with the third correlation data to determine whether the stress of the components is consistent under the same acceleration. If there is a discrepancy, the fifth correlation data calibrated by the test bench is obtained, and the third correlation data is corrected based on the fifth correlation data so that when the fourth correlation data is compared with the third correlation data, the stress of the component is consistent under the same acceleration.

5. The method according to claim 4, characterized in that, The acquisition of the fifth associated data calibrated by the test bench includes: Obtain the fifth correlation data between the load and component stress determined based on the first and second test data during bench calibration; The first test data includes test data on the relationship between the load magnitude and the stress of the component in the Z direction obtained by symmetrically arranging strain gauges on both sides of the first test component and performing tensile and / or compressive treatment on the first test component using a tensile testing machine. The second test data includes test data on the relationship between the load magnitude and the stress of the component in the X and Y directions obtained by placing strain gauges on the second test component and bending it along the X and Y directions using a tensile testing machine.

6. The method according to claim 5, characterized in that, The components include chassis components, the first test component includes a shock absorber rod, and the second test component includes a ball joint rod; the chassis components include wheel hubs, steering knuckles, rods, castings, and sheet metal parts.

7. A device for monitoring the condition of vehicle components, characterized in that, include: The acquisition module is used to acquire acceleration data collected by the acceleration sensors in the vehicle; The determination module is used to determine whether at least one component in the vehicle has a failure risk based on the correspondence between preset component stress and acceleration represented by the acceleration data. This includes: inputting the acceleration data into a monitoring model to obtain stress information of the component output by the monitoring model; the monitoring model stores data representing the correspondence between preset acceleration and preset component stress; if the stress value at at least one location of at least one component in the stress information exceeds a stress limit value, then the component is determined to have a failure risk. The display module is used to display a virtual graphic corresponding to the component with the risk of failure when it is determined that at least one component in the vehicle has a risk of failure, and to display a prompt message indicating that the component with the risk of failure needs to be detected. The monitoring device also includes: The simulation data acquisition module is used to acquire the first correlation data between the load and the component stress, which is calculated by simulation analysis based on the first preset load data. The test data acquisition module is used to acquire second correlation data between acceleration and component stress obtained from bench tests based on second preset load data; The correlation module is used to establish a third correlation data between acceleration, load, and component stress based on the first and second correlation data. A module is established to build a monitoring model based on the third related data.

8. A monitoring device for the condition of vehicle components, characterized in that, include: Memory, processor, and computer programs stored in memory and capable of running on the processor; When the processor executes the computer program, it implements the method of any one of claims 1-6.

9. A vehicle, characterized in that, include: The monitoring device as described in claim 8, comprising the various components of the vehicle, and an acceleration sensor; The monitoring device is communicatively connected to the acceleration sensor.

10. The vehicle according to claim 9, characterized in that, The acceleration sensor includes a three-dimensional acceleration sensor mounted on the wheel hub of the vehicle chassis; the components include chassis components; the chassis components include wheel hubs, steering knuckles, rods, castings, and sheet metal parts.

11. A computer-readable storage medium, characterized in that, The device contains a computer program that, when executed by the monitored device, implements the method of any one of claims 1-6.

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