Automobile test field road surface damage monitoring method, device, equipment and storage medium

Abstract

A method, apparatus, equipment, and storage medium for monitoring road surface damage at an automotive proving ground, relating to the field of automotive testing technology, includes: acquiring real-time position information and corresponding Z-direction acceleration of the target test vehicle while it is driving within the target proving ground using a positioning system and an acceleration sensing module deployed on the target test vehicle; performing polynomial filtering and averaging on the Z-direction acceleration within a target window length to obtain a comprehensive weighted root mean square (RMS) acceleration. The damage status of the target proving ground surface corresponding to the real-time position information is determined based on the relationship between the comprehensive weighted RMS acceleration and the standard value of the target weighted RMS acceleration corresponding to the real-time position information. This application can effectively improve the safety, accuracy, and real-time performance of road surface damage monitoring at automotive proving grounds, while also reducing monitoring costs.

Description

Technical Field

[0001] This application relates to the field of automotive testing technology, specifically to a method, device, equipment, and storage medium for monitoring road surface damage at automotive test tracks. Background Technology

[0002] To ensure the overall performance and component reliability of new products, extensive vehicle testing is required during the research and development and finalization of new models. Automotive proving grounds are crucial for performance and durability testing, but their test surfaces are prone to damage, directly impacting the accuracy and reliability of test results and even posing safety hazards.

[0003] Among related technologies, the main solutions for monitoring road surface damage at automotive proving grounds include: (1) Manual inspection, however, requires a long cycle and is difficult to detect road damage in a timely manner, which may cause the test vehicle to drive in unsafe road conditions, thus increasing the risk of accidents.

[0004] (2) Large-scale monitoring equipment is deployed in the site, but large-scale monitoring equipment is not only expensive, but also difficult to move and insufficient to cover all roads in the test site, resulting in monitoring blind spots.

[0005] (3) Install high-speed cameras on both sides of the vehicle to achieve high-precision monitoring of the road surface by driving the vehicle in the field. However, this solution relies on strong image and video analysis capabilities and large-scale data storage equipment, resulting in high testing costs.

[0006] It is evident that how to safely, accurately, in real-time, and at low cost monitor road surface damage at automotive proving grounds is a pressing issue that needs to be addressed. Summary of the Invention

[0007] This application provides a method, device, equipment, and storage medium for monitoring road surface damage at automotive proving grounds, which can effectively improve the safety, accuracy, and real-time performance of road surface damage monitoring at automotive proving grounds, while also reducing monitoring costs.

[0008] In a first aspect, embodiments of this application provide a method for monitoring road surface damage at an automotive proving ground, the method comprising: The positioning system and acceleration sensing module deployed on the target test vehicle collect the real-time position information of the target test vehicle while it is driving in the target test field, as well as the Z-direction acceleration corresponding to the real-time position information. The Z-direction acceleration within the target window length is subjected to polynomial filtering fitting and mean processing to obtain the comprehensive weighted root mean square acceleration. The damage status of the target test field pavement corresponding to the real-time location information is determined based on the relationship between the root mean square of the comprehensive weighted acceleration and the standard value of the root mean square of the target weighted acceleration corresponding to the real-time location information.

[0009] In conjunction with the first aspect, in one embodiment, the real-time location information includes latitude and longitude coordinate information. Before the step of performing polynomial filtering and averaging on the Z-direction acceleration within the target window length to obtain the comprehensive weighted root mean square acceleration, the method further includes: The latitude and longitude coordinates are mapped onto the target map corresponding to the target test site to determine the target location; The target road type corresponding to the target location is determined based on the preset mapping relationship between road location and road type; The target window length and target polynomial order corresponding to the target road surface type are determined by the preset mapping relationship between road surface type, window length, and polynomial order.

[0010] In conjunction with the first aspect, in one embodiment, before the step of performing polynomial filtering and averaging on the Z-direction acceleration within the target window length to obtain the comprehensive weighted root mean square acceleration, the method further includes: The real-time vehicle speed corresponding to the real-time location information is obtained through the positioning system. The target road type corresponding to the real-time vehicle speed is determined based on the preset mapping relationship between vehicle speed and road type. The target window length and target polynomial order corresponding to the target road surface type are determined by the preset mapping relationship between road surface type, window length, and polynomial order.

[0011] In conjunction with the first aspect, in one embodiment, the acceleration sensing module includes multiple modules, and the step of performing polynomial filtering and averaging on the Z-direction acceleration within the target window length to obtain the comprehensive weighted root mean square acceleration includes: For the Z-direction acceleration collected by each acceleration sensing module, the Z-direction acceleration within the target window length is subjected to polynomial filtering and fitting to obtain the filtered acceleration. The average value of all filtered accelerations is then obtained. The weighted root mean square of all accelerations is calculated to obtain the comprehensive weighted root mean square of acceleration.

[0012] In conjunction with the first aspect, in one implementation, determining the damage status of the target test track pavement corresponding to the real-time location information based on the relationship between the comprehensive weighted root mean square acceleration and the standard value of the target weighted root mean square acceleration corresponding to the real-time location information includes: If the root mean square of the comprehensive weighted acceleration is greater than the standard value of the root mean square of the target weighted acceleration, it is determined that the road surface of the target test field corresponding to the real-time location information is damaged. If the root mean square of the comprehensive weighted acceleration is not greater than the standard value of the root mean square of the target weighted acceleration, it is determined that there is no damage to the target test field road surface corresponding to the real-time location information.

[0013] In conjunction with the first aspect, in one embodiment, after the step of determining that the target test track pavement corresponding to the real-time location information is damaged when the comprehensive weighted root mean square acceleration is greater than the target weighted root mean square acceleration standard value, the method further includes: Calculate the target difference between the root mean square of the overall weighted acceleration and the standard value of the root mean square of the target weighted acceleration; The target damage level is determined based on the mapping relationship between the preset damage level and the difference range, and the target damage level includes minor damage, moderate damage and severe damage.

[0014] Secondly, embodiments of this application provide a road surface damage monitoring device for automotive proving grounds, the road surface damage monitoring device for automotive proving grounds includes: The data acquisition module is used to collect the real-time position information of the target test vehicle and the Z-direction acceleration corresponding to the real-time position information by means of the positioning system and acceleration sensing module deployed on the target test vehicle when it is driving in the target test field. The data processing module is used to perform polynomial filtering and mean processing on the Z-direction acceleration within the target window length to obtain the root mean square of the comprehensive weighted acceleration. The damage monitoring module is used to determine the damage status of the target test track pavement corresponding to the real-time location information based on the relationship between the root mean square of the comprehensive weighted acceleration and the standard value of the root mean square of the target weighted acceleration corresponding to the real-time location information.

[0015] In conjunction with the second aspect, in one implementation, the real-time location information includes latitude and longitude coordinates, and the data processing module is further configured to: The latitude and longitude coordinates are mapped onto the target map corresponding to the target test site to determine the target location; The target road type corresponding to the target location is determined based on the preset mapping relationship between road location and road type; The target window length and target polynomial order corresponding to the target road surface type are determined by the preset mapping relationship between road surface type, window length, and polynomial order.

[0016] In conjunction with the second aspect, in one embodiment, the data processing module is further configured to: The real-time vehicle speed corresponding to the real-time location information is obtained through the positioning system. The target road type corresponding to the real-time vehicle speed is determined based on the preset mapping relationship between vehicle speed and road type. The target window length and target polynomial order corresponding to the target road surface type are determined by the preset mapping relationship between road surface type, window length, and polynomial order.

[0017] In conjunction with the second aspect, in one embodiment, the acceleration sensing module includes multiple modules, and the data processing module is specifically used for: For the Z-direction acceleration collected by each acceleration sensing module, the Z-direction acceleration within the target window length is subjected to polynomial filtering and fitting to obtain the filtered acceleration. The average value of all filtered accelerations is then obtained. The weighted root mean square of all accelerations is calculated to obtain the comprehensive weighted root mean square of acceleration.

[0018] In conjunction with the second aspect, in one implementation, the damage monitoring module is specifically used for: If the root mean square of the comprehensive weighted acceleration is greater than the standard value of the root mean square of the target weighted acceleration, it is determined that the road surface of the target test field corresponding to the real-time location information is damaged. If the root mean square of the comprehensive weighted acceleration is not greater than the standard value of the root mean square of the target weighted acceleration, it is determined that there is no damage to the target test field road surface corresponding to the real-time location information.

[0019] In conjunction with the second aspect, in one embodiment, the damage monitoring module is further used for: Calculate the target difference between the root mean square of the overall weighted acceleration and the standard value of the root mean square of the target weighted acceleration; The target damage level is determined based on the mapping relationship between the preset damage level and the difference range, and the target damage level includes minor damage, moderate damage and severe damage.

[0020] Thirdly, this application provides a road surface damage monitoring device for an automotive proving ground. The road surface damage monitoring device includes a processor, a memory, and an automotive proving ground road surface damage monitoring program stored in the memory and executable by the processor. When the automotive proving ground road surface damage monitoring program is executed by the processor, it implements the steps of the aforementioned automotive proving ground road surface damage monitoring method.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium storing a road surface damage monitoring program for an automotive proving ground, wherein when the road surface damage monitoring program for an automotive proving ground is executed by a processor, it implements the steps of the aforementioned road surface damage monitoring method for an automotive proving ground.

[0022] The beneficial effects of the technical solutions provided in this application include: By using a positioning system and acceleration sensing module deployed on the target test vehicle, real-time position information and Z-direction acceleration corresponding to the real-time position information are collected as the vehicle travels within the target test track. Based on this Z-direction acceleration, the weighted acceleration at each measuring point can be calculated to obtain the ride comfort data processing results. Specifically, the Z-direction acceleration within the target window length is subjected to polynomial filtering fitting and mean processing to obtain the root mean square of the comprehensive weighted acceleration. The smaller the root mean square value of the comprehensive weighted acceleration, the better the ride comfort of the vehicle. Finally, based on the relationship between the root mean square of the comprehensive weighted acceleration and the standard value of the root mean square of the weighted acceleration corresponding to the real-time position information, it is possible to accurately determine whether there is damage to the road surface of the target test track corresponding to the real-time position information. This method not only eliminates the need for manual inspection, the deployment of large-scale monitoring equipment on the site, and reliance on extremely strong image and video analysis capabilities and large-scale data storage devices, but also effectively improves the safety, accuracy, and real-time performance of road surface damage monitoring in automotive test tracks, while reducing monitoring costs. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating an embodiment of the method for monitoring road surface damage at an automotive proving ground according to this application. Figure 2 For this application Figure 1 A detailed flowchart of step S20; Figure 3 This is a schematic diagram of the functional modules of an embodiment of the road surface damage monitoring device for automotive proving grounds in this application; Figure 4 This is a schematic diagram of the hardware structure of the road surface damage monitoring equipment for automotive test tracks involved in the embodiments of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0026] In one aspect, embodiments of this application provide a method for monitoring road surface damage at an automotive proving ground.

[0027] In one embodiment, reference is made to Figure 1 , Figure 1 This is a schematic flowchart illustrating an embodiment of the method for monitoring road surface damage at an automotive proving ground according to this application. Figure 1 As shown, the methods for monitoring road surface damage at automotive proving grounds include: Step S10: The positioning system and acceleration sensing module deployed on the target test vehicle are used to collect the real-time position information of the target test vehicle while it is driving in the target test field, as well as the Z-direction acceleration corresponding to the real-time position information.

[0028] As an example, it is understandable that the high frequency and density of test vehicle operation in automotive proving grounds makes the road surface prone to damage and difficult to detect in a short time, posing a serious threat to the safety of on-site testing. This embodiment will provide a real-time monitoring method for road surface damage in automotive proving grounds, which can monitor and identify whether the road surface is damaged, simplify the road inspection process of the proving ground, and ensure the authenticity, safety and accuracy of the test.

[0029] It should be understood that ride comfort refers to the performance of avoiding discomfort, fatigue, or even health damage caused by vibrations and impacts during vehicle operation, or damage to goods. Therefore, when a driver completes the vehicle's journey according to a preset road map, if large-scale damage occurs on the test track, the vehicle's ride comfort data will fluctuate significantly, exceeding the standard threshold. Therefore, this embodiment will calculate the weighted acceleration at each measuring point using the Z-direction acceleration generated by the vehicle while driving on the test track to obtain ride comfort data processing results. This allows for monitoring of road surface damage and, based on the vehicle's real-time location information on the test track, the localization of road surface damage. The Z-direction refers to the direction pointed to by the Z-axis in a world coordinate system constructed with the vehicle's center as the center.

[0030] It should be noted that the target test track refers to the test track where road surface damage monitoring is required; the target test vehicle refers to the vehicle operating on the target test track and used to install the positioning system and acceleration sensor module; the positioning system refers to the positioning device used to collect real-time position information such as the target test vehicle's location during its operation, preferably a GPS positioning system, which is installed at the top center of the target test vehicle to collect the vehicle's real-time trajectory; the acceleration sensor module refers to the data used to collect Z-direction acceleration generated during the target test vehicle's operation, preferably an accelerometer pad, which is installed in the vehicle's seats, backrests, footwells, etc. Therefore, this embodiment can collect the real-time position information of the target test vehicle while it is driving on the target test track and the corresponding Z-direction acceleration (i.e., the Z-direction acceleration of the target test vehicle at different locations on the target test track) through the positioning system and the acceleration sensor module, respectively; preferably, the acceleration sensor module is set to operate at a high frequency of 200Hz (i.e., sampling interval is...). The acceleration in the Z direction of the target test vehicle during its driving process is collected.

[0031] Step S20: Perform polynomial filtering and mean processing on the Z-direction acceleration within the target window length to obtain the root mean square of the comprehensive weighted acceleration.

[0032] For example, the target window length L refers to the range of data to be filtered. Its specific value can be determined according to actual needs and is not limited here. For example, if the target window length L is 2k+1, then the corresponding window contains the data point to be filtered and the k sets of data before and after this data point. That is, for a given s-th data point... There are k points on the left and k points on the right, where k is a positive integer. Based on this, this embodiment will filter and average all Z-direction accelerations within the target window length L to obtain the root mean square of the comprehensive weighted acceleration. The smaller the root mean square value of the comprehensive weighted acceleration, the better the smoothness of the vehicle. It is worth noting that, in order to ensure that the filtered data can effectively remove noise while retaining road condition-related features, such as impact signals (i.e., vibrations caused by potholes), the Savitzky-Golay filter can be preferably used to filter the Z-direction acceleration information. It should be understood that the Savitzky-Golay filter is also called a polynomial filter. It is essentially a convolutional smoother based on local polynomial least squares fitting. Its core idea is to use an nth-order polynomial to optimally fit the data points within a moving window, and use the value of the fitted polynomial at the center point of the window as the smoothed output value.

[0033] Further, in one embodiment, the real-time location information includes latitude and longitude coordinate information. Before the step of performing polynomial filtering and averaging on the Z-direction acceleration within the target window length to obtain the comprehensive weighted root mean square acceleration, the method further includes: The latitude and longitude coordinates are mapped onto the target map corresponding to the target test site to determine the target location; The target road type corresponding to the target location is determined based on the preset mapping relationship between road location and road type; The target window length and target polynomial order corresponding to the target road surface type are determined by the preset mapping relationship between road surface type, window length, and polynomial order.

[0034] As an example, it should be understood that vehicles need to travel at different speeds on different types of road surfaces to ensure the reliability of the test. For example, when controlling the vehicle to travel on the test track, it is preferable to drive at a constant speed of less than 30 km / h on the enhanced road, at a constant speed of 30-80 km / h on the ordinary road and performance road, and at a constant speed of greater than 80 km / h on the high-speed loop. It is worth noting that in this embodiment, the performance of the polynomial filter will be changed by adjusting the parameters of the polynomial order n and the window width L, and the parameter adjustment rules are as follows: (1) Smoothing effect of L on curve: The smaller the value of L, the closer the curve is to the actual measured curve; while the larger the value of L, the stronger the smoothing effect (L must be a positive odd number).

[0035] (2) The smoothing effect of n value on curve: The larger the n value, the closer the curve is to the real curve, but the smoothing effect is less obvious. The maximum value of n is L-1. When n=L-1, there is no smoothing effect.

[0036] In addition, the window length L is inversely correlated with the vehicle speed. At high speeds, the vibration signal frequency increases, so the window length needs to be shortened to retain details. The polynomial order n decreases as the vehicle speed increases, meaning that the complexity of high-speed signals decreases to avoid overfitting.

[0037] Based on this, the optimized design of the SG filter's parameter dynamic adjustment rule in this embodiment is as follows: Table 1 Parameter Dynamic Adjustment Rules

[0038] It should be noted that the parameter combinations shown in Table 1 achieve a good balance between smoothing minor fluctuations and maintaining the accuracy of signal phase and amplitude. However, the items in Table 1 can be adaptively adjusted according to actual needs, which is not limited here. As shown in Table 1, different road surface types correspond to different vehicle speeds, window lengths, and polynomial orders. Therefore, before fitting the X-direction acceleration, the latitude and longitude coordinates corresponding to the X-direction acceleration are mapped to the target map corresponding to the target test field to determine the target position. That is, the latitude and longitude coordinates of the collected driving trajectory are located in the target map to obtain accurate positioning and achieve real-time vehicle tracking. The target map can be calibrated in advance through pre-tests, that is, the road surface position information and road surface type in the target test field are calibrated, that is, the data of M1 type test vehicles driving multiple times at specific speeds on reinforced roads, ordinary roads, performance roads, and high-speed ring roads are collected. The test parameters were used to establish a road surface smoothness data map to generate a target map. It can be seen that the target map not only contains the latitude and longitude information of each location in the target test field, but also the road surface type corresponding to each location. Therefore, the target road surface type corresponding to the target location can be determined according to the pre-calibrated mapping relationship between road surface location and road surface type. Then, the target window length and target polynomial order corresponding to the target road surface type can be determined through the mapping relationship between road surface type, window length, and polynomial order in Table 1. For example, if the road surface type is reinforced road, then the target window length is 21 points and the target polynomial order is 4th order. Then, a 4th order polynomial filter fitting is performed on the 21 Z-direction accelerations.

[0039] Furthermore, in one embodiment, before the step of performing polynomial filtering and averaging on the Z-direction acceleration within the target window length to obtain the comprehensive weighted root mean square acceleration, the method further includes: The real-time vehicle speed corresponding to the real-time location information is obtained through the positioning system. The target road type corresponding to the real-time vehicle speed is determined based on the preset mapping relationship between vehicle speed and road type. The target window length and target polynomial order corresponding to the target road surface type are determined by the preset mapping relationship between road surface type, window length, and polynomial order.

[0040] As an example, in this embodiment, in addition to collecting the real-time location information of the target test vehicle during its driving process, the positioning system also collects the real-time vehicle speed of the target test vehicle at different locations. Based on this, the target road type corresponding to the real-time vehicle speed can be determined according to the mapping relationship between vehicle speed and road type in Table 1. For example, if the vehicle speed is 80 km / h, the road type is a high-ring road. Then, the target window length and target polynomial order corresponding to the target road type are determined through the mapping relationship between road type and window length and polynomial order in Table 1. For example, if the road type is a high-ring road, the target window length is 11 points and the target polynomial order is 2nd order. Then, a 2nd order polynomial filter fitting is performed on the 11 Z-direction accelerations.

[0041] Furthermore, in one embodiment, the acceleration sensing module includes multiple modules, see [link to relevant documentation]. Figure 2 As shown, the step of performing polynomial filtering and averaging on the Z-direction acceleration within the target window length to obtain the comprehensive weighted root mean square acceleration includes: Step S201: For the Z-direction acceleration collected by each acceleration sensing module, perform polynomial filtering fitting on the Z-direction acceleration within the target window length to obtain the filtered acceleration, and perform mean processing on all filtered accelerations to obtain the average acceleration. Step S202: Calculate the weighted root mean square of all acceleration averages to obtain the comprehensive weighted root mean square of acceleration.

[0042] As an example, in this embodiment, if multiple acceleration sensing modules are installed on the target test vehicle, for example, at six measuring points: above the driver's seat cushion (i.e., measuring point 1), the driver's seat back (i.e., measuring point 2), above the last row seat cushion on the same side as the driver's seat (i.e., measuring point 3), the last row seat back on the same side as the driver's seat (i.e., measuring point 4), the left footrest floor (i.e., measuring point 5), and the right footrest floor (i.e., measuring point 6), then the Z-direction acceleration collected by each acceleration sensing module needs to be filtered. Since the filtering method and principle are the same for each acceleration sensing module, one of them will be used as an example to illustrate the specific filtering process: For the Z-direction acceleration in the target window length L = 2k + 1, it can first be averaged and filtered using a moving average filter, that is, for a given s-th data point... ,have: (1) In the formula, This represents the average filtering result.

[0043] Then, the polynomial filter performs least-squares fitting of the average filtering result with an nth-order polynomial, and uses the calculated center point of the fitted polynomial curve as the post-filtered acceleration.

[0044] For polynomial filtering fitting, an nth-order polynomial is defined as the fitting curve: (2) Assuming the window length L = 2k + 1, for a given s-th data point... ,have: (3) The above formula can be expressed in matrix form as follows: (4) Based on this, the coefficient vector of the fitted polynomial can be obtained. The least squares solution is: (5) Then the polynomial filter value We can use the pseudo-inverse of H and left-multiply by H to calculate it, and we have: (6) Among them, the filter coefficient matrix B can be obtained in advance through pre-testing. That is, before the target test field is damaged, the M1 passenger car is selected as the test vehicle type, and test parameters are collected for multiple drives on different types of road surfaces in the target test field. The filter coefficient matrix B is determined by the test parameters. It can be understood that the filter coefficient matrix B corresponding to polynomials of different orders is different. It can be seen that the average filtering result only needs to be substituted into equation (6) to calculate the acceleration after filtering.

[0045] The above filtering method can be used to obtain multiple filtered accelerations for each measuring point within a certain time period (e.g., 1 second). For example, there are 200 filtered accelerations at measurement point 1, and similarly, there are 200 filtered accelerations at measurement points 2 to 5 respectively; then, the absolute value of all filtered accelerations at each measurement point within a certain time period is calculated. Perform an averaging calculation to obtain the average acceleration value for each measuring point: (7) In the formula, This represents the average acceleration at the nth measuring point. This represents the filtered acceleration at the nth measurement point, and M represents the total number of filtered accelerations at the nth measurement point, for example, M=200.

[0046] Finally, the weighted root mean square of the acceleration values ​​at all measuring points is calculated to obtain the comprehensive weighted root mean square of acceleration: (8) In the formula, This represents the root mean square of the weighted average acceleration, and N represents the total number of measurement points (i.e., the total number of acceleration sensing modules, for example, N=6). This represents the axial weighting coefficient corresponding to the nth measuring point, and its specific value can be found in Table 2.

[0047] Table 2 Axial weighting coefficients

[0048] It should be noted that Table 2 above is only a presentation of an example, and the contents of Table 2 can be adapted to meet actual needs, without limitation.

[0049] Step S30: Determine the damage status of the target test field road surface corresponding to the real-time location information based on the relationship between the root mean square of the comprehensive weighted acceleration and the standard value of the root mean square of the target weighted acceleration corresponding to the real-time location information.

[0050] In this exemplary embodiment, the weighted root mean square (RMS) acceleration standard value is obtained in advance through pre-test calibration. Specifically, before any damage occurs at the target test track, the M1 passenger car is selected as the test vehicle type, and test parameters are collected from multiple drives on different types of road surfaces at the target test track to determine the smoothness signal range (i.e., the weighted RMS acceleration standard value) for different road surface types. Based on this, after determining the comprehensive weighted RMS acceleration, the latitude and longitude coordinates in the real-time location information corresponding to the comprehensive weighted RMS acceleration need to be mapped to the target map to determine the target location. Then, based on the pre-calibrated mapping relationship between road surface location and road surface type, the target road surface type corresponding to the target location is determined. Next, based on the mapping relationship between road surface type and weighted RMS acceleration standard value, the target weighted RMS acceleration standard value corresponding to the target road surface type is determined. Finally, based on the magnitude relationship between the comprehensive weighted RMS acceleration and the target weighted RMS acceleration standard value, the damage status of the target test track road surface (i.e., the road surface at the target location) corresponding to the real-time location information can be determined.

[0051] As can be seen, this embodiment monitors various road conditions at the test track in real time using GPS positioning information and vehicle ride comfort data (i.e., Z-direction acceleration). It has advantages such as real-time monitoring, comprehensive coverage, accurate data, and intelligent management. It not only eliminates the need for manual inspections and the deployment of large-scale monitoring equipment on the site, but also eliminates the need to rely on extremely strong image and video analysis capabilities and large-scale data storage devices. It effectively improves the safety, accuracy, and real-time performance of road surface damage monitoring at the automotive test track, while also reducing monitoring costs and optimizing maintenance plans and resource allocation, providing a scientific basis for test track management.

[0052] Further, in one embodiment, determining the damage status of the target test track pavement corresponding to the real-time location information based on the relationship between the comprehensive weighted root mean square acceleration and the standard value of the target weighted root mean square acceleration corresponding to the real-time location information includes: If the root mean square of the comprehensive weighted acceleration is greater than the standard value of the root mean square of the target weighted acceleration, it is determined that the road surface of the target test field corresponding to the real-time location information is damaged. If the root mean square of the comprehensive weighted acceleration is not greater than the standard value of the root mean square of the target weighted acceleration, it is determined that there is no damage to the target test field road surface corresponding to the real-time location information.

[0053] As an example, in this embodiment, if the root mean square of the overall weighted acceleration is greater than the standard value of the root mean square of the target weighted acceleration, it indicates that the vehicle has received excitation from an uneven road surface, that is, the target test track road surface corresponding to the real-time location information is uneven, and the target test track road surface is determined to be damaged; while if the root mean square of the overall weighted acceleration is less than or equal to the standard value of the root mean square of the target weighted acceleration, it indicates that the vehicle has not received excitation from an uneven road surface, that is, the target test track road surface corresponding to the real-time location information is smooth, and the target test track road surface is determined to be undamaged.

[0054] Furthermore, in one embodiment, after the step of determining that the target test field road surface corresponding to the real-time location information is damaged when the comprehensive weighted root mean square acceleration is greater than the target weighted root mean square acceleration standard value, the method further includes: Calculate the target difference between the root mean square of the overall weighted acceleration and the standard value of the root mean square of the target weighted acceleration; The target damage level is determined based on the mapping relationship between the preset damage level and the difference range, and the target damage level includes minor damage, moderate damage and severe damage.

[0055] As an example, in this embodiment, to provide more accurate damage monitoring results, the degree of damage can be classified into levels, such as minor damage, moderate damage, and severe damage. Of course, other classifications can be made according to actual needs, which are not limited here. It should be noted that the difference range corresponding to different damage levels can be calibrated based on preliminary experiments. The upper limit of the difference range corresponding to minor damage is less than the lower limit of the difference range corresponding to moderate damage, and the upper limit of the difference range corresponding to moderate damage is less than the lower limit of the difference range corresponding to severe damage. In addition, the difference range for the same damage level corresponding to different types of pavement can be the same or different, which can be determined according to specific needs, and are not limited here.

[0056] Based on this, once it is determined that the target test track pavement is damaged, the difference between the root mean square of the comprehensive weighted acceleration and the standard value of the root mean square of the target weighted acceleration will be calculated to obtain the target difference value. Then, it will be determined which damage level the absolute value of the target difference value falls within, thereby determining the target damage level of the target test track pavement. For example, if the absolute value of the target difference value is within the difference range corresponding to moderate damage, then moderate damage will be taken as the target damage level of the target test track pavement.

[0057] It is worth noting that the above-mentioned method for monitoring road surface damage at automotive test tracks can be implemented through a remote data processing center. This center receives latitude and longitude coordinates and vehicle speed information from the positioning system, as well as acceleration time-domain signals collected by the acceleration sensor module, via data transmission methods such as 5G communication. The center then performs filtering and analysis on the acceleration time-domain signal data. When a sudden change occurs in the root mean square of the weighted acceleration, exceeding a set threshold, it indicates that the vehicle has been excited by an uneven road surface. Combined with the GPS location recording the occurrence of this change, the location of the road surface damage is accurately determined, and a warning signal is promptly sent to the site management department, indicating potential road damage so that appropriate measures can be taken, such as dispatching maintenance personnel. Furthermore, the processed data can be stored in a target map for historical data analysis and trend prediction. Simultaneously, management personnel can view road conditions in real time and query historical data through a user interface to manage road conditions.

[0058] In summary, the method for monitoring road surface damage at automotive proving grounds provided in this embodiment can achieve at least the following beneficial effects: (1) Strong real-time performance: It can monitor the road conditions of the test site in real time and detect road surface problems in a timely manner. Compared with traditional monitoring methods, it has a significant advantage in timeliness, which helps to prevent and respond quickly to road faults and reduce the impact on the test process and safety. (2) Wide coverage: With the high frequency of operation of the test vehicle in the field, real-time monitoring of the field roads can be achieved, avoiding the problem of insufficient coverage of traditional fixed monitoring points and providing more comprehensive road condition information. (3) Low cost: Using the existing test vehicle as the data acquisition carrier, there is no need to deploy additional large-scale monitoring equipment, which reduces hardware and maintenance costs and has high economic efficiency and feasibility.

[0059] Secondly, this application also provides a vehicle proving ground road damage monitoring device.

[0060] In one embodiment, reference is made to Figure 3 , Figure 3 This is a functional module diagram of an embodiment of the road surface damage monitoring device for automotive proving grounds according to this application. Figure 3 As shown, the road surface damage monitoring device at the automotive proving ground includes: The data acquisition module is used to collect the real-time position information of the target test vehicle and the Z-direction acceleration corresponding to the real-time position information by means of the positioning system and acceleration sensing module deployed on the target test vehicle when it is driving in the target test field. The data processing module is used to perform polynomial filtering and mean processing on the Z-direction acceleration within the target window length to obtain the root mean square of the comprehensive weighted acceleration. The damage monitoring module is used to determine the damage status of the target test track pavement corresponding to the real-time location information based on the relationship between the root mean square of the comprehensive weighted acceleration and the standard value of the root mean square of the target weighted acceleration corresponding to the real-time location information.

[0061] Furthermore, in one embodiment, the real-time location information includes latitude and longitude coordinates, and the data processing module is further used for: The latitude and longitude coordinates are mapped onto the target map corresponding to the target test site to determine the target location; The target road type corresponding to the target location is determined based on the preset mapping relationship between road location and road type; The target window length and target polynomial order corresponding to the target road surface type are determined by the preset mapping relationship between road surface type, window length, and polynomial order.

[0062] Furthermore, in one embodiment, the data processing module is also used for: The real-time vehicle speed corresponding to the real-time location information is obtained through the positioning system. The target road type corresponding to the real-time vehicle speed is determined based on the preset mapping relationship between vehicle speed and road type. The target window length and target polynomial order corresponding to the target road surface type are determined by the preset mapping relationship between road surface type, window length, and polynomial order.

[0063] Furthermore, in one embodiment, the acceleration sensing module includes multiple modules, and the data processing module is specifically used for: For the Z-direction acceleration collected by each acceleration sensing module, the Z-direction acceleration within the target window length is subjected to polynomial filtering and fitting to obtain the filtered acceleration. The average value of all filtered accelerations is then obtained. The weighted root mean square of all accelerations is calculated to obtain the comprehensive weighted root mean square of acceleration.

[0064] Furthermore, in one embodiment, the damage monitoring module is specifically used for: If the root mean square of the comprehensive weighted acceleration is greater than the standard value of the root mean square of the target weighted acceleration, it is determined that the road surface of the target test field corresponding to the real-time location information is damaged. If the root mean square of the comprehensive weighted acceleration is not greater than the standard value of the root mean square of the target weighted acceleration, it is determined that there is no damage to the target test field road surface corresponding to the real-time location information.

[0065] Furthermore, in one embodiment, the damage monitoring module is specifically used for: Calculate the target difference between the root mean square of the overall weighted acceleration and the standard value of the root mean square of the target weighted acceleration; The target damage level is determined based on the mapping relationship between the preset damage level and the difference range, and the target damage level includes minor damage, moderate damage and severe damage.

[0066] The functions of each module in the aforementioned vehicle proving ground road damage monitoring device correspond to the steps in the aforementioned vehicle proving ground road damage monitoring method embodiment, and their functions and implementation processes will not be described in detail here.

[0067] Thirdly, this application provides a road surface damage monitoring device for an automotive proving ground. The road surface damage monitoring device can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0068] Reference Figure 4 , Figure 4 This is a schematic diagram of the hardware structure of the road surface damage monitoring equipment for automotive proving grounds involved in the embodiments of this application. In this embodiment, the road surface damage monitoring equipment for automotive proving grounds may include a processor, a memory, a communication interface, and a communication bus.

[0069] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0070] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the road surface damage monitoring equipment at the automotive proving ground, as well as interfaces used for interconnecting the equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0071] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0072] The processor can be a general-purpose processor, which can call the vehicle proving ground pavement damage monitoring program stored in the memory and execute the vehicle proving ground pavement damage monitoring method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the vehicle proving ground pavement damage monitoring program is called can refer to the various embodiments of the vehicle proving ground pavement damage monitoring method of this application, and will not be repeated here.

[0073] Those skilled in the art will understand that Figure 4 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0074] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0075] The present application has a storage medium storing a road surface damage monitoring program for an automotive proving ground, wherein when the automotive proving ground road surface damage monitoring program is executed by a processor, it implements the steps of the automotive proving ground road surface damage monitoring method described above.

[0076] The method implemented when the road surface damage monitoring program at the automotive proving ground is executed can be referred to in the various embodiments of the road surface damage monitoring method at the automotive proving ground of this application, and will not be repeated here.

[0077] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0078] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0079] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0080] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0081] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0082] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0083] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for monitoring road surface damage at an automotive proving ground, characterized in that, The method for monitoring road surface damage at the automotive proving ground includes: The positioning system and acceleration sensing module deployed on the target test vehicle collect the real-time position information of the target test vehicle while it is driving in the target test field, as well as the Z-direction acceleration corresponding to the real-time position information. The Z-direction acceleration within the target window length is subjected to polynomial filtering fitting and mean processing to obtain the comprehensive weighted root mean square acceleration. The damage status of the target test field pavement corresponding to the real-time location information is determined based on the relationship between the root mean square of the comprehensive weighted acceleration and the standard value of the root mean square of the target weighted acceleration corresponding to the real-time location information.

2. The method for monitoring road surface damage at an automotive proving ground as described in claim 1, characterized in that, The real-time location information includes latitude and longitude coordinates. Before the step of performing polynomial filtering and averaging on the Z-direction acceleration within the target window length to obtain the root mean square of the comprehensive weighted acceleration, the method further includes: The latitude and longitude coordinates are mapped onto the target map corresponding to the target test site to determine the target location; The target road type corresponding to the target location is determined based on the preset mapping relationship between road location and road type; The target window length and target polynomial order corresponding to the target road surface type are determined by the preset mapping relationship between road surface type, window length, and polynomial order.

3. The method for monitoring road surface damage at an automotive proving ground as described in claim 1, characterized in that, Before the step of performing polynomial filtering and averaging on the Z-direction acceleration within the target window length to obtain the comprehensive weighted root mean square acceleration, the method further includes: The real-time vehicle speed corresponding to the real-time location information is obtained through the positioning system. The target road type corresponding to the real-time vehicle speed is determined based on the preset mapping relationship between vehicle speed and road type. The target window length and target polynomial order corresponding to the target road surface type are determined by the preset mapping relationship between road surface type, window length, and polynomial order.

4. The method for monitoring road surface damage at an automotive proving ground as described in claim 1, characterized in that, The acceleration sensing module includes multiple modules. The step of performing polynomial filtering and averaging on the Z-direction acceleration within the target window length to obtain the comprehensive weighted root mean square acceleration includes: For the Z-direction acceleration collected by each acceleration sensing module, the Z-direction acceleration within the target window length is subjected to polynomial filtering and fitting to obtain the filtered acceleration. The average value of all filtered accelerations is then obtained. The weighted root mean square of all accelerations is calculated to obtain the comprehensive weighted root mean square of acceleration.

5. The method for monitoring road surface damage at an automotive proving ground as described in claim 1, characterized in that, The determination of the damage status of the target test track pavement corresponding to the real-time location information based on the relationship between the comprehensive weighted root mean square acceleration and the standard value of the target weighted root mean square acceleration corresponding to the real-time location information includes: If the root mean square of the comprehensive weighted acceleration is greater than the standard value of the root mean square of the target weighted acceleration, it is determined that the road surface of the target test field corresponding to the real-time location information is damaged. If the root mean square of the comprehensive weighted acceleration is not greater than the standard value of the root mean square of the target weighted acceleration, it is determined that there is no damage to the target test field road surface corresponding to the real-time location information.

6. The method for monitoring road surface damage at an automotive proving ground as described in claim 5, characterized in that, After the step of determining that the target test field road surface corresponding to the real-time location information is damaged when the comprehensive weighted root mean square acceleration is greater than the target weighted root mean square acceleration standard value, the method further includes: Calculate the target difference between the root mean square of the overall weighted acceleration and the standard value of the root mean square of the target weighted acceleration; The target damage level is determined based on the mapping relationship between the preset damage level and the difference range, and the target damage level includes minor damage, moderate damage and severe damage.

7. A road surface damage monitoring device for an automotive proving ground, characterized in that, The road surface damage monitoring device at the automotive proving ground includes: The data acquisition module is used to collect the real-time position information of the target test vehicle and the Z-direction acceleration corresponding to the real-time position information by means of the positioning system and acceleration sensing module deployed on the target test vehicle when it is driving in the target test field. The data processing module is used to perform polynomial filtering and mean processing on the Z-direction acceleration within the target window length to obtain the root mean square of the comprehensive weighted acceleration. The damage monitoring module is used to determine the damage status of the target test track pavement corresponding to the real-time location information based on the relationship between the root mean square of the comprehensive weighted acceleration and the standard value of the root mean square of the target weighted acceleration corresponding to the real-time location information.

8. The vehicle proving ground road damage monitoring device as described in claim 7, characterized in that, The real-time location information includes latitude and longitude coordinates, and the data processing module is further used for: The latitude and longitude coordinates are mapped onto the target map corresponding to the target test site to determine the target location; The target road type corresponding to the target location is determined based on the preset mapping relationship between road location and road type; The target window length and target polynomial order corresponding to the target road surface type are determined by the preset mapping relationship between road surface type, window length, and polynomial order.

9. A road surface damage monitoring device for an automotive proving ground, characterized in that, The vehicle proving ground pavement damage monitoring device includes a processor, a memory, and a vehicle proving ground pavement damage monitoring program stored in the memory and executable by the processor, wherein when the vehicle proving ground pavement damage monitoring program is executed by the processor, it implements the steps of the vehicle proving ground pavement damage monitoring method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a road surface damage monitoring program for an automotive proving ground, wherein when the automotive proving ground road surface damage monitoring program is executed by a processor, it implements the steps of the automotive proving ground road surface damage monitoring method as described in any one of claims 1 to 6.

Images