Road surface state recognition method, device, electronic device and storage medium

Abstract

The present invention provides a road surface condition identification method, device, electronic device, and storage medium, relating to the field of computer technology. The method comprises: collecting signals from vehicle tires within a target time period, the signals including multiple target data; processing the signals based on each target data to obtain a frequency spectrum corresponding to the signals; determining a target value based on the root mean square value of the frequency domain signals in the frequency spectrum in at least one frequency band, wherein the frequency band corresponds to a modal characteristic of the tire; and identifying the road surface condition based on the target value. By combining the root mean square value of the frequency spectrum corresponding to the vehicle tire signals within the target time period in at least one frequency band with the modal characteristics of the tire itself, efficient and accurate identification of the road surface condition within the target time period is achieved, thereby improving the speed and accuracy of road surface condition identification.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a road surface state recognition method, device, electronic equipment and storage medium. Background Art

[0002] The use of intelligent tire sensors is increasing in the modern automotive industry, monitoring tire pressure, temperature, and other key performance parameters. These sensors provide crucial data support for improving vehicle safety, optimizing fuel efficiency, and enhancing the driving experience. In recent years, with the advancement of autonomous driving technology, the use of intelligent tire sensors to identify road conditions in real time has become a hot topic of research. Road surface recognition technology can help vehicles better adapt to various driving environments, ensuring safe and efficient driving.

[0003] Existing smart tire sensor technologies primarily rely on pressure or temperature sensors, which can provide data for monitoring tire condition. However, in complex driving environments, such as rain and snow, or on different road types (such as asphalt, gravel, and dirt), these sensors still have significant deficiencies in road surface recognition accuracy and response speed, limiting their adaptability to diverse environments. Furthermore, they often require integration with other vehicle systems, such as dynamic stability control systems, which increases system complexity and cost.

[0004] When it comes to real-time road surface recognition, existing technologies lack effective algorithms to integrate and analyze data collected from tire sensors, resulting in insufficient vehicle adaptability to changing road conditions. Furthermore, they fail to effectively identify rapid changes in road conditions within a short period of time, such as a sudden change from rough to smooth, which is particularly critical in extreme weather conditions.

[0005] Therefore, how to achieve efficient and accurate identification of road conditions under diverse environments and road conditions is an urgent problem that needs to be solved. Summary of the Invention

[0006] The present invention provides a road surface state recognition method, device, electronic device and storage medium to solve the problem of how to achieve efficient and accurate recognition of road surface state in diverse environments and road conditions.

[0007] The present invention provides a road surface state recognition method, comprising:

[0008] Collecting signals from vehicle tires within a target time period, wherein the signals include a plurality of target data;

[0009] Based on each of the target data, the signal is processed to obtain a frequency spectrum corresponding to the signal;

[0010] determining a target value based on a root mean square value of a frequency domain signal in the spectrum graph in at least one frequency band, wherein the frequency band corresponds to a modal feature of the tire;

[0011] Based on the target value, a road surface condition is identified.

[0012] According to a road surface condition identification method provided by the present invention, determining a target value based on the root mean square value of the frequency domain signal in the spectrum diagram in at least one frequency band includes:

[0013] The target value is obtained by multiplying the root mean square values ​​of the frequency domain signals in the spectrum diagram in at least one frequency band.

[0014] According to a road surface condition identification method provided by the present invention, identifying the road surface condition based on the target value includes:

[0015] Determining the range of the target value;

[0016] When the target value is within the first range, identifying the road surface condition as rough asphalt;

[0017] When the target value is within the second range, identifying the road surface state as a cement road;

[0018] When the target value is within the third range, the road surface condition is identified as smooth asphalt.

[0019] According to a road surface condition recognition method provided by the present invention, the signal is processed based on each target data to obtain a spectrum corresponding to the signal, including:

[0020] determining at least one peak position of each of the target data;

[0021] Splitting the signal into at least two signal segments based on the peak positions;

[0022] Based on the at least two signal segments, a frequency spectrum corresponding to the signal is determined.

[0023] According to a road surface condition recognition method provided by the present invention, determining a frequency spectrum corresponding to the signal based on the at least two signal segments includes:

[0024] For each signal segment, determining a target frequency domain signal corresponding to each signal segment;

[0025] Based on the target frequency domain signals corresponding to all the segment signals, a frequency spectrogram corresponding to the signals is determined.

[0026] According to a road surface condition recognition method provided by the present invention, determining the target frequency domain signal corresponding to each signal segment includes:

[0027] Performing windowing on each segment of the signal to obtain a windowed signal;

[0028] Performing zero padding on the windowed signal;

[0029] Performing Fourier transform on the zero-padded signal to obtain a target frequency domain signal corresponding to each segment of the signal.

[0030] According to a road surface condition recognition method provided by the present invention, determining a frequency spectrum corresponding to a target frequency domain signal corresponding to each of all segment signals includes:

[0031] The target frequency domain signals corresponding to all segment signals are averaged to obtain a spectrum diagram corresponding to the signal.

[0032] The present invention also provides a road surface state recognition device, comprising:

[0033] An acquisition module, configured to acquire signals from vehicle tires within a target time period, wherein the signals include a plurality of target data;

[0034] A processing module, configured to process the signal based on each target data to obtain a frequency spectrum corresponding to the signal;

[0035] a determination module, configured to determine a target value based on a root mean square value of a frequency domain signal in the spectrum graph in at least one frequency band, wherein the frequency band corresponds to a modal feature of the tire;

[0036] The identification module is used to identify the road surface state based on the target value.

[0037] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any of the above-described road surface condition recognition methods is implemented.

[0038] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described road surface condition identification methods.

[0039] The road surface condition identification method, device, electronic device, and storage medium provided by the present invention collect signals from vehicle tires within a target time period, wherein the signals include multiple target data; based on each target data, the signals are processed to obtain a frequency spectrum corresponding to the signals; a target value is determined based on the root mean square value of the frequency domain signal in the frequency spectrum in at least one frequency band; the frequency band corresponds to a modal feature of the tire; and based on the target value, the road surface condition is identified, wherein the frequency band corresponds to a modal feature of the tire. By combining the root mean square value of the frequency spectrum corresponding to the vehicle tire signal within the target time period in at least one frequency band with the modal features of the tire itself, efficient and accurate identification of the road surface condition within the target time period is achieved, thereby improving the speed and accuracy of road surface condition identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 This is one of the flow charts of the road surface state identification method provided by the present invention.

[0042] Figure 2 It is a schematic diagram of a spectrum diagram corresponding to the signal provided by the present invention.

[0043] Figure 3 It is a comparative schematic diagram of the frequency spectrum diagrams corresponding to signals under different road conditions provided by the present invention.

[0044] Figure 4 It is a schematic diagram of each segment of the split signal provided by the present invention.

[0045] Figure 5 Schematic diagram of the windowed signal provided by the present invention.

[0046] Figure 6 This is the second flow chart of the road surface state identification method provided by the present invention.

[0047] Figure 7 It is a structural schematic diagram of the road surface state recognition device provided by the present invention.

[0048] Figure 8 It is a schematic diagram of the physical structure of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0050] The following combination Figures 1-6 The road surface state recognition method of the present invention is described.

[0051] Figure 1 This is one of the flow charts of the road surface state recognition method provided by the present invention, such as Figure 1 As shown, the method includes steps 101 to 104.

[0052] Step 101: Collect signals from vehicle tires within a target time period, where the signals include multiple target data.

[0053] It should be noted that the road surface condition recognition method provided by the present invention can be applied to road surface condition recognition scenarios based on intelligent tire sensors; the executor of this method can be a road surface condition recognition device, such as an electronic device, or a control module in the road surface condition recognition device for executing the road surface condition recognition method.

[0054] Specifically, smart tire sensors, such as accelerometer chips, are attached to the inside of vehicle tires. The sampling frequency of the sensors is 1600 Hz. To ensure the real-time feedback of road conditions, data of a target time period (e.g., 1 second (s)) is selected as the analysis time for a frame for road condition recognition.

[0055] The intelligent tire sensor collects tire signals over a target time period. The signals include multiple target data points, such as the z-axis acceleration inside the tire. For example, a signal within 1 second includes 1600 data points.

[0056] It's important to note that because sensors like the accelerometer chip remain attached to the tire's inner wall during rolling, the baseline value for radial acceleration is the centripetal acceleration, which is equal to the product of the tire's inner radius and rolling velocity. Based on the centripetal acceleration, the radial acceleration exhibits a fluctuating characteristic: within each cycle, there are two maxima, and between these two acceleration maxima lies a minimum trough. This phenomenon is caused by tire-road contact deformation. When the accelerometer is not in contact with the road, the radial acceleration is solely centripetal. When the accelerometer is in full contact with the road, the sensor is in near-translational motion, with an infinite radius, resulting in approximately zero centripetal acceleration, corresponding to the radial acceleration minimum trough. Just before and after contact, the tread rubber's elastic deformation increases rapidly, and the radius of curvature decreases, causing a sudden increase in radial acceleration, corresponding to the two radial acceleration maxima.

[0057] Step 102: Process the signal based on each target data to obtain a frequency spectrum corresponding to the signal.

[0058] Specifically, based on each target data, the signal is processed to obtain the corresponding spectrum of the signal, which represents the amplitude distribution of the vehicle tire in the frequency domain. Figure 2 is a schematic diagram of the spectrum diagram corresponding to the signal provided by the present invention, such as Figure 2 As shown in the spectrum diagram, the frequency corresponding to the highest peak is the tire rotation frequency, that is, the number of revolutions the tire makes per second.

[0059] Figure 3 is a comparative schematic diagram of the spectrum diagram corresponding to the signal under different road conditions provided by the present invention, such as Figure 3 As shown in the figure, by comparing the spectrum curves, it is close to the actual perception, that is, the rough asphalt road has the greatest stimulation on the tire, followed by the cement road, and then the smooth asphalt.

[0060] Step 103: Determine a target value based on the root mean square value of the frequency domain signal in the spectrum diagram in at least one frequency band; the frequency band corresponds to a modal feature of the tire.

[0061] Specifically, each frequency band corresponds to a modal characteristic of the tire, for example, the frequency bands are 50-100 Hz, 200-250 Hz, or 300-500 Hz. For vehicle tires (e.g., passenger car tires), there are three important modes within 500 Hz, each corresponding to a different natural frequency: the radial first-order mode below 100 Hz, the cavity mode between 200-250 Hz, and the tread depression mode between 300-500 Hz. Modal testing of tires can reveal the peak values ​​of these three important modes.

[0062] Based on the root mean square (RMS) value of the frequency domain signal in at least one frequency band in the spectrum diagram, a target value can be determined. The target value is the root mean square sum (RMSS) value, which serves as a numerical indicator for road surface differentiation.

[0063] It should be noted that different road surfaces excite tires differently, so the energy branches of the three important frequency bands are also different. The energy values ​​of these three important frequency bands (50-100Hz, 200-250Hz, and 300-500Hz) can be used to distinguish three different road conditions.

[0064] Step 104: Identify the road surface condition based on the target value.

[0065] Specifically, based on the target value, the road surface state can be identified; wherein the road surface state includes any one of the following: rough asphalt, cement road, and smooth asphalt.

[0066] The road surface condition identification method provided by the present invention collects signals from vehicle tires within a target time period, the signals including multiple target data; processes the signals based on each target data to obtain a frequency spectrum corresponding to the signals; determines a target value based on the root mean square value of the frequency domain signals in the frequency spectrum in at least one frequency band, wherein the frequency band corresponds to a modal characteristic of the tire; and identifies the road surface condition based on the target value, wherein the frequency band corresponds to a modal characteristic of the tire. By combining the root mean square value of the frequency spectrum corresponding to the vehicle tire signals within the target time period in at least one frequency band with the modal characteristics of the tire itself, efficient and rapid identification of the road surface condition within the target time period is achieved, thereby improving the speed and accuracy of road surface condition identification.

[0067] Optionally, a specific implementation of step 102 includes:

[0068] (1) Determine at least one peak position of each target data.

[0069] Specifically, based on each target data included in the signal, at least one peak position of each target data may be determined, wherein the peak position is a negative peak position.

[0070] (2) Splitting the signal into at least two signal segments based on the peak positions.

[0071] Specifically, based on the peak positions, the signal can be split into at least two segments. For example, if nine negative peak positions are found, recorded as P1, P2, P3, ..., P9, the signal is split into P1~P2, P2~P3, ..., P8~P9, a total of eight segments. Figure 4 As shown, Figure 4It is a schematic diagram of each segment of the split signal provided by the present invention.

[0072] (3) Based on the at least two signal segments, determine a frequency spectrum corresponding to the signal.

[0073] Specifically, based on at least two signal segments, a frequency spectrum corresponding to the signal can be further determined.

[0074] Optionally, the specific implementation of step (3) above includes:

[0075] For each segment of the signal, a target frequency domain signal corresponding to each segment of the signal is determined; based on the target frequency domain signals corresponding to all the segment signals, a frequency spectrum corresponding to the signal is determined.

[0076] Specifically, the target frequency domain signal can be the absolute value of the amplitude data sorted into the first 256. For each signal segment, the target frequency domain signal corresponding to each signal segment can be determined; then, based on the target frequency domain signals corresponding to all signal segments, the corresponding spectrum of the signal can be determined.

[0077] Optionally, determining the target frequency domain signal corresponding to each signal segment includes:

[0078] Windowing is performed on each segment of the signal to obtain a windowed signal; zero-padding is performed on the windowed signal; and Fourier transform is performed on the zero-padding signal to obtain a target frequency domain signal corresponding to each segment of the signal.

[0079] It should be noted that the peak energy caused by the tire contact area is very large, similar to a large pulse signal. Due to the shape and periodicity of the pulse, additional spectral components will be introduced on the basis of the spectrum of the original signal. These components are mainly concentrated in multiples of the pulse transmission frequency. Since the energy of tire contact and lift-off is large, this part of energy greatly interferes with the spectrum of the road surface itself and the road surface response. Therefore, it is necessary to eliminate the contact end and the lift-off end.

[0080] Specifically, each segment of the signal is windowed to obtain a windowed signal. For example, a Hanning window is added to each segment of the split signal. After adding the Hanning window, the signal at the negative peak position of each segment of the signal is set to zero, that is, the ground end portion is set to zero, such as Figure 5 As shown, Figure 5 Schematic diagram of the windowed signal provided by the present invention.

[0081] To ensure that the length of each signal segment after Fourier transform is the same, the windowed signal is padded with zeros, that is, zeros are added to the end of the data so that the length of each windowed signal segment is equal to 512. The zero-padded signal is then Fourier transformed to obtain the target frequency domain signal corresponding to each signal segment, that is, the absolute value of the amplitude data sorted into the first 256.

[0082] Optionally, determining a frequency spectrogram corresponding to the signal based on the target frequency domain signals corresponding to all the segment signals includes:

[0083] The target frequency domain signals corresponding to all segment signals are averaged to obtain a spectrum diagram corresponding to the signal.

[0084] Specifically, the target frequency domain signals corresponding to all segment signals are averaged to obtain a spectrum diagram corresponding to the signal.

[0085] In this application, by determining at least one peak position of each target data, the cause of the sudden change characteristics of the Z-axis acceleration signal inside the tire is analyzed, and then the interference of the sudden change signal on the spectral characteristics of the acceleration signal is analyzed, thereby improving the speed and accuracy of road condition recognition.

[0086] Optionally, a specific implementation of step 103 includes:

[0087] The target value is obtained by multiplying the root mean square values ​​of the frequency domain signals in the spectrum diagram in at least one frequency band.

[0088] Specifically, based on the frequency domain signal in the spectrogram, the RMS value in at least one frequency band is calculated. For example, the RMS values ​​in the 50-100 Hz, 200-250 Hz, and 300-500 Hz bands are calculated and recorded as RMS1, RMS2, and RMS3. The RMS values ​​in at least one frequency band are then multiplied together to obtain the target RMSS value.

[0089] In order to amplify the difference between the three road surfaces, the target value may be amplified, for example, by amplifying it 100 times.

[0090] Optionally, a specific implementation of step 104 includes:

[0091] When the target value is within the first range, the road surface condition is identified as rough asphalt; when the target value is within the second range, the road surface condition is identified as cement road; when the target value is within the third range, the road surface condition is identified as smooth asphalt.

[0092] Specifically, the first range is between 200-250, the second range is between 150-200, and the third range is between 50-100. The target value range is determined: if the target value is in the first range (200-250), the road surface condition is identified as rough asphalt; if the target value is in the second range (150-200), the road surface condition is identified as cement road; and if the target value is in the third range (50-100), the road surface condition is identified as smooth asphalt.

[0093] When a vehicle travels on different road surfaces, the target value (RMSS value) changes in real time. The road surface condition can be identified by collecting data within the target time period (for example, 1 second).

[0094] Figure 6 This is the second flow chart of the road surface state recognition method provided by the present invention, such as Figure 6 As shown, the method includes steps 601 to 608.

[0095] Step 601: Collect signals from vehicle tires within a target time period, where the signals include multiple target data.

[0096] Step 602: Determine at least one peak position of each target data; and split the signal into at least two signal segments based on each peak position.

[0097] Step 603: For each signal segment, perform windowing on each signal segment to obtain a windowed signal; perform zero padding on the windowed signal; perform Fourier transform on the zero-padded signal to obtain a target frequency domain signal corresponding to each signal segment.

[0098] Step 604: average the target frequency domain signals corresponding to all the segment signals to obtain a spectrum diagram corresponding to the signal.

[0099] Step 605: Multiply the root mean square values ​​of the frequency domain signals in the spectrum diagram in at least one frequency band to obtain a target value.

[0100] Step 606: When the target value is within the first range, identify the road surface condition as rough asphalt.

[0101] Step 607: When the target value is within the second range, identify the road surface condition as cement road.

[0102] Step 608: When the target value is within the third range, identify the road surface condition as smooth asphalt.

[0103] The road surface condition recognition method provided by the present invention has a fast response and can quickly identify different road surface conditions within a target time period (1 second). It is also highly efficient in operation and can be integrated into sensors for edge computing, further accelerating the response speed.

[0104] The road surface condition recognition device provided by the present invention is described below. The road surface condition recognition device described below and the road surface condition recognition method described above can be referenced to each other.

[0105] Figure 7 This is a schematic diagram of the structure of the road surface state recognition device provided by the present invention. Figure 7 As shown, the road surface state recognition device 700 includes: a collection module 701, a processing module 702, a determination module 703 and a recognition module 704; wherein,

[0106] An acquisition module 701 is configured to acquire signals of vehicle tires within a target time period, wherein the signals include a plurality of target data;

[0107] A processing module 702 is configured to process the signal based on each target data to obtain a frequency spectrum corresponding to the signal;

[0108] a determination module 703 for determining a target value based on a root mean square value of the frequency domain signal in the spectrum graph in at least one frequency band, wherein the frequency band corresponds to a modal feature of the tire;

[0109] The identification module 704 is configured to identify a road surface condition based on the target value.

[0110] The road surface condition identification device provided by the present invention collects signals from vehicle tires within a target time period, the signals including multiple target data; processes the signals based on each target data to obtain a frequency spectrum corresponding to the signals; determines a target value based on the root mean square value of the frequency domain signals in the frequency spectrum in at least one frequency band, wherein the frequency band corresponds to a modal characteristic of the tire; and identifies the road surface condition based on the target value, wherein the frequency band corresponds to a modal characteristic of the tire. By combining the root mean square value of the frequency spectrum corresponding to the vehicle tire signals within the target time period in at least one frequency band with the modal characteristics of the tire itself, efficient and rapid identification of the road surface condition within the target time period is achieved, thereby improving the speed and accuracy of road surface condition identification.

[0111] Optionally, the determining module 703 is specifically configured to:

[0112] The target value is obtained by multiplying the root mean square values ​​of the frequency domain signals in the spectrum diagram in at least one frequency band.

[0113] Optionally, the identification module 704 is specifically configured to:

[0114] When the target value is within the first range, identifying the road surface condition as rough asphalt;

[0115] When the target value is within the second range, identifying the road surface state as a cement road;

[0116] When the target value is within the third range, the road surface condition is identified as smooth asphalt.

[0117] Optionally, the processing module 702 is specifically configured to:

[0118] determining at least one peak position of each of the target data;

[0119] Splitting the signal into at least two signal segments based on the peak positions;

[0120] Based on the at least two signal segments, a frequency spectrum corresponding to the signal is determined.

[0121] Optionally, the processing module 702 is further configured to:

[0122] For each signal segment, determining a target frequency domain signal corresponding to each signal segment;

[0123] Based on the target frequency domain signals corresponding to all the segment signals, a frequency spectrogram corresponding to the signals is determined.

[0124] Optionally, the processing module 702 is further configured to:

[0125] Performing windowing on each segment of the signal to obtain a windowed signal;

[0126] Performing zero padding on the windowed signal;

[0127] Performing Fourier transform on the zero-padded signal to obtain a target frequency domain signal corresponding to each segment of the signal.

[0128] Optionally, the processing module 702 is further configured to:

[0129] The target frequency domain signals corresponding to all segment signals are averaged to obtain a spectrum diagram corresponding to the signal.

[0130] Figure 8 This is a schematic diagram of the physical structure of the electronic device provided by the present invention, such as Figure 8As shown, the electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communications bus 840, wherein the processor 810, the communications interface 820, and the memory 830 communicate with each other via the communications bus 840. The processor 810 may invoke logic instructions in the memory 830 to execute a road surface condition identification method, which includes: collecting signals from a vehicle tire within a target time period, the signals including multiple target data; processing the signals based on each target data to obtain a frequency spectrum corresponding to the signals; determining a target value based on the root mean square value of the frequency domain signal in at least one frequency band in the frequency spectrum; the frequency band corresponding to a modal characteristic of the tire; and identifying the road surface condition based on the target value.

[0131] Furthermore, the logic instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0132] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the road surface condition identification method provided by the above-mentioned methods, the method comprising: collecting signals of vehicle tires within a target time period, the signals comprising multiple target data; processing the signals based on each of the target data to obtain a spectrum graph corresponding to the signals; determining a target value based on the root mean square value of the frequency domain signal in the spectrum graph in at least one frequency band; the frequency band corresponds to a modal feature of the tire; and identifying the road surface condition based on the target value.

[0133] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0134] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A road surface condition recognition method, characterized in that: include: The acceleration chip sensor collects signals from the vehicle tire within a target time period, wherein the signals include multiple target data; the target data is the acceleration inside the tire in the z direction; Based on each of the target data, the signal is processed to obtain a frequency spectrum corresponding to the signal; determining a target value based on a root mean square value of a frequency domain signal in the spectrum graph in at least one frequency band, wherein the frequency band corresponds to a modal feature of the tire; The target value is the root mean square product value; Based on the target value, identifying a road surface condition; the road surface condition includes any one of the following: rough asphalt, cement road, and smooth asphalt; The determining of the target value based on the root mean square value of the frequency domain signal in the spectrum diagram in at least one frequency band includes: Multiplying the root mean square values ​​of the frequency domain signals in the spectrum graph in at least one frequency band to obtain the target value; The processing of the signal based on each target data to obtain a frequency spectrum corresponding to the signal includes: determining at least one peak position of each of the target data; Splitting the signal into at least two signal segments based on the peak positions; For each signal segment, windowing is performed on the signal segment to obtain a windowed signal; Performing zero padding on the windowed signal; Performing Fourier transform on the zero-padded signal to obtain a target frequency domain signal corresponding to each segment of the signal; The target frequency domain signals corresponding to all segment signals are averaged to obtain a spectrum diagram corresponding to the signal.

2. The road surface state recognition method according to claim 1, characterized in that: The identifying of the road surface condition based on the target value includes: When the target value is within the first range, identifying the road surface condition as rough asphalt; When the target value is within the second range, identifying the road surface state as a cement road; When the target value is within the third range, the road surface condition is identified as smooth asphalt.

3. A road surface state recognition device, characterized in that: include: An acquisition module is used to acquire signals from a vehicle tire within a target time period through an acceleration chip sensor, wherein the signals include a plurality of target data; the target data is the acceleration inside the tire in the z direction; A processing module, configured to process the signal based on each target data to obtain a frequency spectrum corresponding to the signal; a determination module, configured to determine a target value based on a root mean square value of a frequency domain signal in the spectrum graph in at least one frequency band, wherein the frequency band corresponds to a modal feature of the tire; The target value is the root mean square product value; an identification module, configured to identify a road surface condition based on the target value; the road surface condition including any one of the following: rough asphalt, cement road, and smooth asphalt; The determining module is specifically configured to: Multiplying the root mean square values ​​of the frequency domain signals in the spectrum graph in at least one frequency band to obtain the target value; The processing module is specifically used to: determining at least one peak position of each of the target data; Splitting the signal into at least two signal segments based on the peak positions; For each signal segment, windowing is performed on the signal segment to obtain a windowed signal; Performing zero padding on the windowed signal; Performing Fourier transform on the zero-padded signal to obtain a target frequency domain signal corresponding to each segment of the signal; The target frequency domain signals corresponding to all segment signals are averaged to obtain a spectrum diagram corresponding to the signal.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the road surface condition identification method according to claim 1 or 2 is implemented.

5. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the road surface condition recognition method according to claim 1 or 2 is implemented.

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