Road disease detection method based on deformation coefficient

Abstract

The invention discloses a road disease detection method based on a deformation coefficient, and the method comprises the following steps: S1, deploying sensor nodes carried by steering engine holders at the two sides of a road at equal intervals, and combining the nodes into all possible quadrilateral structures; s2, the lengths of two diagonal lines and the angles of four corners of a quadrangle are obtained through rotation of a steering engine holder and distance measurement of a sensor; s3, calculating the side length, the diagonal line length and the angle of each quadrangle; s4, sorting the selectivity scores of all the quadrangles in a descending order; s5, acquiring distance data and angle data among the nodes in real time by the sensors forming the reference quadrangle; and S6, comparing the deformation coefficient calculated in real time with the reference quadrangle. According to the invention, on the basis of the distance between the sensors and the angles between the four sensors, the diagonal and angle information of the quadrangle is obtained, and by comparing the deformation coefficients before and after the road disease, the consumption of computing resources is reduced, and the efficient detection of the road disease is realized.

Description

Technical Field

[0001] This invention relates to the field of highway inspection technology, and in particular to a method for detecting highway defects based on the deformation coefficient. Background Technology

[0002] Road condition inspection is a crucial aspect of ensuring traffic safety. Road surface defects such as cracks, potholes, ruts, and peeling, if not detected in time, can lead to structural damage to highways and cause traffic accidents. Currently, scholars and engineers both domestically and internationally often employ various non-destructive inspection technologies to identify and assess road surface defects, including vision-based image processing methods, LiDAR 3D scanning, and ground-penetrating radar (GPR) detection.

[0003] Image processing technology based on high-definition cameras uses area scanning or line scanning cameras to acquire road surface images, and combines them with machine learning or deep learning models, such as U-Net and YOLO series models, to segment cracks and potholes. It has the advantages of low cost and high resolution. However, this type of method is easily affected by ambient light, rain and snow, and obstructions. Moreover, the effect is significantly reduced under low light conditions such as at night or in tunnels. The workload of image preprocessing and calibration is large.

[0004] LiDAR 3D scanning can generate high-precision point clouds, acquire road surface geometry information in real time, and support operations at night and in complex lighting conditions; its high degree of automation facilitates large-scale rapid inspections. However, LiDAR equipment is expensive, point cloud data processing is complex, and it has high requirements for computing resources and storage. In contrast, ground-penetrating radar (GPR) uses an antenna to emit electromagnetic waves to detect the subsurface structure of asphalt, and can identify stripping, voids, and subgrade defects without damaging the road surface. Its disadvantages are that operation requires professional training, the detection results are easily affected by moisture content and material dielectric constant, and it cannot directly determine the components of the defect, but can only indicate abnormal areas. It is evident that existing technologies still have many shortcomings. Summary of the Invention

[0005] The purpose of this invention is to provide a highway defect detection method based on deformation coefficient. Based on the distance between sensors and the angle between four sensors, the diagonal and angle information of a quadrilateral are obtained. By comparing the deformation coefficients before and after highway defects, the consumption of computing resources is reduced, and efficient detection of highway defects is achieved.

[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution: A method for detecting highway defects based on deformation coefficient, characterized by comprising the following steps: S1, sensor nodes mounted on servo gimbals are deployed at fixed intervals on both sides of the highway, and the nodes are combined to form all possible quadrilateral structures. S2, represent each quadrilateral as a coordinate in three-dimensional space, and calculate the value of the three-dimensional coordinates; S3, calculate the product of the three-dimensional coordinates of each quadrilateral to obtain the selective score for each quadrilateral; S4: Sort all quadrilaterals by their selective scores in descending order, and use the quadrilateral with the highest score as the detection benchmark quadrilateral. S5, the sensors that form the reference quadrilateral acquire the distance and angle data between each node in real time and build a KD tree to store the data for each time period for fast retrieval and feature differentiation, and obtain the deformation coefficient through real-time calculation; S6 compares the real-time calculated deformation coefficient with the reference quadrilateral, stores the detection data of each sequence in the KD tree, and plots the deformation amplitude-measurement sequence change graph. If a trend of change in deformation amplitude is detected at a certain time point, it indicates that the highway has begun to deform; thus completing the highway defect detection.

[0007] Furthermore: In S1, the fixed distance between the deployed sensor nodes is 200cm. Through the ultrasonic transmitter module of the sensor, each node can measure the distance between itself and obtain distance data between the sensor nodes. Within the measuring range of 3cm-400cm, all sensors can collectively form... Form quadrilaterals and calculate the total number. The method is as follows: in, This indicates the total number of sensors deployed.

[0008] Furthermore: In S2, each quadrilateral formed is represented by coordinates in three-dimensional space, denoted as... The index of the quadrilateral is the number of the quadrilateral it forms among all the quadrilaterals it makes up; the indexing starts from 1 and continues to the next level. For the first The x-coordinates of the quadrilaterals; For the first The ordinates of the quadrilaterals; For the first The ordinates of the quadrilaterals; For the first Find the coordinates of a quadrilateral in three-dimensional space; calculate the x-coordinate. and ordinate and vertical coordinates The value; No. The x-coordinates of the quadrilaterals Defined as the dot product of the shorter diagonal vectors of a quadrilateral, with the ordinate as... The vertical axis is defined as the ratio of the sum of the two longer sides to the sum of the two shorter sides of a quadrilateral, and the vertical axis is defined as the tangent of the smallest angle of the quadrilateral. Therefore, calculate the first The x-coordinate of the quadrilateral and ordinate and vertical coordinates The method is as follows: in, and Represents the vectors of the two diagonals of a quadrilateral; It represents the sum of the two longer sides of a quadrilateral. This represents the sum of the two shorter sides of a quadrilateral. It represents the smallest angle of a quadrilateral.

[0009] Furthermore: In S3, the first The selectivity score of the quadrilateral is defined as the product of its three-dimensional coordinates. The calculation of the... The method for multiplying the three-dimensional coordinates of a quadrilateral is as follows: in, Indicates the first Selective scores for quadrilaterals, Indicates the first The x-coordinate of a quadrilateral, Indicates the first The ordinate of a quadrilateral, Indicates the first The vertical coordinates of a quadrilateral.

[0010] Furthermore: In S4, the selective scores of all quadrilaterals are sorted in descending order to obtain an ordered list, and the quadrilateral with the highest score is selected as the detection benchmark quadrilateral.

[0011] Furthermore: In S5, the three sensor nodes that constitute the reference quadrilateral are respectively represented as sensor nodes. Sensor nodes Sensor nodes Sensor nodes ; The real-time distance data acquired by all sensors constituting the reference quadrilateral includes: sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance and the four corners of the quadrilateral , , , ; When the highway is free from defects, the deformation coefficient of the reference quadrilateral is expressed as: The method for calculating the deformation coefficient is as follows: in, This represents the length of the longer diagonal of the baseline quadrilateral when the highway is free of defects. This represents the shorter diagonal length of the baseline quadrilateral when the highway is free of defects, α represents the minimum angle of the baseline quadrilateral when the highway is free of defects, and β represents the diagonal angle of the minimum angle of the baseline quadrilateral when the highway is free of defects. A KD tree is constructed with this set of data as the root node. The method for calculating the deformation coefficient after highway damage occurs is as follows: in, This represents the length of the longer diagonal of the baseline quadrilateral after road damage occurs. This represents the shorter diagonal length of the baseline quadrilateral after road damage occurs. This represents the minimum angle of the reference quadrilateral after highway damage occurs. This represents the minimum angle of the baseline quadrilateral after the highway has suffered damage. At the same time, the data from this detection is stored in a KD tree for quick retrieval and feature differentiation.

[0012] Furthermore: In S6, the deformation coefficient will be calculated in real time. Deformation coefficient before highway distress occurs The comparison is conducted using the following method: in, Indicates the magnitude of deformation. The deformation coefficient before any road damage occurs is represented by F, which represents the deformation coefficient before any deformation occurs. The detection data of each sequence is stored in a KD tree, and a deformation amplitude-measurement sequence change graph is plotted. When a trend of change in deformation amplitude is detected at a certain time point, it indicates that the road has begun to deform, and the road damage detection is completed.

[0013] Furthermore, the specific information of the sensor is as follows: The sensor model is HC-SR04, which can provide a distance measurement function of 3cm-400cm. The HC-SR04 ultrasonic sensor includes four modules: ultrasonic transmitter, receiver, two-dimensional pan-tilt unit and control circuit. The ultrasonic transmitter module is used to emit ultrasonic pulses after receiving the trigger signal from the control circuit module, forming a sound wave detection source so that the sound waves can propagate along both sides of the road within the detection range and reach other sensor nodes. The receiver module is used to capture echo signals in real time, improving the efficiency of highway defect detection. The 2D gimbal module is used to calculate the rotation angle. It calculates the angle of a certain corner of a quadrilateral by rotating the angles of two layers of 2D gimbals. The control circuit module is used to convert distance and angle values ​​into digital values, upload them to the central processing unit via bus or wireless communication unit, calculate the horizontal and vertical coordinates of the quadrilaterals in two-dimensional space, and calculate the selective score of each quadrilateral.

[0014] In summary, the present invention has the following beneficial effects: Firstly, this invention constructs a reference quadrilateral structure by combining the spatial layout information of sensor nodes, compares the deformation coefficients before and after highway defects, and achieves efficient detection of highway defects. Based on the distance between sensors, it obtains the diagonal and angle information of the quadrilateral, thereby quickly selecting the reference quadrilateral, reducing the consumption of computing resources and meeting the strict requirements of highway defect detection on resource consumption. Secondly, the sensor node composed of the HC-SR04 ultrasonic ranging sensor and the pan-tilt unit used in this invention has the advantages of flexible deployment, fast response and low cost, making it suitable for large-scale deployment and significantly reducing the overall system deployment cost. In addition, the sensor modules have a high degree of standardization and can flexibly adapt to the detection needs of different road sections and environmental conditions, providing a good hardware foundation for highway defect detection methods. Thirdly, this invention can intuitively provide the measurement sequence of when road deformation begins. Using the measurement sequence of when deformation begins as an index, the corresponding node in the KD tree can be quickly found. Through the data stored in the node, quadrilaterals at each moment can be quickly retrieved and their features can be distinguished. This can meet the needs of high-frequency measurements in some scenarios and reduce the retrieval time from linear complexity to logarithmic level, greatly reducing retrieval time. Feature distinction can quickly query nearby data when the disease occurs, providing efficient data support for disease evolution trend analysis and cause diagnosis, and reducing redundant calculations. Fourth, this invention uses the diagonal and angle of a detection benchmark quadrilateral to detect road defects. When slight deformations occur on the road that are difficult to observe with the naked eye, the data returned by the sensor will change. Because it involves the calculation of multiple angles and lengths, even slight data changes can lead to significant changes in the deformation coefficient. This allows the invention to sensitively respond to road deformations and predict road defects in advance. Attached Figure Description

[0015] Figure 1 This is a flowchart of the detection method of the present invention; Figure 2 This is a diagram showing all possible scenarios in which the five sensor nodes of this invention form quadrilaterals with other nodes within the ranging range; Figure 3 This is a schematic diagram of the quadrilateral based on the present invention before the occurrence of highway defects; Figure 4 This is a schematic diagram of the quadrilateral based on the present invention after highway damage occurs; Figure 5 This is a graph showing the change range of the measurement sequence after the reference quadrilateral has been determined, as plotted according to the present invention. Figure 6 This is a schematic diagram of the HC-SR04 sensor of the present invention. Detailed Implementation

[0016] The present invention will be further described in detail below with reference to the accompanying drawings.

[0017] In the description of this invention, it should be understood that the terms "upper", "lower", "left", "right", "front", "rear", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0018] Example, refer to Figure 1 This includes the following steps: S1, sensor nodes mounted on servo gimbals are deployed every 200cm on both sides of the road, and the nodes are combined to form all possible quadrilateral structures. Through the ultrasonic transmitter module of the sensor, the nodes can measure distances to each other, acquiring distance data between sensor nodes. Within the distance measurement range of 3cm-400cm, all sensors can form a total of Form quadrilaterals and calculate the total number. The method is as follows: in, Indicates the total number of sensors deployed; Select sensor model HC-SR04 ( Figure 6 The HC-SR04 ultrasonic sensor, which provides a ranging function from 3cm to 400cm, consists of four modules: an ultrasonic transmitter, a receiver, a two-dimensional pan-tilt unit, and a control circuit. The ultrasonic transmitter module is used to emit ultrasonic pulses after receiving the trigger signal from the control circuit module, forming a sound wave detection source so that the sound waves can propagate along both sides of the road within the detection range and reach other sensor nodes. The receiver module is used to capture echo signals in real time, improving the efficiency of highway defect detection. The 2D gimbal module is used to calculate the rotation angle. It calculates the angle of a certain corner of a quadrilateral by rotating the angles of two layers of 2D gimbals. The control circuit module is used to convert distance and angle values ​​into digital values, upload them to the central processing unit via bus or wireless communication unit, calculate the horizontal and vertical coordinates of the quadrilateral in two-dimensional space, and calculate the selective score of each quadrilateral. S2 represents each quadrilateral as a coordinate system in three-dimensional space, and denotes it as... The index of the quadrilateral is the number of the quadrilateral it forms among all the quadrilaterals it makes up; the indexing starts from 1 and continues to the next level. For the first The x-coordinates of the quadrilaterals; For the first The ordinates of the quadrilaterals; For the first The ordinates of the quadrilaterals are calculated; For the first Find the coordinates of a quadrilateral in three-dimensional space; calculate the x-coordinate. and ordinate and vertical coordinates The value; No. The x-coordinates of the quadrilaterals Defined as the dot product of the shorter diagonal vectors of a quadrilateral, with the ordinate as... The vertical axis is defined as the ratio of the sum of the two longer sides to the sum of the two shorter sides of a quadrilateral, and the vertical axis is defined as the tangent of the smallest angle of the quadrilateral. Therefore, calculate the first The x-coordinate of the quadrilateral and ordinate and vertical coordinates The method is as follows: in, and Represents the vectors of the two diagonals of a quadrilateral; It represents the sum of the two longer sides of a quadrilateral. This represents the sum of the two shorter sides of a quadrilateral. It represents the smallest angle of a quadrilateral.

[0019] S3, calculate the product of the three-dimensional coordinates of each quadrilateral to obtain the selectivity score for each quadrilateral. The selective score of a quadrilateral is defined as the product of its three-dimensional coordinates; Calculate the first The method for multiplying the three-dimensional coordinates of a quadrilateral is as follows: in, Indicates the first Selective scores for quadrilaterals, Indicates the first The x-coordinate of a quadrilateral, Indicates the first The ordinate of a quadrilateral, Indicates the first The vertical coordinates of the quadrilateral; S4: Sort all quadrilaterals in descending order of their selective scores to obtain an ordered list. Select the quadrilateral with the highest score as the detection benchmark quadrilateral. S5, the sensors that form the reference quadrilateral acquire the distance and angle data between each node in real time and build a KD tree to store the data for each time period for fast retrieval and feature differentiation, and obtain the deformation coefficient through real-time calculation; The three sensor nodes that make up the reference quadrilateral are respectively represented as sensor nodes. Sensor nodes Sensor nodes Sensor nodes ; The real-time distance data acquired by all sensors constituting the reference quadrilateral includes: sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance and the four corners of the quadrilateral ; When the highway is free from defects, the deformation coefficient of the reference quadrilateral is expressed as: The method for calculating the deformation coefficient is as follows: in, This represents the length of the longer diagonal of the baseline quadrilateral when the highway is free of defects. This represents the shorter diagonal length of the baseline quadrilateral when the highway is free of defects, α represents the minimum angle of the baseline quadrilateral when the highway is free of defects, and β represents the diagonal angle of the minimum angle of the baseline quadrilateral when the highway is free of defects. A KD tree is constructed with this set of data as the root node. The method for calculating the deformation coefficient after highway damage occurs is as follows: in, This represents the length of the longer diagonal of the baseline quadrilateral after road damage occurs. This represents the shorter diagonal length of the baseline quadrilateral after road damage occurs. This represents the minimum angle of the reference quadrilateral after highway damage occurs. This represents the minimum angle of the baseline quadrilateral after the highway has suffered damage. At the same time, the data from this detection is stored in a KD tree for quick retrieval and feature differentiation. S6 compares the real-time calculated deformation coefficient with the reference quadrilateral, and then compares the real-time calculated deformation coefficient with the reference quadrilateral. Deformation coefficient before highway distress occurs The comparison is conducted using the following method: in, Indicates the magnitude of deformation. represents the deformation coefficient when no road distress has occurred, and F represents the deformation coefficient when no deformation has occurred; The detection data for each sequence is stored in a KD tree, and a deformation amplitude-measurement sequence change graph is plotted. If a trend of change in deformation amplitude is detected at a certain time point, it indicates that the highway has begun to deform; thus, the highway defect detection is completed.

[0020] Setting up experiments Two sensor nodes are deployed on one side of the road, and three sensor nodes are deployed on the other side, for a total of five sensors. The distance between adjacent sensor nodes is 200cm, forming four quadrilateral structures (e.g., Figure 2 (As shown).

[0021] S1, The method for calculating the total number of quadrilaterals is as follows: S2 represents each quadrilateral as a coordinate system in three-dimensional space, and calculates the x, y, and y coordinates. ,in Indicates the first The dot product of the vectors of the adjacent diagonals of the shorter diagonal of a quadrilateral. Indicates the first The ratio of the longer diagonal to the shorter diagonal of a quadrilateral. Indicates the first The tangent of the smallest angle of a quadrilateral; The method for calculating the sum and ratio of the dot products of five quadrilaterals is as follows: in, This represents the sum of the lengths of the two longer sides of a quadrilateral. This represents the sum of the lengths of the two shorter sides of a quadrilateral. and Let represent the vectors of the two diagonals of the quadrilateral. The angle representing the smallest angle of a quadrilateral. Represents the angle opposite the smallest angle of a quadrilateral; The final coordinates of the five quadrilaterals in three-dimensional space are as follows: .

[0022] S3, calculate the selectivity score for each quadrilateral, which is the product of the x and y coordinates: Indicates the first The selection score of each quadrilateral, and ; S4: Sort all quadrilaterals by their selective scores in descending order. By comparing these scores, a sorted list is obtained by descending order. Choose the first quadrilateral. As a benchmark quadrilateral ( Figure 2 (a) Blue quadrilateral); S5, the four sensor nodes that form the reference quadrilateral are respectively represented as sensor nodes. Sensor nodes Sensor nodes Sensor nodes The corner data collected in real time by the four sensors includes: sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance The four corners of the quadrilateral formed by the sensors , , , When no road defects occur ( Figure 3 The method for calculating the deformation coefficient of the reference quadrilateral is as follows: in, This represents the deformation coefficient when no road defects have occurred. Represents the longer diagonal of the reference quadrilateral. The shorter diagonal of the reference quadrilateral. It represents the smallest angle of the reference quadrilateral. It represents the angle opposite the smallest angle of the reference quadrilateral.

[0023] After highway defects occur ( Figure 4 The corner data acquired in real time by all sensors constituting the reference quadrilateral includes: sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance The four angles ∠ of the quadrilateral formed by these elements ,∠ ,∠ ,∠ At this point, the method for calculating the deformation coefficient of the reference quadrilateral is as follows: in, This represents the deformation coefficient calculated in real time. Represents the longer diagonal of the reference quadrilateral. The shorter diagonal of the reference quadrilateral. Represents the smallest angle of the reference quadrilateral. Represents the angle opposite the smallest angle of the reference quadrilateral; S6 will calculate the deformation coefficient in real time. Deformation coefficient before highway distress occurs The comparison is conducted using the following method: in, Indicates the magnitude of deformation. The deformation coefficient before highway distress occurs is represented by F, and the deformation coefficient before deformation occurs is represented by the deformation amplitude-measurement sequence variation graph. Figure 5 ), observe the trend of deformation amplitude changes, determine whether the highway has deformed; complete the highway defect detection.

[0024] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make inventive modifications to this embodiment as needed, but as long as they are within the scope of the claims of the present invention, they are protected by patent law.

Claims

1. A method for detecting highway defects based on deformation coefficient, characterized in that, Includes the following steps: S1, sensor nodes mounted on servo gimbals are deployed at fixed intervals on both sides of the highway, and the nodes are combined to form all possible quadrilateral structures. S2, represent each quadrilateral as a coordinate in three-dimensional space, and calculate the value of the three-dimensional coordinates; S3, calculate the product of the three-dimensional coordinates of each quadrilateral to obtain the selective score for each quadrilateral; S4: Sort all quadrilaterals by their selective scores in descending order, and use the quadrilateral with the highest score as the detection benchmark quadrilateral. S5, the sensors that form the reference quadrilateral acquire the distance and angle data between each node in real time and build a KD tree to store the data for each time period for fast retrieval and feature differentiation, and obtain the deformation coefficient through real-time calculation; S6 compares the real-time calculated deformation coefficient with the reference quadrilateral, stores the detection data of each sequence in the KD tree, and plots the deformation amplitude-measurement sequence change graph. If a trend of change in deformation amplitude is detected at a certain time point, it indicates that the highway has begun to deform; thus completing the highway defect detection.

2. The method for detecting highway defects based on deformation coefficient according to claim 1, characterized in that: In S1, the fixed distance between the deployed sensor nodes is 200cm. Through the ultrasonic transmitter module of the sensor, each node can measure the distance between itself and obtain distance data between the sensor nodes. Within the measuring range of 3cm-400cm, all sensors can form a total... Form quadrilaterals and calculate the total number. The method is as follows: in, This indicates the total number of sensors deployed.

3. The method for detecting highway defects based on deformation coefficient according to claim 1, characterized in that: In S2, each quadrilateral formed is represented by coordinates in three-dimensional space, denoted as... The index of the quadrilateral is the number of the quadrilateral it forms among all the quadrilaterals it makes up; the indexing starts from 1 and continues to the next level. For the first The x-coordinates of the quadrilaterals; For the first The ordinates of the quadrilaterals; For the first The ordinates of the quadrilaterals; For the first Find the coordinates of a quadrilateral in three-dimensional space; calculate the x-coordinate. and ordinate and vertical coordinates The value; No. The x-coordinates of the quadrilaterals Defined as the dot product of the shorter diagonal vectors of a quadrilateral, with the ordinate as... The vertical axis is defined as the ratio of the sum of the two longer sides to the sum of the two shorter sides of a quadrilateral, and the vertical axis is defined as the tangent of the smallest angle of the quadrilateral. Therefore, calculate the first The x-coordinate of the quadrilateral and ordinate and vertical coordinates The method is as follows: in, and Represents the vectors of the two diagonals of a quadrilateral; It represents the sum of the two longer sides of a quadrilateral. This represents the sum of the two shorter sides of a quadrilateral. It represents the smallest angle of a quadrilateral.

4. The method for detecting highway defects based on deformation coefficient according to claim 1, characterized in that: In S3, the first The selectivity score of the quadrilateral is defined as the product of its three-dimensional coordinates. The calculation of the... The method for multiplying the three-dimensional coordinates of a quadrilateral is as follows: in, Indicates the first Selective scores for quadrilaterals, Indicates the first The x-coordinate of a quadrilateral, Indicates the first The ordinate of a quadrilateral, Indicates the first The vertical coordinates of a quadrilateral.

5. The method for detecting highway defects based on deformation coefficient according to claim 1, characterized in that: In S4, the selective scores of all quadrilaterals are sorted in descending order to obtain an ordered list, and the quadrilateral with the highest score is selected as the detection benchmark quadrilateral.

6. The method for detecting highway defects based on deformation coefficient according to claim 1, characterized in that: In S5, the three sensor nodes that form the reference quadrilateral are respectively represented as sensor nodes. Sensor nodes Sensor nodes Sensor nodes ; The real-time distance data acquired by all sensors constituting the reference quadrilateral includes: sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance Sensor nodes To sensor node distance and the four corners of the quadrilateral , , , ; When the highway is free from defects, the deformation coefficient of the reference quadrilateral is expressed as: The method for calculating the deformation coefficient is as follows: in, This represents the length of the longer diagonal of the baseline quadrilateral when the highway is free of defects. This represents the shorter diagonal length of the baseline quadrilateral when the highway is free of defects, α represents the minimum angle of the baseline quadrilateral when the highway is free of defects, and β represents the diagonal angle of the minimum angle of the baseline quadrilateral when the highway is free of defects. A KD tree is constructed with this set of data as the root node. The method for calculating the deformation coefficient after highway damage occurs is as follows: in, This represents the length of the longer diagonal of the baseline quadrilateral after road damage occurs. This represents the shorter diagonal length of the baseline quadrilateral after road damage occurs. This represents the minimum angle of the reference quadrilateral after highway damage occurs. This represents the minimum angle of the baseline quadrilateral after the highway has suffered damage. At the same time, the data from this detection is stored in a KD tree for quick retrieval and feature differentiation.

7. The method for detecting highway defects based on deformation coefficient according to claim 1, characterized in that: In S6, the deformation coefficient is calculated in real time. Deformation coefficient before highway distress occurs The comparison is conducted using the following method: in, Indicates the magnitude of deformation. The deformation coefficient before any road damage occurs is represented by F, which represents the deformation coefficient before any deformation occurs. The detection data of each sequence is stored in a KD tree, and a deformation amplitude-measurement sequence change graph is plotted. When a trend of change in deformation amplitude is detected at a certain time point, it indicates that the road has begun to deform, and the road damage detection is completed.

8. The method for detecting highway defects based on deformation coefficient according to claim 2, characterized in that, The specific information about the sensor is as follows: The sensor model is HC-SR04, which can provide a distance measurement function of 3cm-400cm. The HC-SR04 ultrasonic sensor includes four modules: ultrasonic transmitter, receiver, two-dimensional pan-tilt unit and control circuit. The ultrasonic transmitter module is used to emit ultrasonic pulses after receiving the trigger signal from the control circuit module, forming a sound wave detection source so that the sound waves can propagate along both sides of the road within the detection range and reach other sensor nodes. The receiver module is used to capture echo signals in real time, improving the efficiency of highway defect detection. The 2D gimbal module is used to calculate the rotation angle. It calculates the angle of a certain corner of a quadrilateral by rotating the angles of two layers of 2D gimbals. The control circuit module is used to convert distance and angle values ​​into digital values, upload them to the central processing unit via bus or wireless communication unit, calculate the horizontal and vertical coordinates of the quadrilaterals in two-dimensional space, and calculate the selective score of each quadrilateral.

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