An asphalt pavement integrated detection method and system

Abstract

The application provides an asphalt pavement integrated detection method and system, relates to the technical field of detection, and can capture the real performance of the pavement under dynamic load in real time by collecting data during normal driving of a vehicle on the asphalt pavement to be detected, combining the running data of the vehicle and the multidirectional acceleration data of the detection terminal, can directly obtain the dynamic friction coefficient estimation value of the pavement through fusion calculation of the first data set and the second data set, and the value can reflect the actual friction state of the tire and the pavement when the vehicle is driving more than the traditional static detection method, and can evaluate the pavement with a larger length.

Description

Technical Field

[0001] The present application relates to the field of detection technology, and in particular to an integrated detection method and system for asphalt pavement. Background Art

[0002] Asphalt pavement is one of the most important pavement structures in highway engineering. Its construction quality and performance are directly related to road safety, comfort, and service life. Traditional asphalt pavement inspection methods rely on manual inspections, sampling and coring, or specialized testing equipment (such as drop weight deflectometers and laser roughness meters). These methods suffer from low inspection efficiency, high costs, limited coverage, and difficulty in reflecting the dynamic performance of the pavement in real time.

[0003] Traditional road skid resistance testing (such as the pendulum instrument method and the structural depth method) is mostly static point measurement. It is impossible to continuously obtain the road friction coefficient when the vehicle is in normal driving state, and it is difficult to reflect the road's true anti-skid ability under dynamic loads. Insufficient friction coefficient can easily lead to safety hazards such as skidding in rainy days and extended braking distance. At the same time, it is impossible to measure long road surfaces. Summary of the Invention

[0004] The embodiments of the present application provide an integrated asphalt pavement detection method and system to improve the above-mentioned problems.

[0005] To achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, the present application proposes an integrated asphalt pavement detection method. The method is applicable to an integrated asphalt pavement detection system. The system includes a processing terminal and a detection terminal. The detection terminal is provided on a vehicle. The method is applicable to the processing terminal and includes:

[0007] The processing terminal obtains a first data set based on the vehicle, the first data set being data of the vehicle when it is normally traveling on the asphalt road to be tested, including vehicle braking intensity data, vehicle output power data, and vehicle driving speed data;

[0008] The processing terminal obtains a second data set based on the detection terminal. The second data set is multi-directional acceleration data obtained by the detection terminal. The second data set includes vehicle lateral acceleration data. The lateral acceleration data includes acceleration direction and acceleration magnitude.

[0009] The processing terminal aligns the data in the first data group and the data in the second data group based on the time axis, and obtains an estimated dynamic friction coefficient of the asphalt road surface to be tested based on the first data group and the second data group;

[0010] The processing terminal determines whether the asphalt pavement is a qualified pavement based on the dynamic friction coefficient estimation.

[0011] With reference to the first aspect, optionally, the processing terminal aligns the data in the first data set and the data in the second data set based on a time axis, and obtains an estimated value of the dynamic friction coefficient of the asphalt pavement to be detected based on the first data set and the second data set, including:

[0012] The processing terminal obtains, based on the braking intensity data, the vehicle output power data, and the vehicle speed data, an average theoretical acceleration value corresponding to each of the target time windows, wherein the target time window is any time period within the time period during which the vehicle travels on the asphalt pavement to be detected, and the length of the target time window is much smaller than the length of the time period during which the vehicle travels on the asphalt pavement to be detected.

[0013] The processing terminal obtains the estimated value of the dynamic friction coefficient based on the plurality of vehicle lateral acceleration data and the plurality of theoretical acceleration values.

[0014] With reference to the first aspect, optionally, the processing terminal obtains, based on the braking intensity data, the vehicle output power data, and the vehicle speed data, an average theoretical acceleration value corresponding to each of the target time windows, including:

[0015] The processing terminal obtains, for each of the target time windows, average vehicle braking intensity data, average vehicle output power data, and average vehicle speed data.

[0016] The processing terminal determines, based on the plurality of average vehicle braking intensity data, the plurality of average vehicle output power data, and the plurality of average vehicle speed data, a plurality of average theoretical acceleration values corresponding to the plurality of target time windows.

[0017] With reference to the first aspect, optionally, the processing terminal determines, based on the plurality of average vehicle braking intensity data, the plurality of average vehicle output power data, and the plurality of average vehicle speed data, a plurality of average theoretical acceleration values corresponding to the plurality of target time windows, including:

[0018] The processing terminal obtains vehicle state data, including vehicle weight data.

[0019] The processing terminal obtains a neural network model, and inputs the vehicle weight data, the average vehicle braking intensity data, the average vehicle output power data, and the average vehicle speed data into the neural network model, wherein the neural network model is configured to output a predicted average theoretical acceleration value based on the input vehicle weight data, the average vehicle braking intensity data, the average vehicle output power data, and the average vehicle speed data.

[0020] With reference to the first aspect, optionally, the processing terminal determines, based on the estimated value of the dynamic friction coefficient, whether the asphalt pavement is a qualified pavement, including:

[0021] The processing terminal obtains dynamic friction coefficient estimates corresponding to multiple target time windows, and obtains an average dynamic friction coefficient estimate based on the dynamic friction coefficient estimates corresponding to the multiple target time windows;

[0022] The processing terminal compares the average dynamic friction coefficient estimate with the preset friction coefficient estimate, and determines whether the asphalt pavement is a qualified pavement based on the comparison result.

[0023] In combination with the first aspect, optionally, the processing terminal determines whether the asphalt pavement is a qualified pavement based on the dynamic friction coefficient estimation, including:

[0024] The processing terminal obtains an average vibration reference value, and compares the average vibration reference value with a preset vibration reference value, and determines whether the asphalt pavement is a qualified pavement based on the comparison result.

[0025] In combination with the first aspect, optionally, the processing terminal obtains an average vibration reference value, compares the average reference value with a preset vibration reference value, and determines whether the asphalt pavement is a qualified pavement based on the comparison result, including:

[0026] The processing terminal obtains a plurality of longitudinal acceleration data corresponding to the target time window based on the second data group;

[0027] The processing terminal determines a vibration intensity reference value corresponding to the target time window based on the multiple longitudinal acceleration data;

[0028] The processing terminal determines an average shock reference value based on the plurality of shock intensity reference values.

[0029] In a second aspect, the present application proposes an integrated asphalt pavement detection system, which includes a processing terminal and a detection terminal. The detection terminal is disposed on a vehicle, and the system is configured as follows:

[0030] The processing terminal obtains a first data set based on the vehicle, the first data set being data of the vehicle when it is normally traveling on the asphalt road to be tested, including vehicle braking intensity data, vehicle output power data, and vehicle driving speed data;

[0031] The processing terminal obtains a second data set based on the detection terminal. The second data set is multi-directional acceleration data obtained by the detection terminal. The second data set includes vehicle lateral acceleration data. The lateral acceleration data includes acceleration direction and acceleration magnitude.

[0032] The processing terminal aligns the data in the first data group and the data in the second data group based on the time axis, and obtains an estimated dynamic friction coefficient of the asphalt road surface to be tested based on the first data group and the second data group;

[0033] The processing terminal determines whether the asphalt pavement is a qualified pavement based on the dynamic friction coefficient estimation.

[0034] In conjunction with the second aspect, optionally, the system is configured to:

[0035] The processing terminal aligns the data in the first data group and the second data group based on the time axis, and obtains an estimated dynamic friction coefficient of the asphalt road surface to be tested based on the first data group and the second data group, including:

[0036] The processing terminal obtains the corresponding average theoretical acceleration values ​​within multiple target time windows based on the braking intensity data, the vehicle output power data, and the vehicle driving speed data. The target time window is any time period within the time period when the vehicle is driving on the asphalt road to be tested. The time length of the target time window is much shorter than the time length corresponding to the time period when the vehicle is driving on the asphalt road to be tested.

[0037] The processing terminal obtains an estimated dynamic friction coefficient based on the plurality of vehicle lateral acceleration data and the plurality of theoretical acceleration values.

[0038] In conjunction with the second aspect, optionally, the system is configured to:

[0039] The processing terminal obtains the corresponding average theoretical acceleration values ​​within multiple target time windows based on the braking intensity data, vehicle output power data, and vehicle speed data, including:

[0040] The processing terminal obtains the corresponding average vehicle braking intensity data, average vehicle output power data, and average vehicle driving speed data within the target time window;

[0041] The processing terminal determines a plurality of average theoretical acceleration values ​​corresponding to a plurality of target time windows based on a plurality of average vehicle braking intensity data, a plurality of average vehicle output power data, and a plurality of average vehicle driving speed data.

[0042] In conjunction with the second aspect, optionally, the system is configured to:

[0043] The processing terminal determines a plurality of average theoretical acceleration values ​​corresponding to a plurality of target time windows based on a plurality of average vehicle braking intensity data, a plurality of average vehicle output power data, and a plurality of average vehicle driving speed data, including:

[0044] The processing terminal obtains vehicle status data, the vehicle status data including vehicle weight data;

[0045] The processing terminal obtains a neural network model and inputs vehicle weight data, average vehicle braking intensity data, average vehicle output power data and average vehicle driving speed data into the neural network model. The neural network model is used to output a predicted average theoretical acceleration value based on the input vehicle weight data, average vehicle braking intensity data, average vehicle output power data and average vehicle driving speed data.

[0046] In conjunction with the second aspect, optionally, the system is configured to:

[0047] The processing terminal determines whether the asphalt pavement is qualified based on the dynamic friction coefficient estimation, including:

[0048] The processing terminal obtains dynamic friction coefficient estimates corresponding to multiple target time windows, and obtains an average dynamic friction coefficient estimate based on the dynamic friction coefficient estimates corresponding to the multiple target time windows;

[0049] The processing terminal compares the average dynamic friction coefficient estimate with the preset friction coefficient estimate, and determines whether the asphalt pavement is a qualified pavement based on the comparison result.

[0050] In conjunction with the second aspect, optionally, the system is configured to:

[0051] The processing terminal determines whether the asphalt pavement is qualified based on the dynamic friction coefficient estimation, including:

[0052] The processing terminal obtains an average vibration reference value, and compares the average vibration reference value with a preset vibration reference value, and determines whether the asphalt pavement is a qualified pavement based on the comparison result.

[0053] In conjunction with the second aspect, optionally, the system is configured to:

[0054] The processing terminal obtains the average vibration reference value and compares the average reference value with the preset vibration reference value. Based on the comparison result, it is determined whether the asphalt pavement is a qualified pavement, including:

[0055] The processing terminal obtains a plurality of longitudinal acceleration data corresponding to the target time window based on the second data group;

[0056] The processing terminal determines a vibration intensity reference value corresponding to the target time window based on the multiple longitudinal acceleration data;

[0057] The processing terminal determines an average shock reference value based on the plurality of shock intensity reference values.

[0058] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including:

[0059] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method proposed in the first aspect of the embodiment of the present invention.

[0060] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect of the embodiment of the present invention.

[0061] In summary, the above method and system have the following technical effects:

[0062] The embodiments of the present application propose an integrated asphalt pavement detection method and system. By collecting data while a vehicle is normally driving on the asphalt pavement to be tested, and combining the vehicle's own operating data with the multi-directional acceleration data of the detection terminal, the true performance of the pavement under dynamic loads can be captured in real time. By fusion calculation of the first data group and the second data group, an estimated dynamic friction coefficient of the pavement can be directly obtained. Compared with traditional static detection methods, this value can better reflect the actual friction state between the tire and the pavement when the vehicle is driving, and can also evaluate longer lengths of pavement. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of an integrated asphalt pavement detection method proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0065] The embodiment of this application proposes an integrated detection method for asphalt pavement, which is applicable to an integrated detection system for asphalt pavement. The system includes a processing terminal and a detection terminal, and the detection terminal is set on a vehicle. The method proposed in this application is applicable to the processing terminal, please refer to Figure 1 , the method comprises the following steps:

[0066] S101: The processing terminal obtains a first data group based on the vehicle. The first data group is data of the vehicle when it is driving normally on the asphalt road to be tested, including vehicle braking intensity data, vehicle output power data and vehicle driving speed data.

[0067] The processing terminal can connect to the vehicle computer via an ODB (On-Board Diagnostics) interface, enabling access to real-time vehicle driving information. In this embodiment, the first data set is driving information, including but not limited to vehicle braking intensity data, vehicle output power data, and vehicle speed data. This driving information, combined with relevant vehicle parameters such as drag coefficient and weight, can be used to determine the theoretical acceleration value of the vehicle at any given moment. For example, the actual wheel output power can be derived from the engine's output speed and torque, and combined with the current braking force to determine the corresponding acceleration value, i.e., the "ideal lateral acceleration" calculated based on the vehicle dynamics model (or geometric relationships). This calculation typically relies on vehicle parameters (such as wheelbase and track width) and driver input (vehicle speed). Assuming a sufficiently high road friction coefficient (i.e., no tire slip), this is the theoretical lateral acceleration that the vehicle "should achieve." The specific calculation method is disclosed in relevant technical documents and is not limited here.

[0068] S102: The processing terminal obtains a second data set based on the detection terminal. The second data set is multi-directional acceleration data obtained by the detection terminal. The second data set includes vehicle lateral acceleration data. The lateral acceleration data includes acceleration direction and acceleration magnitude.

[0069] The detection terminal can be an IMU six-axis sensor, that is, an inertial measurement unit (Inertial Measurement Unit) six-axis sensor. The IMU six-axis sensor is a sensor module that integrates a three-axis accelerometer and a three-axis gyroscope. It can measure the linear acceleration of an object in three-dimensional space and the angular velocity of rotation around three axes. The actual lateral acceleration is collected in real time by the acceleration sensor installed on the vehicle body. It directly reflects the acceleration generated by the actual lateral "grip" that can be provided between the tire and the ground under the current road conditions. Of course, the detection terminal can also be some other sensor components that can obtain vehicle posture information, which is not limited in this application.

[0070] S103: The processing terminal aligns the data in the first data group and the second data group based on the time axis, and obtains an estimated dynamic friction coefficient of the asphalt road surface to be tested based on the first data group and the second data group.

[0071] It will be appreciated that in this embodiment, the processing terminal aligns the data in the first and second data groups based on the time axis, that is, aligns the data in the first and second data groups at corresponding moments. Since multiple data are instantaneous, in this embodiment, the corresponding average theoretical acceleration values ​​within multiple target time windows can be obtained based on the braking intensity data, vehicle output power data, and vehicle speed data. The target time window is any time period within the time period of the vehicle traveling on the asphalt road to be tested, wherein the time length of the target time window is significantly shorter than the time length corresponding to the time period of the vehicle traveling on the asphalt road to be tested. It should be noted that if the target time window is sufficiently small, the instantaneous data within that time window can be used as the average data corresponding to that time window.

[0072] It can be understood that the maximum value of the dynamic frictional lateral force obtained by the processing terminal based on multiple vehicle lateral acceleration data and multiple theoretical acceleration values ​​is limited by the road friction coefficient. For example,

[0073] Fy=μFz

[0074] Where Fy is the tire lateral force, and μ is the estimated dynamic friction coefficient. According to vehicle dynamics, the lateral force provided by the tire is the core reason for the vehicle's lateral acceleration, while the dynamic friction coefficient, Fz, is the normal load on the tire, which is approximately equal to the distribution of the vehicle's vertical weight. The relationship between lateral acceleration and lateral force is:

[0075] ay=mFy

[0076] Where ay is the lateral acceleration and m is the vehicle mass.

[0077] It can be seen from the combined results that the lateral acceleration is directly related to the dynamic friction coefficient. That is, under the same vehicle state (m and Fz are fixed), the greater the actual lateral acceleration, the higher the current road friction coefficient.

[0078] Of course, other calculation methods may be used in other embodiments, which are not limited in this embodiment.

[0079] Therefore, in this embodiment, the corresponding average vehicle braking intensity data, average vehicle output power data and average vehicle driving speed data within the target time window can be obtained, and based on multiple average vehicle braking intensity data, multiple average vehicle output power data and multiple average vehicle driving speed data, multiple average theoretical acceleration values ​​corresponding to multiple target time windows can be determined.

[0080] Optionally, in this embodiment, a neural network model can be used to process multiple data. For example, the processing terminal acquires vehicle status data, including vehicle weight data. The processing terminal acquires a neural network model and inputs the vehicle weight data, average vehicle braking intensity data, average vehicle output power data, and average vehicle speed data into the neural network model. The neural network model is configured to output a predicted average theoretical acceleration value based on the input vehicle weight data, average vehicle braking intensity data, average vehicle output power data, and average vehicle speed data.

[0081] S104: The processing terminal determines whether the asphalt pavement is a qualified pavement based on the dynamic friction coefficient estimation.

[0082] As will be appreciated, after obtaining multiple dynamic friction coefficient estimates corresponding to multiple time windows, an average dynamic friction coefficient estimate can be obtained based on the dynamic friction coefficient estimates corresponding to multiple target time windows. The average dynamic friction coefficient estimate is then compared with the preset friction coefficient estimate, and the asphalt pavement's compliance is determined based on the comparison results.

[0083] For example, when the average dynamic friction coefficient estimate is less than a preset friction coefficient estimate, indicating that the road is prone to slipping, the road can be determined to be unqualified. Of course, the road surface can also be divided into multiple different grades based on the gradient of the average dynamic friction coefficient estimate.

[0084] It is not enough to judge whether the road surface is qualified only by referring to the friction condition of the road surface. Therefore, in this application, the judgment can also be made in combination with the degree of unevenness of the road surface.

[0085] Therefore, the processing terminal can obtain the average vibration reference value, and compare the average vibration reference value with the preset vibration reference value, and determine whether the asphalt pavement is a qualified pavement based on the comparison result.

[0086] Exemplarily, the processing terminal obtains multiple longitudinal acceleration data corresponding to the target time window based on the second data group, determines the vibration intensity reference value corresponding to the target time window based on the multiple longitudinal acceleration data, and determines the average vibration reference value based on the multiple vibration intensity reference values.

[0087] As an implementation method, the maximum positive value and the minimum negative value (absolute value) of the longitudinal acceleration within the window may be taken and their average value may be calculated to represent the maximum vibration level within the window;

[0088] As can be understood, in this embodiment, road bumps and undulations cause changes in longitudinal acceleration, so longitudinal acceleration data can reflect road vibration. For example, if the detection section is divided into five target time windows, and the vibration intensity reference values ​​are 0.6, 0.7, 0.8, 0.7, and 0.6, respectively, the average vibration reference value is (0.6 + 0.7 + 0.8 + 0.7 + 0.6) / 5 = 0.68.

[0089] An integrated asphalt pavement detection method proposed in an embodiment of the present application collects data while a vehicle is normally driving on the asphalt pavement to be tested, and combines the vehicle's own operating data with the multi-directional acceleration data of the detection terminal to capture the actual performance of the pavement under dynamic loads in real time. Through the fusion calculation of the first data group and the second data group, an estimated dynamic friction coefficient of the pavement can be directly obtained. Compared with traditional static detection methods, this value can better reflect the actual friction state between the tire and the pavement when the vehicle is driving, and can also evaluate a longer length of pavement.

[0090] Based on the same inventive concept, this application proposes an integrated asphalt pavement detection system. The system includes a processing terminal and a detection terminal. The detection terminal is set on a vehicle. The system is configured as follows:

[0091] The processing terminal obtains a first data set based on the vehicle, the first data set being data of the vehicle when it is normally traveling on the asphalt road to be tested, including vehicle braking intensity data, vehicle output power data, and vehicle driving speed data;

[0092] The processing terminal obtains a second data set based on the detection terminal. The second data set is multi-directional acceleration data obtained by the detection terminal. The second data set includes vehicle lateral acceleration data. The lateral acceleration data includes acceleration direction and acceleration magnitude.

[0093] The processing terminal aligns the data in the first data group and the data in the second data group based on the time axis, and obtains an estimated dynamic friction coefficient of the asphalt road surface to be tested based on the first data group and the second data group;

[0094] The processing terminal determines whether the asphalt pavement is a qualified pavement based on the dynamic friction coefficient estimation.

[0095] Optionally, the system is configured to:

[0096] The processing terminal aligns the data in the first data group and the second data group based on the time axis, and obtains an estimated dynamic friction coefficient of the asphalt road surface to be tested based on the first data group and the second data group, including:

[0097] The processing terminal obtains the corresponding average theoretical acceleration values ​​within multiple target time windows based on the braking intensity data, the vehicle output power data, and the vehicle driving speed data. The target time window is any time period within the time period when the vehicle is driving on the asphalt road to be tested. The time length of the target time window is much shorter than the time length corresponding to the time period when the vehicle is driving on the asphalt road to be tested.

[0098] The processing terminal obtains an estimated dynamic friction coefficient based on the plurality of vehicle lateral acceleration data and the plurality of theoretical acceleration values.

[0099] Optionally, the system is configured to:

[0100] The processing terminal obtains the corresponding average theoretical acceleration values ​​within multiple target time windows based on the braking intensity data, vehicle output power data, and vehicle speed data, including:

[0101] The processing terminal obtains the corresponding average vehicle braking intensity data, average vehicle output power data, and average vehicle driving speed data within the target time window;

[0102] The processing terminal determines a plurality of average theoretical acceleration values ​​corresponding to a plurality of target time windows based on a plurality of average vehicle braking intensity data, a plurality of average vehicle output power data, and a plurality of average vehicle driving speed data.

[0103] Optionally, the system is configured to:

[0104] The processing terminal determines a plurality of average theoretical acceleration values ​​corresponding to a plurality of target time windows based on a plurality of average vehicle braking intensity data, a plurality of average vehicle output power data, and a plurality of average vehicle driving speed data, including:

[0105] The processing terminal obtains vehicle status data, the vehicle status data including vehicle weight data;

[0106] The processing terminal obtains a neural network model and inputs vehicle weight data, average vehicle braking intensity data, average vehicle output power data and average vehicle driving speed data into the neural network model. The neural network model is used to output a predicted average theoretical acceleration value based on the input vehicle weight data, average vehicle braking intensity data, average vehicle output power data and average vehicle driving speed data.

[0107] Optionally, the system is configured to:

[0108] The processing terminal determines whether the asphalt pavement is qualified based on the dynamic friction coefficient estimation, including:

[0109] The processing terminal obtains dynamic friction coefficient estimates corresponding to multiple target time windows, and obtains an average dynamic friction coefficient estimate based on the dynamic friction coefficient estimates corresponding to the multiple target time windows;

[0110] The processing terminal compares the average dynamic friction coefficient estimate with the preset friction coefficient estimate, and determines whether the asphalt pavement is a qualified pavement based on the comparison result.

[0111] Optionally, the system is configured to:

[0112] The processing terminal determines whether the asphalt pavement is qualified based on the dynamic friction coefficient estimation, including:

[0113] The processing terminal obtains an average vibration reference value, and compares the average vibration reference value with a preset vibration reference value, and determines whether the asphalt pavement is a qualified pavement based on the comparison result.

[0114] Optionally, the system is configured to:

[0115] The processing terminal obtains the average vibration reference value and compares the average reference value with the preset vibration reference value. Based on the comparison result, it is determined whether the asphalt pavement is a qualified pavement, including:

[0116] The processing terminal obtains a plurality of longitudinal acceleration data corresponding to the target time window based on the second data group;

[0117] The processing terminal determines a vibration intensity reference value corresponding to the target time window based on the multiple longitudinal acceleration data;

[0118] The processing terminal determines an average shock reference value based on the plurality of shock intensity reference values.

[0119] An integrated asphalt pavement detection system proposed in an embodiment of the present application collects data while a vehicle is normally driving on the asphalt pavement to be tested, and combines the vehicle's own operating data with the multi-directional acceleration data of the detection terminal to capture the actual performance of the pavement under dynamic loads in real time. Through the fusion calculation of the first data group and the second data group, an estimated dynamic friction coefficient of the pavement can be directly obtained. Compared with traditional static detection methods, this value can better reflect the actual friction state between the tire and the pavement when the vehicle is driving, and can also evaluate a longer length of pavement.

[0120] Based on the same inventive concept, an embodiment of the present application further provides an electronic device, the electronic device comprising:

[0121] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the asphalt pavement integrated detection method of an embodiment of the present application.

[0122] In addition, to achieve the above object, the embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the integrated detection method of the asphalt pavement.

[0123] The various components of the electronic device will be described in detail as follows:

[0124] The processor is the control center of the electronic device, and can be one processor or a plurality of processing elements. For example, the processor is one or more central processing units (CPU), or is an application specific integrated circuit (ASIC), or is one or more integrated circuits configured to implement the embodiments of the present application, such as one or more microprocessors (digital signal processor, DSP), or one or more field programmable gate arrays (field programmable gate array, FPGA).

[0125] Optionally, the processor can execute various functions of the electronic device by running or executing the software program stored in the memory and calling the data stored in the memory.

[0126] The memory is used to store the software program for implementing the scheme of the present application, and is controlled by the processor to execute. The specific implementation manner can refer to the above method embodiments, and will not be described here.

[0127] Optionally, the memory can be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM), or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage or other magnetic storage devices, or any other medium capable of storing desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to this. The memory can be integrated with the processor or exist independently and be coupled to the processor through the interface circuit of the electronic device, and the embodiments of the present application do not make a specific limitation in this regard.

[0128] The transceiver is configured to communicate with the network device or the terminal device.

[0129] Optionally, the transceiver can include a receiver and a transmitter. The receiver is configured to implement the receiving function, and the transmitter is configured to implement the transmitting function.

[0130] Optionally, the transceiver can be integrated with the processor or exist independently and be coupled to the processor through the interface circuit of the router, and the embodiments of the present application do not make a specific limitation in this regard.

[0131] In addition, the technical effects of the electronic device can refer to the technical effects of the data transmission method of the above-mentioned method embodiments, and will not be repeated here.

[0132] It should be understood that the processor in the embodiments of the present application can be a central processing unit (CPU), and the processor can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), ready programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0133] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0134] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0135] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0136] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0137] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0138] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

Claims

1. An integrated asphalt pavement detection method, characterized in that: The method adopts an integrated asphalt pavement detection system, which includes a processing terminal and a detection terminal. The detection terminal is set on a vehicle. The method is applicable to the processing terminal and includes: The processing terminal obtains a first data set based on the vehicle, wherein the first data set is data of the vehicle when it is normally traveling on the asphalt road to be tested, including vehicle braking intensity data, vehicle output power data, and vehicle driving speed data; The processing terminal obtains a second data group based on the detection terminal, the second data group being multi-directional acceleration data obtained by the detection terminal, the second data group including vehicle lateral acceleration data, the lateral acceleration data including acceleration direction and acceleration magnitude; The processing terminal aligns the data in the first data group and the data in the second data group based on a time axis, and obtains an estimated dynamic friction coefficient of the asphalt road surface to be tested based on the first data group and the second data group; The processing terminal determines whether the asphalt road surface is a qualified road surface based on the dynamic friction coefficient estimation.

2. The asphalt pavement integrated detection method according to claim 1, characterized in that: The processing terminal aligns the data in the first data group and the second data group based on a time axis, and obtains an estimated dynamic friction coefficient of the asphalt road surface to be tested based on the first data group and the second data group, including: The processing terminal obtains corresponding average theoretical acceleration values ​​within a plurality of target time windows based on the braking intensity data, the vehicle output power data, and the vehicle driving speed data, wherein the target time window is any time period within the time period when the vehicle is driving on the asphalt road surface to be tested, wherein the time length of the target time window is much shorter than the time length corresponding to the time period when the vehicle is driving on the asphalt road surface to be tested; The processing terminal obtains the dynamic friction coefficient estimation based on the plurality of vehicle lateral acceleration data and the plurality of theoretical acceleration values.

3. The asphalt pavement integrated detection method according to claim 2, characterized in that: The processing terminal obtains corresponding average theoretical acceleration values ​​within a plurality of target time windows based on the braking intensity data, the vehicle output power data, and the vehicle travel speed data, including: The processing terminal obtains the corresponding average vehicle braking intensity data, average vehicle output power data and average vehicle driving speed data within the target time window; The processing terminal determines a plurality of the average theoretical acceleration values ​​corresponding to a plurality of the target time windows based on a plurality of the average vehicle braking intensity data, a plurality of the average vehicle output power data, and a plurality of the average vehicle driving speed data.

4. The asphalt pavement integrated detection method according to claim 3, characterized in that: The processing terminal determines, based on the plurality of the average vehicle braking intensity data, the plurality of the average vehicle output power data, and the plurality of the average vehicle driving speed data, the plurality of the average theoretical acceleration values ​​corresponding to the plurality of the target time windows, including: The processing terminal acquires vehicle status data, wherein the vehicle status data includes vehicle weight data; The processing terminal obtains a neural network model and inputs the vehicle weight data, the average vehicle braking intensity data, the average vehicle output power data and the average vehicle driving speed data into the neural network model. The neural network model is used to output the predicted average theoretical acceleration value based on the input vehicle weight data, the average vehicle braking intensity data, the average vehicle output power data and the average vehicle driving speed data.

5. The asphalt pavement integrated detection method according to claim 3, characterized in that: The processing terminal determines whether the asphalt pavement is a qualified pavement based on the dynamic friction coefficient estimation, including: The processing terminal obtains the dynamic friction coefficient estimates corresponding to the multiple target time windows, and obtains an average dynamic friction coefficient estimate based on the dynamic friction coefficient estimates corresponding to the multiple target time windows; The processing terminal compares the average dynamic friction coefficient estimate with a preset friction coefficient estimate, and determines whether the asphalt pavement is a qualified pavement based on the comparison result.

6. The asphalt pavement integrated detection method according to claim 3, characterized in that: The processing terminal determines whether the asphalt pavement is a qualified pavement based on the dynamic friction coefficient estimation, including: The processing terminal obtains an average vibration reference value, compares the average vibration reference value with a preset vibration reference value, and determines whether the asphalt pavement is a qualified pavement based on the comparison result.

7. The asphalt pavement integrated detection method according to claim 6, characterized in that: The processing terminal obtains an average vibration reference value, compares the average vibration reference value with a preset vibration reference value, and determines whether the asphalt pavement is a qualified pavement based on the comparison result, including: The processing terminal acquires a plurality of longitudinal acceleration data corresponding to the target time window based on the second data group; The processing terminal determines a vibration intensity reference value corresponding to the target time window based on the plurality of longitudinal acceleration data; The processing terminal determines the average vibration reference value based on a plurality of the vibration intensity reference values.

8. An integrated asphalt pavement detection system, characterized in that: The system includes a processing terminal and a detection terminal, wherein the detection terminal is provided on a vehicle, and the system is configured to: The processing terminal obtains a first data set based on the vehicle, wherein the first data set is data of the vehicle when it is normally traveling on the asphalt road to be tested, including vehicle braking intensity data, vehicle output power data, and vehicle driving speed data; The processing terminal obtains a second data group based on the detection terminal, the second data group being multi-directional acceleration data obtained by the detection terminal, the second data group including vehicle lateral acceleration data, the lateral acceleration data including acceleration direction and acceleration magnitude; The processing terminal aligns the data in the first data group and the data in the second data group based on a time axis, and obtains an estimated dynamic friction coefficient of the asphalt road surface to be tested based on the first data group and the second data group; The processing terminal determines whether the asphalt road surface is a qualified road surface based on the dynamic friction coefficient estimation.

9. An electronic device, characterized in that: include: at least one processor; And, a memory communicatively connected to at least one processor; wherein the memory stores instructions executable by at least one processor, and the instructions are executed by at least one processor to enable the at least one processor to execute the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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