An intelligent detection system and method for a road-use flying matter detection device

Abstract

The application discloses a kind of road flying matter detection device intelligent detection system and method, it is related to road traffic detection field, including unmanned aerial vehicle linkage structure, gyroscope module, fixed rod piece, laser sensing device, data processing module, control module, detection data matching transmission sensing device.The application reduces detection cost by using unmanned aerial vehicle combined with laser radar system, avoids road bumps, traffic restrictions and other error data, ensures the scientificity, authenticity, effectiveness of data, on the basis of being able to accurately detect a large amount of road morphology, improves detection rate and accuracy, and can continuously detect, real-time change and repair detection route according to operator demand, reduces the staffing and delay of information transmission of staff, and safe, easy to operate, little impact on road traffic, convenient maintenance and installation, convenient to carry out work, wide range of application.

Description

Technical Field

[0001] This invention relates to the field of road traffic inspection, and in particular to an intelligent inspection system and method for road-use aerial quality inspection devices. Background Technology

[0002] With the increasing total traffic volume in my country in recent years, the proportion of highways reaching their service life has also increased annually, and a large number of highways are entering their maintenance cycle. Asphalt pavement, as the main paving form for expressways and first-class highways, is facing serious road defects, mainly rutting and subsidence, under the increasing traffic volume, which severely affect the traffic safety of travelers. In order to improve the comfort of driving and riding and reduce the impact of road defects, road design and testing agencies need to conduct regular road maintenance designs.

[0003] In the initial stages of road design and inspection, road inspection devices have become essential equipment for understanding the severity and extent of road damage. Currently, the most widely used are road inspection vehicles. These vehicles, operating smoothly, integrate photoelectric equipment, 3S technology, and digital modules using modern information technology to automatically map road surface morphology, internal voids and cracks, etc. The collected data is then processed to analyze key data such as road geometry, smoothness, and the extent of road damage. Through intelligent calculation of the imaged road surface topography, indices such as road smoothness (road travel quality index) and road damage condition (road condition index) are derived, providing effective and accurate data support for scientific road maintenance and traffic flow optimization.

[0004] While road inspection vehicles offer some convenience for modern intelligent maintenance and inspection, they still have some shortcomings and deficiencies in practical engineering applications. The main reason is that these vehicles have very high requirements for the road surface itself. Road inspection vehicles are greatly affected by road conditions and surface conditions. Obstacles such as mud, water, or garbage on the road surface can cause deviations in the inspection data. In addition, during the inspection process, the road inspection vehicle must travel in a specific lane, and the inspection process cannot be too bumpy, otherwise, the inspection data will be too inaccurate. Road inspection vehicles also require the road to be as closed as possible during inspections; excessive traffic flow will affect inspection efficiency. Due to the high requirements of road inspection vehicles, the inspection frequency and continuity are greatly limited.

[0005] Because road inspection vehicles are equipped with specialized equipment, traditional vehicle mechanics cannot provide comprehensive maintenance and repair services effectively. This leads to increased equipment maintenance costs and a shortage of skilled personnel, further hindering the widespread use of road inspection equipment. While traditional road inspection personnel are proficient in road inspection, they lack expertise in vehicle driving, equipment debugging, and basic maintenance. Therefore, a road inspection vehicle typically requires both a driver and a specialized inspection personnel. However, the coordination between the two inevitably results in delays and errors, making it difficult to achieve the expected inspection results. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention aims to provide an intelligent inspection system and method for road-based aerial quality inspection devices. It utilizes a drone device and a gyroscope module to efficiently, safely, and accurately perform laser scanning of the road surface morphology within a specific range, and a data processing module generates road defect investigation images. While reducing costs, it rapidly and effectively collects data, overcoming the shortcomings of traditional road inspection methods, such as low efficiency, discontinuity, and poor safety, which fail to meet production demands. This improves the efficiency and accuracy of road inspection, enabling continuous operation, high safety, and operation by a single professional.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] A smart inspection system for a flight quality inspection device for road use includes: a UAV linkage structure, a gyroscope module, an imaging rod, a laser sensing device, a data processing module, a control module, and a detection data matching and transmission sensing device; the UAV linkage structure includes a main UAV, a secondary UAV, and landing gear; the laser sensing device includes a laser ranging sensor mounted on the main UAV and a lidar component mounted on the imaging rod; the UAV linkage structure and the imaging rod constitute the flight quality inspection device.

[0009] The main UAV is equipped with a laser rangefinder, a signal receiver / transmitter, and an emergency automatic intelligent return-to-home device. The laser rangefinder consists of two pairs of laser rangefinders forming a transmission group. The transmission group includes one group with a fixed vertical flight direction and one group with a periodically changing direction. The laser beams emitted by the transmission group transmit the collected data to the data processing module.

[0010] The imaging rod is connected to the UAV linkage structure via a ball joint and a steering gear set. The gyroscope module is mounted on the ball joint and the steering gear set. The ball joint supports the UAV linkage structure and controls the direction of the imaging rod.

[0011] The lidar assembly is disposed on the lower surface of the imaging rod and includes a vertically arranged laser emitter, a laser rangefinder, and a data transmission sensor. The laser emitter and the laser rangefinder form multiple pairs of periodically transversely scanning laser emission ranging groups and longitudinally scanning spiral transversely oscillating laser emission ranging groups. The detection data matching and transmission sensing device includes a laser detection device one disposed on the lidar assembly, a spatial position detection device on the gyroscope module, and a laser detection device two disposed on the laser ranging sensing device. The laser detection device one and the laser detection device two form a laser detection group that is offset from the laser emission ranging groups.

[0012] The data processing module includes a data collection mechanism, a data analysis mechanism, and a data feedback mechanism; the data processing module is communicatively connected to the laser sensing device and the detection data matching and transmission sensing device, and the laser sensing device, the data collection mechanism, the data analysis mechanism, and the data feedback mechanism are connected in sequence.

[0013] Furthermore, the two lidar components are respectively positioned at 1 / 4 and 3 / 4 of the imaging rod.

[0014] Furthermore, the control module includes a remote wireless data receiving device, a command transmitting device, and a remote control PC terminal.

[0015] Furthermore, the data analysis mechanism includes a PC and a data preprocessing module, a road damage analysis module, a road surface smoothness analysis module, a road rutting analysis module, an area feature extraction algorithm module, and an algorithm processing module mounted on the PC; the data feedback mechanism includes a control terminal display device; the data collection mechanism includes a laser receiver M and a laser receiver N; the input end of the laser receiver is connected to the data transmission device on the laser sensing device and the laser detection device, respectively, and the output end of the laser receiver is connected to the PC via a network port.

[0016] Furthermore, the data preprocessing module is used to convert the laser feedback data collected from the laser sensing device and the distance signal from the road surface to the laser rangefinder into digital signals, transmit their spatial information and feature information, match and compare them with the data from the laser detection device, and remove duplicate data and data with large deviations.

[0017] The road damage analysis module is used to calculate the road damage status and the real-time road damage rate, to statistically analyze the damage situation of each road section, to accumulate the relative areas of cracks, block cracks, transverse cracks, vertical cracks, ruts, subsidence, bulges, potholes, loosening, and repair defects, and to transmit the data to the algorithm processing module and to provide timely feedback to the control module.

[0018] The road surface smoothness analysis module is used to calculate the road surface smoothness condition, statistically analyze the smoothness index, analyze the road surface driving quality index, and transmit it to the algorithm processing module.

[0019] The road rutting analysis module is used to calculate rutting depth. After multiple laser cross-sectional scans, the morphological features of the road cross section are drawn. The height of each spatial location on the road can be analyzed. Through analysis and comparison, the rutting depth is determined, and the data is transmitted to the algorithm processing module to analyze the road rutting depth index.

[0020] The area feature extraction algorithm module is used to record the damage features detected by laser sensing devices at different angles when passing through a certain cross section, until the damage disappears at a certain cross section. The recorded damage features are then converted into digital information and transmitted to the road damage analysis module and the algorithm analysis module.

[0021] The algorithm processing module is used to remove duplicate data and data with large deviations. After sorting the data, it converts the actual road surface condition into digital information and transmits it to the road surface technical condition processing software. This software calculates the road surface condition under different maintenance history conditions and feeds the data back to highway maintenance units at all levels for further processing.

[0022] This invention also provides an intelligent detection method for a road-based flight quality inspection device, comprising the following steps:

[0023] S0: Overall flight quality inspection device in operation;

[0024] S1: The laser sensing system of the UAV linkage structure is activated;

[0025] S2: Data collection;

[0026] S3: Data Analysis.

[0027] Furthermore, the operation of the S0 overall flight quality inspection device specifically includes the following steps:

[0028] S001: The UAV linkage structure receives signals from the control module via an electromagnetic signal receiver to perform remote operations;

[0029] S002: The drone transmits real-time footage captured by a high-definition camera to the inspection personnel.

[0030] S003: The inspector sends a control command. The UAV controls the ball joint and steering gear through the control component to change the height and horizontal angle of the imaging rod. If the set height and horizontal angle are met, proceed to S004. If not, proceed to S002.

[0031] S004: The horizontal scanning component inside the lidar assembly starts to control the optical component to rotate in the horizontal direction within a preset angle range and transmits the imaging information back.

[0032] S005: The lidar component will transmit data to the inspection personnel's PC in real time for data integration to obtain a road topography map;

[0033] S006: The inspection personnel control the lidar controller and the UAV control components in real time based on the road topography map.

[0034] Furthermore, the laser sensing system of the UAV linkage structure described in S1 is activated, specifically including the following steps:

[0035] S101: The drone linkage structure flies along a prescribed route according to instructions;

[0036] S102: Collect data from the two transmitting groups of the laser rangefinder sensor on the UAV;

[0037] S103: Based on the data obtained in S102, calculate the distance and relative speed between the front vehicle and the rear vehicle and the flight device during low-altitude flight, and calculate the relative spatial position of the flight device.

[0038] S104: Determine whether the preceding and following vehicles will affect the subsequent low-altitude reconnaissance. If so, proceed to S105; otherwise, proceed to S107.

[0039] S105: Determine whether the speed of the flight quality inspection device needs to be changed without affecting the inspection route. If yes, proceed to S106; otherwise, proceed to S107.

[0040] S106: Analyze the speed of the vehicles in front and behind to adjust the speed of the flight device, ensure that the space clearance of the flight quality inspection device meets the requirements, and then execute S108.

[0041] S107: The spatial position and speed of the current vehicle and the following vehicle exceed the upper limit of the rated operating speed, affecting the safety of the flight quality inspection device. Adjust the spatial altitude of the flight quality inspection device, reduce the speed, and then execute S108.

[0042] S108: Records the spatial position of the flight quality inspection device in real time and feeds it back to the control module, which then issues adjustment commands in a timely manner.

[0043] Furthermore, the S2 data collection specifically includes the following steps:

[0044] S201: Receives data from the laser sensing device;

[0045] S202: Receives laser ranging data;

[0046] S203: Based on the data obtained in S202, calculate the distance from the measured road surface to the laser emitter, and thus obtain the road surface dimensions;

[0047] S204: Obtain the morphological characteristics of the road surface cross section based on the laser emission data of the laser sensing device;

[0048] S205: Based on the laser emission group data of the laser sensing device, obtain the characteristics and dimensions of different road surface defects, output the data, and execute S206.

[0049] S206: Determine whether there is a large deviation or duplication in the cross-sectional data of the two sets of laser emission groups at the same vertical angle. If so, proceed to S207; otherwise, proceed to S208.

[0050] S207: Compare this value with the detection data, remove duplicate values ​​and values ​​with large deviations, and then proceed to S208;

[0051] S208: Process the laser detection data in the same way as S201-S207, and output the data to the data analysis module.

[0052] Furthermore, S3 specifically includes the following steps:

[0053] S301: Read the data sent by the laser collector of the laser sensing device;

[0054] S302: Convert the data into computer language and compare the entered quantity with the detection data;

[0055] S303: Determine whether the data being compared in real time are the same. If they are not the same, remove duplicate values ​​from the larger values ​​or add missing values ​​from the smaller values ​​based on the position comparison between S304 and S305, and then execute step S304.

[0056] S304: Based on the transmitted cross-sectional data, analyze the location of the cross-section, compare it with the location in S305, transmit it to S303, analyze the dimensions of the road surface, the location and size of each defect in a single cross-section, compare it with the road surface dimensions, defect locations and sizes in S305, calculate the average value, locate the road surface damage and rut depth in the cross-section, and execute S310.

[0057] S208: Process the laser detection data in the same way as S201-S207, and output the data to the data analysis module.

[0058] Furthermore, S3 specifically includes the following steps:

[0059] S301: Read the data sent by the laser collector of the laser sensing device;

[0060] S302: Convert the data into computer language and compare the entered quantity with the detection data;

[0061] S303: Determine whether the data being compared in real time are the same. If they are not the same, remove duplicate values ​​from the larger values ​​or add missing values ​​from the smaller values ​​based on the position comparison between S304 and S305, and then execute step S304.

[0062] S304: Based on the transmitted cross-sectional data, analyze the location of the cross-section, compare it with the location in S305, transmit it to S303, analyze the dimensions of the road surface, the location and size of each defect in a single cross-section, compare it with the road surface dimensions, defect locations and sizes in S305, calculate the average value, locate the road surface damage and rut depth in the cross-section, and execute S310.

[0063] S307: Based on the data from each section transmitted by the laser detection data, analyze the differences in size and location of adjacent sections, accumulate and statistically analyze a certain defect feature, calculate the area by multiplying the lengths in mutually perpendicular driving directions, then remove values ​​with excessive deviation, and then calculate the average value Q2. Compare it with the average value Q1 of the area of ​​defect location features of each section in S307, calculate the average value again, locate the surface base of a certain defect feature of the continuous section, and execute S310.

[0064] S308: Analyze the severity of disease characteristics based on data from S304, S305, S306, and S307;

[0065] S309: Record S308 in the database and execute S312;

[0066] S310: Accumulate the number and surface base of each defect feature, and statistically analyze the pavement morphology features of each section and the section group composed of adjacent sections, including pavement smoothness and rut depth, and execute S311.

[0067] S311: Analyze the data of each disease and pavement morphology, output a line graph, and execute S312;

[0068] S312: The obtained data is fed back to the control module and the road surface detection database in real time, and the execution ends.

[0069] According to specific embodiments provided by the present invention, the following technical effects are disclosed: The intelligent detection system for road-use aerial quality inspection devices provided by the present invention overcomes the shortcomings of traditional road inspection equipment, such as its inability to be applied on a large scale and in a wide range, its inability to work continuously under conditions of bumpy roads and harsh working environments, and its low detection efficiency, long working time, and high cost. Furthermore, the present invention reduces detection costs and increases the versatility of the detection equipment by using a drone paired with a lidar system. The application of gyroscope components and drone components avoids erroneous data due to road bumps and traffic flow limitations, ensuring the authenticity, usefulness, and timeliness of the data. While accurately detecting a large number of road features, the invention increases the detection rate and allows for continuous detection, real-time modification and repair of the detection route to achieve the operator's objectives. This reduces the need for personnel and delays in information transmission, effectively reducing detection errors. The system is also safe, simple, easy to operate, has minimal impact on road traffic flow, is convenient to maintain and install, and is easy to carry for work. Specifically, the technical effects are as follows:

[0070] 1. By using UAV aerial surveying technology, data collection costs can be reduced, the efficiency and accuracy of road inspection can be improved, and the process can be safe, convenient, timely and accurate. It saves patrol personnel time in identifying potential hazards, increases the safety factor for patrol personnel in identifying potential hazards, and improves work efficiency and flexibility.

[0071] 2. The road inspection aerial device offers rapid image acquisition, portability, high mobility, and good operational continuity. It can be deployed in various environments to quickly collect road surface data. Important inspection areas can be continuously observed multiple times in multiple environments. Real-time data from multiple time periods and environments can be transmitted to the backend via a data transmission chain. The aerial device can also be remotely controlled.

[0072] 3. The road inspection aerial device is equipped with a gyroscope control system, which can effectively correct for various external disturbances that occur during the inspection process, greatly improving the accuracy of the inspection;

[0073] 4. Equipped with a dual lidar device that can intelligently adjust the angle, increasing the detection range and ensuring detection accuracy, while also making it easier to draw the three-dimensional shape of the road surface;

[0074] 5. Replacing traditional road inspection vehicles with drone technology reduces personnel requirements and lowers travel costs. Furthermore, remote control is more environmentally friendly and safer than fuel-consuming vehicles.

[0075] The intelligent detection system for road-based aerial quality inspection provided by this invention is powerful, portable, and can be deployed at any time. It is less affected by traffic flow, can operate continuously in complex terrain environments, ensures timeliness, can plan routes in advance, travels smoothly, and is highly efficient. Compared with traditional methods, it is more environmentally friendly, safer, and more efficient. Attached Figure Description

[0076] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0077] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0078] Figure 2 This is a schematic diagram of the structure of the gyroscope module of the present invention;

[0079] Figure 3 This is a schematic diagram of the imaging rod structure according to an embodiment of the present invention;

[0080] Figure 4 This is an embodiment of the present invention. Figure 3 A schematic diagram of the structure of the laser sensing module;

[0081] Figure 5 This is an embodiment of the present invention. Figure 4 Internal top view of the structure;

[0082] Figure 6 A top-view image of a road surface inspected by a drone-linked laser detection system for road-use aerial quality inspection devices.

[0083] Figure 7 This is a structural connection diagram of the intelligent detection system for the flight quality inspection device used on this route.

[0084] Figure 8 This is the overall logic flowchart of the intelligent detection method for the flight quality inspection device used on this route;

[0085] Figure 9 The laser sensing system steps of the UAV linkage structure described in the intelligent detection method of the flight quality inspection device for this road use are as follows:

[0086] Figure 10 Here is the logic flowchart of the laser sensing system for the intelligent detection method of the flight quality inspection device used on this route;

[0087] Explanation of reference numerals in the attached diagram: 1. Main UAV; 2. Secondary UAV; 3. Gyroscope module; 4. Imaging rod; 5. Landing gear; 6. Signal receiving / transmitting device; 7. LiDAR assembly; 8. Gyroscope assembly; 9. Gyroscope bracket; 10. Sleeve; 11. Connecting arm; 12. Ball joint; 13. Steering gear; 14. Connecting tray; 15. Battery; 16. Wire; 17. Fixing nut; 18. Laser rangefinder; 19. Swing arm; 20. Connecting plate; 21. Hemispherical light-transmitting cover; 22. Vertical scanning motor; 23. Optical assembly; 24. Light-transmitting base; 25. Phototube; 26. Receiving plate; 27. Aperture; 28. Light-shielding tube; 29. ​​Motor; 30. Scanning area; 31. Flight quality inspection device. Detailed Implementation

[0088] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0089] The purpose of this invention is to provide an intelligent detection system for road-use aerial quality inspection devices, which can accurately detect various road shapes, can be deployed at any time, is less affected by traffic flow, can operate continuously in complex terrain environments, ensures timeliness while allowing for advance route planning, has a smooth ride, high detection accuracy and high efficiency, is easy to carry, reduces personnel requirements, and also has advantages such as being environmentally friendly, safe, and easy to operate.

[0090] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0091] Example 1

[0092] like Figure 1-6 As shown, the intelligent detection system for road-use flight quality inspection devices provided in this embodiment of the invention includes a UAV linkage structure, a gyroscope module 3, an imaging rod 4, a laser sensing device, a data processing module, a control module, and a detection data matching and transmission sensing device. The UAV linkage structure includes a main UAV 1, a secondary UAV 2, and a landing gear 5. The laser sensing device includes a laser ranging sensor mounted on the main UAV 1 and a lidar assembly 7 mounted on the imaging rod 4. The UAV linkage structure and the imaging rod 4 constitute the flight quality inspection device 31.

[0093] The main UAV 1 is equipped with a laser rangefinder, a signal receiver / transmitter 6, and an emergency automatic intelligent return-to-home device. The signal receiver / transmitter 6 and the emergency automatic intelligent return-to-home device are both installed on the upper part of the main UAV 1. The laser rangefinder consists of two pairs of laser rangefinders forming a transmission group: one pair with a fixed vertical flight direction and one pair with a periodically changing direction. The laser beams emitted by the two transmission groups will collect data and transmit it back to the data processing device.

[0094] The imaging rod 4 is connected to the UAV linkage structure through a ball joint 12 and a steering gear 13. The gyroscope module 3 is mounted on the ball joint 12 and the steering gear frame. The ball joint 12 supports the UAV linkage structure and controls the imaging rod 4. Specifically, the imaging rod 4 includes a crossbar and a power supply. The power supply is arranged at both ends of the crossbar to provide power to the laser sensing module.

[0095] The lidar component 7 is disposed on the lower surface of the imaging rod 4. The laser sensing device includes a vertically arranged laser emitter, a laser rangefinder, and a data transmission sensor. The laser emitter and the laser rangefinder include multiple pairs of periodically transversely scanning laser emission ranging groups and longitudinally scanning spiral transversely swinging laser emission ranging groups.

[0096] The detection data matching and transmission sensing device includes a laser detection device 1 mounted on the lidar assembly, a spatial position detection device on the gyroscope module, and a laser detection device 2 mounted on the laser ranging sensing device. The laser detection device 1 and the laser detection device 2 form a laser detection group that is misaligned with the laser emission ranging group.

[0097] The data processing module includes a data collection mechanism, a data analysis mechanism, and a data feedback mechanism; the data processing module is communicatively connected to the laser sensing device and the detection data matching and transmission sensing device, and the laser sensing device, the data collection mechanism, the data analysis mechanism, and the data feedback mechanism are connected in sequence.

[0098] This design involves multiple sets of laser sensing devices that move vertically and periodically laterally, forming a vertical laser sensing network with the longitudinally scanning, spirally oscillating laser emission ranging group. When a feature area of ​​the road surface comes into contact with the laser, data is recorded. The data collection, analysis, and feedback mechanisms extract geometric features from the collected road surface data to obtain the required road damage rate, smoothness, and rut depth. The final road surface technical condition indicators are then output, promptly fed back to the control module, and stored in the database. The laser sensing devices and laser detection devices are arranged horizontally side by side. When the laser touches the road surface material, a precise judgment can be made based on the spatial position provided by the gyroscope module 3 and the positioning device, as well as the height provided by the laser rangefinder 18. This determines the basic morphological features of the road surface on the cross-section, as well as the dimensions of some special road defects, and the measured dimensions of the road surface.

[0099] In some embodiments, a data collection device can be added to transmit location information to the back-end control system. The planar virtual laser network formed by the transverse and longitudinal emission groups is based on transverse laser beam groups. However, to avoid omissions or excessive deviations in longitudinal defects on adjacent cross sections, which could lead to calculation errors for longitudinal cracks or other defects, the laser sensing module can be equipped with a longitudinally scanning, spirally oscillating laser emission group, thus forming a planar laser network to acquire the characteristics of longitudinal pavement defects. In the laser emission group, to avoid redundant data calculations, the laser rangefinder 18, gyroscope assembly 8, and positioning device work together to transmit data to the data acquisition module. Combined with location information, duplicate and large-deviation data are eliminated, improving data accuracy.

[0100] Example 2

[0101] In this embodiment, the main and auxiliary UAVs are hovering quadcopter UAVs. This type of UAV can fly stably. Both UAVs are equipped with landing gear to ensure the safety of the instrument landing. In order to prevent obstacles from affecting flight safety during flight, the UAVs contain early warning radar and braking system. The power supply of the UAVs is independent and independent from the gyroscope module and lidar component, thereby ensuring the safe and efficient use of the multi-functional road detection flight device, increasing the range of use, and enabling long-term continuous use.

[0102] In this embodiment, to ensure the timeliness of the flight device, both the main and auxiliary UAVs are wirelessly connected to the PC. The main UAV can receive command frequencies transmitted by the PC. Both UAVs are equipped with high-definition cameras, which can provide images of the inspection site and provide inspection personnel with a field of vision, thus well meeting the requirements for efficient inspection in various terrains and environments.

[0103] In this embodiment, the upper end of the gyroscope control frame is connected to the center of the UAV's lower chassis. The gyroscope is connected to the control module inside the UAV and fixed on the gyroscope bracket below the ball-and-socket joint. If the lower imaging rod assembly encounters airflow disturbances or angular errors, the gyroscope can promptly provide feedback to the control module, adjusting the ball-and-socket joint and steering gear to control the accuracy of the detection imaging. The lower part of the ball-and-socket joint is equipped with a rotating joint, which is sleeved with the lower balance fixing frame and meshes with its fixed gear. The balance fixing frame is fixed on the imaging rod 4. The gyroscope ensures that the LiDAR scanning is on a horizontal plane, reducing data accuracy problems caused by inaccurate reference surfaces.

[0104] In this embodiment, in order to ensure that the gears rotate accurately when the two UAVs are rotating, the main and auxiliary UAVs perform steering and other tasks synchronously under the control of the control module, and the ball joint is adjusted at the same adjustment angle and speed.

[0105] In this embodiment, the imaging rod 4 is equipped with a power supply component and a lidar component 7, which are connected to the lower end of the gyroscope control frame. The power supply component is built into both ends of the imaging rod 4, and the two lidar components 7 are embedded at 1 / 4 and 3 / 4 of the imaging rod 4. Both can be disassembled, which facilitates replacement, improves maintenance efficiency, and reduces costs.

[0106] Furthermore, the power supply component built into the imaging rod provides power only to the lidar component 7 and the gyroscope component 8, and the stored power is sufficient for continuous operation. The lidar component 7 includes a component housing, a motor 29, a lidar controller, a vertical scanning component, a horizontal scanning component, an optical component 23, and a connecting plate 20. The vertical and horizontal scanning components are both connected to the connecting plate 20 and controlled by the lidar controller. Their respective motors and controllers control the reciprocating motion of the optical component in the vertical and horizontal directions.

[0107] Furthermore, in this embodiment, the optical component 23 includes a reflector, an aperture 27, a light shield, and a signal receiving lens group, used to perform radar scanning and receive radar scanning signals to draw a road topography image and further analyze the road damage situation.

[0108] In this embodiment, the outer shell of the lidar component 7 is a hemispherical light-transmitting cover 21 with an infrared light-reflecting film. The hemispherical light-transmitting cover 21 is hard and less affected by external light, and can work in different light intensity environments.

[0109] Furthermore, both the laser sensing device and the laser detection device are placed inside the photomask, and periodically scan the entire cross-section in the vertical direction with a maximum scanning angle of 30° to 150°; the maximum scanning angle of the laser emission group of the laser sensing device in the longitudinal direction is -45° to 45°; the remaining structures and components are as described in Example 1, and will not be described again.

[0110] Example 3

[0111] In the intelligent detection system of the road-use flight quality inspection device in this embodiment, the data analysis mechanism includes a PC and a data preprocessing module, a road damage analysis module, a road surface smoothness analysis module, a road rutting analysis module, an area feature extraction algorithm module, and an algorithm processing module mounted on the PC; the feedback module includes a control terminal display device. The data collection mechanism includes a laser receiver M and a laser receiver N; the input end of the laser receiver is connected to the data transmission device on the laser sensing device and the laser detection device, respectively, and the output end of the laser receiver is connected to the PC through a network port.

[0112] Furthermore, in this embodiment, the PC is equipped with the following modules: a data preprocessing module, which is used to convert the laser feedback data collected from the laser sensing device and the distance signal between the road surface and the laser rangefinder into digital signals, transmit their spatial information and feature information, match and compare them with the data from the laser detection device, eliminate duplicate data and data with large deviations, and ensure the authenticity and accuracy of the data.

[0113] The area feature extraction algorithm module is used to detect damage features of a road surface when passing through a certain cross section. It starts recording these features until the damage disappears at a specific cross section. The recorded features are then converted into digital information using the formula A = x * y, where A is the area of ​​the damage; x is the width of the damage at that cross section obtained from the cross-sectional laser scan; and y is the number of cross sections where the damage consistently appears and the longitudinal length. The results are then transmitted to the road surface damage analysis module and the algorithm analysis module.

[0114] The pavement damage analysis module calculates pavement damage status and real-time pavement breakage rate, statistically analyzes the damage situation of each pavement segment, and accumulates the relative areas of various defects such as cracks, block cracks, transverse cracks, vertical cracks, rutting, subsidence, bulges, potholes, loosening, and repairs. This data is then transmitted to the algorithm processing module and fed back to the control system as needed. Considering the impact of differences in pavement defect morphology on the statistical results, a custom defect morphology parameter, "three-point dimension," is introduced. Pavement defect morphology is categorized into five types based on the three-point dimension parameter, laser ranging height difference, and the ratio of transverse to longitudinal dimensions: transverse cracks, longitudinal cracks, potholes, rutting, and others. Based on the geometric relationship between pavement defect types and the laser grid, special fuzzy boundaries are equivalently replaced. Based on the data collected by the laser sensing device F, the number and area of ​​different defect types are accumulated, and expressed according to the algorithm formula: In the formula, a0 is 15 for asphalt pavement and 10.66 for cement pavement; a1 is 0.412 for asphalt pavement and 0.461 for cement pavement; A is the area of ​​pavement inspection; i is the type of pavement damage, with different types having different weights; k is the damage conversion coefficient, k=2 for transverse and longitudinal joints, and k=1 for other damages; calculate the pavement damage index.

[0115] The road surface smoothness analysis module is used to calculate the road surface smoothness condition and statistically analyze the smoothness index, according to the algorithm formula requirements: (expressed as follows) In the formula, IRI is the International Roughness Index, which is used to analyze the road surface driving quality index; and the data is transmitted to the algorithm processing module.

[0116] The road rutting analysis module is used to calculate rutting depth. Through multiple laser cross-sectional scans, it maps the morphological features of the road surface cross-section, analyzes the height of various spatial locations on the road surface, and determines the rutting depth through analysis and comparison. The algorithm formula is expressed as follows: In the formula: h1 is the highest point of the road surface within the laser detection area, and h2 is the lowest point of the road surface within the laser detection area; the results are transmitted to the algorithm processing module to analyze the road rut depth index;

[0117] The algorithm processing module is used to remove duplicate data and data with large deviations. After sorting the data, it converts the actual road surface conditions into digital information and transmits it to the road surface technical condition database. This records the road surface conditions under different maintenance historical conditions and promptly feeds the data back to highway maintenance units at all levels for further processing.

[0118] The data analysis module requires the following algorithm formula: The technical condition index Z of the road surface is analyzed and saved to the database. The remaining structures and components are as described in Implementation Method 1 and will not be described again.

[0119] Example 4

[0120] like Figures 1 to 6 As shown, the intelligent detection system method for road-going flight quality inspection devices provided in this embodiment of the invention is implemented through the following steps:

[0121] S0: Overall flight quality inspection device in operation;

[0122] S1: The laser sensing system of the UAV linkage structure is activated;

[0123] S2: Data collection;

[0124] S3: Data Analysis.

[0125] Furthermore, the operation steps of the overall flight quality inspection device described in S0 specifically include the following process:

[0126] S001: The UAV linkage structure receives signals from the control module via an electromagnetic signal receiver to perform remote operations;

[0127] S002: The drone transmits real-time footage captured by a high-definition camera to the inspection personnel.

[0128] S003: The inspector sends a control command. The UAV controls the ball joint and steering gear through the control component to change the height and horizontal angle of the imaging rod. If the set height and horizontal angle are met, proceed to S004; otherwise, proceed to S002.

[0129] S004: The horizontal scanning component inside the lidar module starts to control the optical component to rotate in the horizontal direction within a preset angle range and transmits the imaging information back to the database.

[0130] S005: The lidar component will transmit data to the inspection personnel's PC in real time for data integration to obtain a road topography map;

[0131] S006: The inspection personnel control the lidar controller and the UAV control components in real time based on the road topography map.

[0132] Furthermore, the specific steps for activating the laser sensing system of the UAV linkage structure described in S1 include:

[0133] S101: The drone linkage structure flies along a prescribed route according to instructions;

[0134] S102: Collect data from the two transmitting groups of the laser rangefinder sensor on the UAV;

[0135] S103: Based on laser ranging, the distance and relative speed between the front vehicle and the rear vehicle and the flight device during low-altitude flight are obtained, thereby determining the relative spatial position of the flight device;

[0136] S104: Determine whether the preceding and following vehicles will affect the subsequent low-altitude survey. If yes, proceed to S105; otherwise, proceed to S107.

[0137] S105: Determine whether the speed of the flight quality inspection device needs to be changed without affecting the inspection route. If yes, proceed to S106; otherwise, proceed to S107.

[0138] S106: Analyze the speed of the vehicles in front and behind to adjust the speed of the flight quality inspection device, ensure that the space clearance of the flight quality inspection device meets the requirements, and then execute S108.

[0139] S107: The spatial position and speed of the current vehicle and the following vehicle exceed the upper limit of the rated operating speed, affecting the safety of the flight device. Adjust the spatial altitude of the flight quality inspection device, reduce the speed, and then execute S108.

[0140] S108: Records the spatial position of the flight quality inspection device in real time and feeds it back to the control module, which then issues instructions to adjust the device in a timely manner.

[0141] Furthermore, the data collection step described in S2 specifically includes:

[0142] S201: Receives data from the laser sensing device;

[0143] S202: Receives laser ranging data;

[0144] S203: Based on the data obtained in S202, calculate the distance from the measured road surface to the laser emitter, and thus obtain the road surface dimensions;

[0145] S204: Obtain the morphological characteristics of the road surface cross section based on the laser emission data of the laser sensing device;

[0146] S205: Based on the laser emission group data of the laser sensing device, obtain the characteristics and dimensions of different road surface defects, output the data, and execute S206.

[0147] S206: Determine whether there is a large deviation or duplication in the cross-sectional data of the two sets of laser emission groups at the same vertical angle. If so, proceed to S207; otherwise, proceed to S208.

[0148] S207: Compare this value with the detection data, remove duplicate values ​​and values ​​with large deviations, and then proceed to S208;

[0149] S208: Process the laser detection data in the same way as S201-S207, and output the data to the data analysis module.

[0150] Furthermore, the data analysis steps described in S3 specifically include:

[0151] S301: Read the data sent by the laser collector of the laser sensing device;

[0152] S302: Convert the data into computer language and compare the entered quantity with the detection data;

[0153] S303: Determine whether the data being compared in real time are the same. If they are not the same, remove duplicate values ​​from the larger values ​​or add missing values ​​from the smaller values ​​based on the position comparison between S304 and S305, and then execute step S304.

[0154] S304: Based on the transmitted cross-sectional data, analyze the location of the cross-section, compare it with the location in S305, transmit it to S303, analyze the dimensions of the road surface, the location and size of each defect in a single cross-section, compare it with the road surface dimensions, defect locations and sizes in S305, calculate the average value, locate the road surface damage and rut depth in the cross-section, and execute S310.

[0155] S305: Based on the transmitted laser detection data, analyze the size and location of the measured road cross section, compare it with the location in S304, transmit it to S303, analyze the size of the road surface in a single cross section, the location and size of each defect, compare it with the size of the road surface, defect location and size of the same cross section in S305, calculate the average value, locate the road surface damage and rutting depth in the cross section, and execute S310.

[0156] S306: Based on the data from each cross section, analyze the size difference between adjacent cross sections and the difference in other disease characteristics, accumulate and statistically analyze a certain disease characteristic, calculate the area by multiplying the lengths in mutually perpendicular driving directions, then remove values ​​with excessive deviation, then calculate the average value Q1, compare it with the average value Q2 of the area of ​​disease characteristics of each cross section in S307, calculate the average value again, locate the surface base Qi of a certain disease characteristic of the continuous cross section, and execute S310.

[0157] S307: Based on the data from each section transmitted by the laser detection data, analyze the differences in size and location of features between adjacent sections, accumulate and statistically analyze a certain feature of a disease, calculate the area by multiplying the lengths in mutually perpendicular driving directions, then remove values ​​with excessive deviations, and then calculate the average value Q2. Compare it with the average value Q1 of the area of ​​features such as the location of disease in each section of S307, and calculate the average value again. Locate the surface base of a certain feature of a disease in the continuous section, and execute S310.

[0158] S308: Analyze the severity of disease characteristics based on data from S304, S305, S306, and S307;

[0159] S309: Record S308 in the database and execute S312;

[0160] S310: Accumulate the number and surface base of each defect feature, and statistically analyze the pavement morphology features of each section and the section group composed of adjacent sections, including pavement smoothness and rut depth, and execute S311.

[0161] S311: Analyze the data of each disease and pavement morphology, output a line graph, and execute S312;

[0162] S312: The obtained data is fed back to the control module and the road surface detection database in real time, and the execution ends.

[0163] In summary, this road inspection aerial quality inspection system and method, utilizing UAV aerial surveying technology, reduces data acquisition costs, improves the efficiency and accuracy of road inspection, and offers safe, convenient, timely, and accurate results. It saves inspection personnel time in identifying potential hazards and increases their safety, thus improving work efficiency and flexibility. The road inspection aerial device acquires images quickly, is portable, highly mobile, and offers good operational continuity, allowing for rapid data collection of road conditions in various environments. It can continuously observe key inspection areas at multiple frequencies and in multiple environments, transmitting real-time data from various time periods and environments to the backend via a data transmission chain. Simultaneously, the aerial device can be monitored. Equipped with a gyroscope control system, the road inspection aerial device effectively corrects for various external disturbances during the inspection process, significantly improving inspection accuracy. The dual-laser radar device with intelligent angle adjustment increases the detection range, ensuring accuracy while facilitating the mapping of the road surface's three-dimensional topography. Replacing traditional road inspection vehicles with UAV technology reduces personnel requirements and travel costs, while remote control is more environmentally friendly and safer than fuel-consuming vehicles. This invention presents a portable and powerful road detection aerial device that can be deployed at any time, is less affected by traffic flow, and can operate continuously in complex terrain environments. It ensures timeliness while allowing for pre-planned routes, operates smoothly, and is highly efficient. Compared to traditional methods, it is more environmentally friendly, safer, and more efficient. The system's data processing section includes complete data acquisition, analysis, and feedback modules, as well as comprehensive algorithms, which avoid problems such as duplication, omissions, and miscalculations.

[0164] The remaining technical features in this embodiment can be flexibly selected by those skilled in the art to meet different specific practical needs. However, it is obvious to those skilled in the art that these specific details are not necessary to implement the present invention. In other instances, to avoid obscuring the present invention, well-known components, structures, or parts are not specifically described, and all are within the scope of technical protection defined by the claims of the present invention.

[0165] In the description of this invention, unless otherwise explicitly specified and limited, the terms "set up," "connection," etc., are used in a broad sense and should be interpreted broadly by those skilled in the art. For example, it can refer to a fixed connection, a movable connection, an integral connection, or a partial connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can also refer to the internal connection of two components, etc. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances. That is, the expression of the written language can flexibly correspond to the actual implementation of the technology. The expression of the written language (including the drawings) in this specification does not constitute any single limiting interpretation of the claims.

[0166] Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of this invention should be within the protection scope of the appended claims. In the above description, numerous specific details have been set forth to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, to avoid obscuring the invention, well-known techniques, such as specific construction details, operating conditions, and other technical conditions, have not been specifically described.

[0167] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A road-use flying matter detection device intelligent detection system, characterized in that, The unmanned aerial vehicle linkage structure, the gyroscope module, the imaging rod, the laser sensing device, the data processing module, the control module, and the detection data matching transmission sensor device are included. The main unmanned aerial vehicle is provided with a laser ranging sensing device, a signal receiving / transmitting device, and an emergency automatic intelligent return device. The imaging rod and the unmanned aerial vehicle linkage structure are connected through a ball joint and a steering gear set, and the gyroscope module is arranged on the ball joint and the steering gear set. The laser radar assembly is arranged on the lower surface of the imaging rod and includes a vertically arranged laser emitter, a laser range finder, and a data transmission sensor. The data processing module includes a data collection mechanism, a data analysis mechanism, and a data feedback mechanism. Two laser radar assemblies are arranged at 1 / 4 and 3 / 4 of the imaging rod, respectively. The data analysis mechanism includes a PC and a data preprocessing module, a road damage condition analysis module, a road flatness analysis module, a road rut analysis module, an area feature extraction algorithm module, and an algorithm processing module. The data preprocessing module is used to convert the laser feedback data collected from the laser sensing device and the distance signal from the road to the laser range finder into digital signals, transmit the spatial information and feature information, and match and compare with the laser detection device data to eliminate repeated data and large deviation data. The road flatness analysis module is used to calculate the road flatness condition, count the flatness index, analyze the road driving quality index, and transmit it to the algorithm processing module. The road surface damage condition analysis module is configured to calculate the road surface damage condition and the real-time damage rate of the road surface, count the damage conditions of each section of the road surface, accumulate the relative areas of cracks, block cracks, transverse cracks, vertical cracks, ruts, depressions, bulges, pits, looseness and repair diseases, and transmit the accumulated relative areas to the algorithm processing module and timely feedback to the control module. The road surface damage condition index formula is: z 1=100− , wherein a 0 is 15 for asphalt pavement and 10.66 for cement pavement; a 1 is 0.412 for asphalt pavement and 0.461 for cement pavement; A is the area of the road surface detection; i is the road surface damage type, and different types have different weights; k is a disease conversion coefficient. IRI The flatness index calculation formula is , wherein ​ is the international flatness index analysis pavement driving quality index; The road surface rut analysis module is used for counting rut depth, drawing road surface cross-section profile characteristics through laser multi-section scanning, analyzing the height of each spatial position of the road surface, determining the rut depth through analysis and comparison, and transmitting data to the algorithm processing module to analyze the road surface rut depth index. The area feature extraction algorithm module is used for detecting disease damage features by different angle laser sensing devices when passing through a section, starting recording until the disease disappears at a section, converting the recorded disease features into digital information, and transmitting to the road surface damage condition analysis module and the algorithm analysis module. The algorithm processing module is used for removing repeated data and large deviation data, converting the actual road surface conditions into digital information after data arrangement, and transmitting to the road surface technical condition processing software, so as to calculate the road surface conditions under different maintenance history states, and feed back the data to the road maintenance units at all levels for further processing.

2. The intelligent detection system for a road-use flying matter detection device according to claim 1, characterized in that, The control module comprises a remote wireless data receiving device, an instruction emitting device and a pc terminal with remote control property.

3. The intelligent detection system of the road flying quality detection device according to claim 1, wherein the data feedback mechanism comprises a control terminal display device; the data collection mechanism comprises a laser receiver M and a laser receiver N; the laser receiver input end is connected to the data transmission device on the laser sensing device and the laser detection device respectively, and the laser receiver output end is connected to the PC through a network port.

4. An intelligent detection method for a road-use flying matter detection device, characterized in that, The intelligent detection system of the road flying quality detection device according to any one of claims 1-3 is implemented, comprising the following steps: S0: overall flying quality detection device operation; S1: laser sensing system of unmanned aerial vehicle linkage structure starts; S2: data collection; S3: data analysis.

5. The intelligent detection method of a road-use flying matter detection device according to claim 4, characterized in that, The S0 overall flying quality detection device operation specifically comprises the following steps: S001: the unmanned aerial vehicle linkage structure receives the signal of the control module through the electromagnetic signal receiver to perform remote work; S002: the unmanned aerial vehicle transmits the real scene photographed by the high-definition camera to the detection personnel in real time to transmit the field of view information; S003: the detection personnel sends a control command, and the unmanned aerial vehicle changes the height and horizontal angle of the imaging rod through the control assembly of the ball joint and the steering gear; if the set height and horizontal angle are met, S004 is performed, and if not, S002 is performed; S004: the horizontal scanning member in the laser radar assembly starts to control the optical assembly to rotate in the horizontal direction within the preset angle range, and transmits the imaging information back; S005: the laser radar assembly transmits real-time data to the PC terminal of the detection personnel to integrate the data and obtain the road profile map; S006: the detection personnel controls the laser radar controller and the unmanned aerial vehicle control assembly in real time according to the road profile map. 6.The intelligent detection method of the road-use flying matter detection device according to claim 4, characterized in that, S1 the laser sensing system of the unmanned aerial vehicle linkage structure starts, specifically comprising the following steps: S101: the unmanned aerial vehicle linkage structure flies according to the specified route according to the instruction; S102: collect the data of two emission groups of the laser ranging sensing device on the unmanned aerial vehicle; S103: According to the data obtained in S102, the distance and relative speed of the front and rear vehicles from the flying device are calculated, and the relative spatial position of the flying device is calculated; S104: Determine whether the front and rear vehicles affect the subsequent low-altitude survey; if yes, execute S105; if no, execute S107; S105: Determine whether the speed of the flying quality inspection device needs to be changed without affecting the detection route; if yes, execute S106; otherwise, execute S107; S106: Analyze the speed of the front and rear vehicles to adjust the speed of the flying quality inspection device, ensure that the spatial clearance of the flying quality inspection device meets the requirements, and then execute S108; S107: The spatial position and speed of the front and rear vehicles exceed the rated operating speed limit, affecting the safety of the flying quality inspection device, adjust the spatial height of the flying quality inspection device, reduce the speed, and then execute S108; S108: Real-time record the spatial position of the flying quality inspection device, feedback to the control module, and the control module timely sends adjustment instructions.

7. The intelligent detection method of a road-use flying matter detection device according to claim 6, characterized in that, The S2 data collection specifically includes the following steps: S201: Accept the data of the laser sensing device; S202: Accept the laser ranging group data; S203: According to the data obtained in S202, the distance from the measured road surface to the laser transmitter is calculated, and the road surface size is calculated; S204: Obtain the road cross-section feature according to the laser emission group data of the laser sensing device; S205: Obtain the characteristics and size of different road diseases according to the laser emission group data of the laser sensing device, output the data, and execute S206; S206: Determine whether the data of the two laser emission groups has a large deviation or repetition in the same vertical angle, if yes, execute S207, otherwise execute S208; S207: Remove repeated values and values with large deviations, and then execute S208; S208: Process the laser detection data according to S201-S207, and output the data to the data analysis module. 8.The intelligent detection method of the road-use flying matter detection device according to claim 4, characterized in that, The S3 specifically includes the following steps: S301: Read the data sent by the laser collector of the laser sensing device; S302: Convert the data into computer language, and compare the recorded quantity with the detection data; S303: Determine whether the real-time comparison data is the same, if not, according to the position comparison of S304 and S305, remove the repeated values in the large values, or add the missing values in the small values, and then execute step S304; S304: According to the transmission of each cross-section data, analyze the position of the cross-section, compare with the position of S305, transmit to S303, analyze the size of the road surface in a single cross-section, the position and size of each disease, and compare with the road surface size, disease position and size of S305 in the cross-section to obtain the average value, and position the road damage and rut depth in the cross-section, and execute S310; S305: Based on the transmitted laser detection data, analyze the size and location of the measured road cross section, compare it with the location in S304, transmit it to S303, analyze the size of the road surface in a single cross section, the location and size of each defect, compare it with the size of the road surface, defect location and size of the same cross section in S305, calculate the average value, locate the road surface damage and rutting depth in the cross section, and execute S310. S306: Based on the data from each cross section, analyze the size difference between adjacent cross sections and the difference in other disease characteristics, accumulate and statistically analyze a certain disease characteristic, calculate the area by multiplying the lengths in mutually perpendicular driving directions, then remove values ​​with excessive deviation, then calculate the average value Q1, compare it with the average value Q2 of the area of ​​disease characteristics of each cross section in S307, calculate the average value again, locate the surface base Qi of a certain disease characteristic of the continuous cross section, and execute S310. S307: Based on the data from each section transmitted by the laser detection data, analyze the size difference and the difference in the location characteristics of adjacent sections, accumulate and statistically analyze a certain defect feature, calculate the area by multiplying the lengths in mutually perpendicular driving directions, then remove values ​​with excessive deviation, then calculate the average value Q2, compare it with the average value Q1 of the area of ​​the defect location characteristics of each section in S307, calculate the average value again, locate the surface base of a certain defect feature of the continuous section, and execute S310. S308: Analyze the severity of disease characteristics based on data from S304, S305, S306, and S307; S309: Record S308 in the database and execute S312; S310: Accumulate the number and surface base of each defect feature, and statistically analyze the pavement morphology features of each section and the section group composed of adjacent sections, including pavement smoothness and rut depth, and execute S311. S311: Analyze the data of each disease and pavement morphology, output a line graph, and execute S312; S312: The obtained data is fed back to the control module and the road surface detection database in real time, and the execution ends.

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