Road flatness detection method and system based on laser scanning

Abstract

The invention relates to the technical field of road flatness detection, and particularly discloses a road flatness detection method and system based on laser scanning, and the method comprises the steps: S1, controlling a mobile carrier to move on a road at a preset constant speed, enabling a laser scanner to collect the coordinate information of a road surface at a preset frequency, the acceleration sensor collects acceleration information of the laser scanner in the vertical direction in real time; when the road flatness of the corresponding road section is judged to be possibly substandard according to preliminary analysis, whether the road section needs to be repaired or not is further judged, so that the computing power demand of an analysis control module in the road flatness detection process can be effectively reduced; the cost of road flatness detection is reduced, the risk of crash of the analysis control module is reduced, a proper vehicle speed is ensured in the flatness detection process of the driving carrier on the expressway, and potential traffic safety hazards caused by low-speed driving are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of road smoothness detection, and in particular to a road smoothness detection method and system based on laser scanning. Background Art

[0002] In modern highway construction, asphalt pavement is widely used in high-grade highways due to its advantages such as short construction period, high driving comfort, low noise and easy later maintenance. However, high-grade asphalt pavement has high requirements for flatness. The uneven surface of the road structure layer or poor road surface flatness of some sections caused by construction will cause a series of road diseases to appear or even worsen under the influence of repeated vehicle loads and natural environmental conditions during subsequent road operation, destroying the road surface structure and shortening the overall service life of the road. Laser scanning technology, as an emerging measurement technology, has the advantages of fast, real-time and high-density acquisition of target surface data and long-distance non-contact measurement compared with total stations. Compared with traditional road surface flatness detection technology, it has higher detection accuracy and detection efficiency that perfectly meets the construction progress. It is less affected by the environment and construction, and is suitable for surface flatness detection of the structural layer of construction sections.

[0003] In the process of detecting the flatness of roads through laser scanning technology, it has a significant speed advantage in collecting road coordinate data. However, due to the need to process, analyze and convert the collected data, the speed of analyzing the collected data is limited. In the process of detecting the flatness of traditional roads, the moving speed of the vehicle is slow. A laser scanner can be installed on the vehicle to continuously detect the flatness of the traditional road during operation. The laser scanner can meet the needs of traditional road flatness detection. However, when it is necessary to perform flatness detection on a highway during passage, if the vehicle moves quickly on the highway for detection, the laser scanner may cause a "freeze" due to excessive data that needs to be analyzed and processed. If the vehicle moves at a low speed on the highway for detection, there are certain traffic safety hazards, which may cause rear-end collisions on the highway. Summary of the invention

[0004] The purpose of the present invention is to provide a road roughness detection method and system based on laser scanning to solve the following technical problems:

[0005] How to detect the smoothness of highways based on laser scanning technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A road roughness detection method based on laser scanning, the method comprising:

[0008] S1, controlling the mobile vehicle to move at a preset speed on the road, the laser scanner collects the coordinate information of the road surface at a preset frequency, the acceleration sensor collects the acceleration information of the laser scanner in the up and down directions in real time, and the noise sensor collects the noise information generated by the tire and the road surface in real time;

[0009] S2, the analysis control module analyzes the information collected by the acceleration sensor and the noise sensor to obtain a road bump characteristic value. When the road bump characteristic value exceeds the preset warning bump characteristic value, it indicates that the road flatness of the corresponding road section may not meet the standard, and step S3 is executed;

[0010] S3, the analysis control module analyzes the information collected by the laser scanner of the road section corresponding to the warning signal generated in step S2, obtains the road smoothness reference coefficient of the road section, and determines whether the road smoothness of the road section meets the standard. If not, a warning signal is generated indicating that the road section needs to be repaired.

[0011] Furthermore, the process of obtaining that the road smoothness of the corresponding road section may not meet the standard includes:

[0012] According to the acceleration value collected by the acceleration sensor and the noise decibel value collected by the noise sensor, the acceleration change curve A over time is obtained respectively. s (t) and the noise decibel value changing with time curve L s (t);

[0013] During the preset time period, the acceleration time curve A s (t) and noise decibel value over time to analyze the curve L s (t), obtain the road bump characteristic value Q corresponding to the section where the mobile vehicle travels within the preset time period Δt ;

[0014] The road bump characteristic value Q Δt and the preset warning turbulence characteristic value Q std Make a comparison;

[0015] If there is Q Δt ≥Q std , indicating that the road smoothness of the corresponding road section may not meet the standard, executing step S3, and simultaneously analyzing the information collected by the acceleration sensor and the noise sensor in the next preset time period;

[0016] On the contrary, the information collected by the acceleration sensor and the noise sensor in the next preset time period is analyzed.

[0017] Furthermore, the process of obtaining the road bump characteristic value corresponding to the road section traveled by the mobile vehicle within the preset time period includes:

[0018]

[0019] The road bump characteristic value Q corresponding to the driving section of the mobile vehicle within the preset time period is obtained by analysis and calculation through formula (1): Δt ;

[0020] Wherein, [t1, t2] is a preset time period, Δt=t2-t1, τ1 and τ2 are weight coefficients corresponding to the factors affecting the road bumpy characteristic value.

[0021] In one embodiment, the process of generating a warning signal indicating that the road section needs to be repaired includes:

[0022] S31, establishing a rectangular coordinate system for analyzing all coordinate information collected by the laser scanner at a preset frequency within a preset time period [t1, t2], and obtaining a road transverse surface curve diagram corresponding to each collection node;

[0023] S32, analyzing the road transverse surface curve graphs corresponding to each collection node in turn to obtain the road transverse flatness parameters corresponding to each collection node of the road section;

[0024] S33, comparing and analyzing the road transverse surface curve graphs corresponding to each acquisition node to obtain the road longitudinal smoothness parameters of the road section;

[0025] S34, obtaining a road smoothness reference coefficient of the road section by analyzing the road transverse smoothness parameters and the road longitudinal smoothness parameters corresponding to each acquisition node of the road section;

[0026] S35. Compare the road smoothness reference coefficient of the road section with the preset road smoothness danger reference coefficient. If the road smoothness reference coefficient of the road section is greater than or equal to the preset road smoothness danger reference coefficient, generate a warning signal that the road section needs to be repaired.

[0027] Furthermore, in step S32, the process of obtaining the road transverse flatness parameters corresponding to each acquisition node of the road section includes:

[0028] In the preset time period [t1, t2], the laser scanner collects N road transverse surface curve graphs, and obtains M Y-axis corresponding coordinate data at equal intervals along the X axis for each road transverse surface curve graph;

[0029]

[0030] The road transverse flatness parameter σ corresponding to the i-th collection node is obtained by analysis and calculation through formula (2): i ;

[0031] Among them, i∈[1,N], j∈[1,M], W i,jis the coordinate data corresponding to the j-th Y axis obtained in the i-th road transverse surface curve diagram, It is the average value of all Y-axis corresponding coordinates obtained in the i-th road transverse surface curve diagram.

[0032] Furthermore, in step S33, the process of obtaining the longitudinal roughness parameter of the road section includes:

[0033]

[0034] The longitudinal roughness parameter ρ of the road section is obtained by analyzing formulas (3)-(5): s ;

[0035] Among them, E i is the area characteristic parameter of the i-th road transverse surface curve, f i (l) is the lateral surface curve of the i-th road, It is the average area characteristic parameter of N road transverse surface curves within a preset time period.

[0036] Furthermore, in step S34, the process of obtaining the road smoothness reference coefficient of the road section includes:

[0037]

[0038] The road roughness reference coefficient P of the road section is obtained by analyzing formula (6): s ;

[0039] Among them, ω1 and ω2 are weight coefficients corresponding to the parameters affecting the road smoothness reference coefficient.

[0040] In one embodiment, the method further comprises:

[0041] S4. Comprehensively analyzing all sections of the preset detection road for which the road flatness reference coefficients are generated by the analysis control module, comparing all the generated road flatness reference coefficients with the preset road flatness warning reference coefficients in turn, and counting the number of sections for which the road flatness reference coefficients are greater than or equal to the preset road flatness warning reference coefficients. If the number K obtained by the statistics is greater than or equal to G*α, a warning signal indicating that the preset detection road needs maintenance is generated;

[0042] Among them, K is the number of sections in the preset detection road where the road flatness reference coefficient is greater than or equal to the preset road flatness warning reference coefficient, G is the total number of detection sections in the preset detection road, and α is the preset proportion coefficient.

[0043] A road roughness detection system based on laser scanning, used in a road roughness detection method based on laser scanning, the system comprising:

[0044] Mobile vehicles are vehicles used to move on roads at a preset speed;

[0045] A pan / tilt head, installed on the mobile vehicle, to ensure that the equipment installed thereon always remains level;

[0046] A laser scanner, mounted on the pan-tilt platform, is used to scan the road surface and collect the coordinate information of the road surface;

[0047] An acceleration sensor, mounted on the laser scanner, for collecting acceleration information in the up and down directions of the laser scanner;

[0048] A noise sensor is installed near the tires of the mobile vehicle and is used to collect noise information generated by the tires and the road surface during the movement of the mobile vehicle;

[0049] Early warning module, used to execute early warning signals for production;

[0050] The analysis control module is used to analyze the information collected by the acceleration sensor and the noise sensor to obtain the road bump characteristic value. When the road bump characteristic value exceeds the preset warning bump characteristic value, the coordinate information corresponding to the road section collected by the laser scanner is analyzed to determine whether a warning signal is generated that the road section needs to be repaired. A comprehensive analysis is performed on all sections in the preset detection road that generate road flatness reference coefficients to determine whether a warning signal is generated that the preset detection road needs maintenance.

[0051] Beneficial effects of the present invention:

[0052] The present invention performs a preliminary analysis on the flatness information of the highway by firstly analyzing the tire noise information and the acceleration change information in the up and down directions generated by the mobile vehicle during the driving process on the highway. When the road flatness of the corresponding section may not meet the standard, the information collected by the laser scanner when the mobile vehicle passes through the section is further analyzed to determine whether the section needs to be repaired. This can effectively reduce the computing power demand of the analysis and control module in the process of road flatness detection, reduce the cost of road flatness detection and the risk of the analysis and control module "crashing", and ensure that the driving vehicle maintains an appropriate vehicle speed during the flatness detection on the highway to avoid traffic safety hazards caused by low-speed driving, thereby achieving the purpose of fully detecting the flatness of the highway based on laser scanning technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention will be further described below in conjunction with the accompanying drawings.

[0054] Figure 1 It is a flow chart of the steps of a road flatness detection method based on laser scanning proposed by the present invention;

[0055] Figure 2 It is a flowchart of the steps of generating a warning signal that the road section needs to be repaired in a road flatness detection method based on laser scanning proposed by the present invention;

[0056] Figure 3 This is a schematic block diagram of a road smoothness detection system based on laser scanning proposed by the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] See also Figure 1-2 As shown, in one embodiment, a road flatness detection method based on laser scanning is provided, the method comprising:

[0059] S1. Control the mobile vehicle to move at a preset speed on the road at a constant speed. The laser scanner collects the coordinate information of the road surface at a preset frequency. The acceleration sensor collects the acceleration information of the laser scanner in the up and down directions in real time. The noise sensor collects the noise information generated by the tire and the road surface in real time. The preset speed is the normal driving speed on the highway. The preset frequency can be selected based on experience.

[0060] S2, the analysis control module analyzes the information collected by the acceleration sensor and the noise sensor in each preset time period to obtain the road bump characteristic value in each preset time period. When the road bump characteristic value in a preset time period exceeds the preset warning bump characteristic value, it indicates that the road flatness of the corresponding road section may not meet the standard, and step S3 is executed;

[0061] S3, the analysis control module analyzes the information collected by the laser scanner of the road section corresponding to the warning signal generated in step S2, obtains the road smoothness reference coefficient of the road section, and determines whether the road smoothness of the road section meets the standard. If not, a warning signal is generated indicating that the road section needs to be repaired.

[0062] Through the above technical scheme, this embodiment provides a road flatness detection method based on laser scanning. The method preliminarily analyzes the flatness information of the highway by firstly analyzing the tire noise information and the acceleration change information in the up and down directions generated by the mobile vehicle during the driving on the highway. In this way, the problem of "freezing" failure of the analysis control module due to excessive coordinate data to be processed during the detection of the surface flatness of the highway can be avoided, and the appropriate vehicle speed can be ensured during the flatness detection of the moving vehicle on the highway to avoid traffic safety hazards caused by low-speed driving. When the road flatness of the corresponding section may not meet the standard, the information collected by the laser scanner when the mobile vehicle passes through the section is further analyzed to determine whether the section needs to be repaired. In the process of analyzing the coordinate information of a certain section of road, the risk of "freezing" of the analysis control module due to excessive amount of data to be processed can be reduced.

[0063] In one embodiment, the process of obtaining that the road smoothness of the corresponding road section may not meet the standard includes:

[0064] Two rectangular coordinate systems are established, with time as the X-axis, the acceleration value collected by the acceleration sensor and the noise decibel value collected by the noise sensor as the Y-axis, and the acceleration versus time curve A is obtained by fitting modeling. s (t) and the noise decibel value changing with time curve L s (t);

[0065] During the preset time period, the acceleration time curve A s (t) and the noise decibel value changing with time curve L s (t) Analyze and obtain the road bump characteristic value Q corresponding to the driving section of the mobile vehicle within the preset time period Δt ;

[0066] The road bump characteristic value Q Δt and the preset warning turbulence characteristic value Q std Make a comparison;

[0067] If there is Q Δt ≥Q std , indicating that the road smoothness of the corresponding road section may not meet the standard, executing step S3, and simultaneously analyzing the information collected by the acceleration sensor and the noise sensor in the next preset time period;

[0068] On the contrary, the information collected by the acceleration sensor and the noise sensor in the next preset time period is analyzed.

[0069] The process of obtaining the road bump characteristic value corresponding to the road section traveled by the mobile vehicle within the preset time period includes:

[0070]

[0071] The road bump characteristic value Q corresponding to the driving section of the mobile vehicle within the preset time period is obtained by analysis and calculation through formula (1): Δt ;

[0072] Among them, [t1, t2] is a preset time period, which is selected based on experience, Δt=t2-t1, τ1 and τ2 are weight coefficients corresponding to the factors affecting the road bumpy characteristic value, which can be selected based on the experimental analysis of the road bumpy characteristic value during the driving process of the mobile vehicle.

[0073] Through the above technical solution, this embodiment provides a method for generating a warning signal of the risk of non-compliance with the road flatness of the corresponding road section. First, the acceleration change curve A over time is obtained by fitting and modeling the information collected by the acceleration sensor and the noise sensor. s (t) and the noise decibel value changing with time curve L s (t), and then the road bump characteristic value Q corresponding to the driving section of the mobile vehicle in each preset time period is calculated by formula (1): Δt , when there is a road bump value Q on a certain road section Δt Greater than or equal to the preset warning turbulence characteristic value Q std When the road roughness of the corresponding road section may not meet the standard, step S3 is executed to further analyze the road roughness of the road section, wherein the preset warning bump characteristic value Q std It can be obtained by experimental analysis of the characteristic value of road bumps during the driving of a mobile vehicle. This can effectively reduce the computing power demand of the analysis and control module in the process of road flatness detection, reduce the cost of road flatness detection and reduce the risk of the analysis and control module "crashing".

[0074] In one embodiment, the process of generating a warning signal indicating that the road section needs to be repaired includes:

[0075] S31. For all coordinate information collected by the laser scanner at a preset frequency within a preset time period [t1, t2], a rectangular coordinate system is sequentially established for analysis. The preset frequency can be selected based on experience. All coordinate information can be quickly stored in the built-in storage space of the analysis control module during the collection process to obtain a road transverse surface curve corresponding to each collection node. The road transverse surface curve can have the road transverse width as the X-axis and the vertical distance from each laser landing point on the road surface to the laser emission point of the laser scanner as the Y-axis. Both the road transverse width and the vertical distance can be collected and obtained by the laser scanner;

[0076] S32, sequentially analyzing the road transverse surface curve graphs corresponding to each acquisition node to obtain the road transverse flatness parameters corresponding to each acquisition node of the road section, wherein each acquisition node is a time node at which the laser scanner acquires the road surface coordinates at a preset frequency;

[0077] S33, comparing and analyzing the road transverse surface curve graphs corresponding to each acquisition node to obtain the longitudinal smoothness parameter of the road section. It should be noted that since the mobile vehicle may experience horizontal fluctuations during rapid driving on the highway, the transverse surface curve graphs of the roads collected by the laser scanner are not uniformly detected at the same height. However, the comparison and analysis between the transverse surface curve graphs of the roads can also provide feedback on the longitudinal smoothness of the road.

[0078] S34, obtaining a road smoothness reference coefficient of the road section by analyzing the road transverse smoothness parameters and the road longitudinal smoothness parameters corresponding to each acquisition node of the road section;

[0079] S35. Compare the road smoothness reference coefficient of the road section with a preset road smoothness hazard reference coefficient, wherein the preset road smoothness hazard reference coefficient can be preset based on experience; if the road smoothness reference coefficient of the road section is greater than or equal to the preset road smoothness hazard reference coefficient, it means that the road smoothness of the road section does not meet the standard and the road needs to be repaired, and generate a warning signal indicating that the road section needs to be repaired.

[0080] In step S32, the process of obtaining the road transverse flatness parameters corresponding to each acquisition node of the road section includes:

[0081] In the preset time period [t1, t2], the laser scanner collects N road transverse surface curve graphs, and obtains M Y-axis corresponding coordinate data at equal intervals along the X axis for each road transverse surface curve graph;

[0082]

[0083] The road transverse flatness parameter σ corresponding to the i-th collection node is obtained by analysis and calculation through formula (2): i ;

[0084] Among them, i∈[1,N], j∈[1,M], W i,j is the coordinate data corresponding to the j-th Y axis obtained in the i-th road transverse surface curve graph, obtained by taking Y values ​​at equal intervals on the i-th road transverse surface curve graph, is the average value of all Y-axis corresponding coordinates obtained in the i-th road transverse surface curve graph, which can be obtained by averaging all Y values ​​obtained from the i-th road transverse surface curve graph.

[0085] In step S33, the process of obtaining the longitudinal roughness parameter of the road section includes:

[0086]

[0087] The longitudinal roughness parameter ρ of the road section is obtained by analyzing formulas (3)-(5): s ;

[0088] Among them, E i is the area characteristic parameter of the i-th road transverse surface curve, f i (l) is the i-th road transverse surface curve, obtained by fitting and modeling in step S31, is the average area characteristic parameter of N road transverse surface curves within a preset time period, and is obtained by averaging the area characteristic parameters of all road transverse surface curves.

[0089] In step S34, the process of obtaining the road smoothness reference coefficient of the road section includes:

[0090]

[0091] The road roughness reference coefficient P of the road section is obtained by analyzing formula (6): s ;

[0092] Among them, ω1 and ω2 are weight coefficients corresponding to the influencing parameters of the road smoothness reference coefficient, which can be selected and obtained according to the experimental analysis of the road smoothness reference coefficient.

[0093] Through the above technical scheme, this embodiment provides a method for detecting whether a road section needs to be repaired. By analyzing the data collected by the laser scanner, the road transverse flatness parameters corresponding to each collection node of the road section and the longitudinal flatness parameters of the entire road are obtained, and then the road flatness reference coefficient of the road section is obtained. The road flatness reference coefficient of the road section is compared with the preset road flatness hazard reference coefficient. If the road flatness reference coefficient of the road section is greater than or equal to the preset road flatness hazard reference coefficient, it means that the road flatness of the road section does not meet the standard and the road needs to be repaired. A warning signal that the road section needs to be repaired is generated. In this way, the road section whose road flatness may not meet the standard detected in step S2 can be further analyzed, thereby achieving the purpose of flatness detection of highways based on laser scanning technology. It is suitable for flatness detection of highways during continuous traffic use.

[0094] In one embodiment, the method further comprises:

[0095] S4. First, a comprehensive analysis is performed on all sections of the preset detection road for which a road flatness reference coefficient is generated by means of an analysis control module. The preset detection road can be selected and obtained according to the detection section required for road flatness detection. Then, all generated road flatness reference coefficients are sequentially compared with preset road flatness warning reference coefficients. The preset road flatness warning reference coefficient can be preset and obtained according to experience, and its value is less than the preset road flatness danger reference coefficient. Then, the number of sections whose road flatness reference coefficient is greater than or equal to the preset road flatness warning reference coefficient is counted. If the number K≥G*α obtained by statistics, it indicates that the overall flatness of the preset detection road is poor and the preset detection road needs to be maintained to extend the service life of the expressway. Then, a warning signal indicating that the preset detection road needs to be maintained is generated. In this way, the purpose of detecting the road flatness of the expressway can also be achieved.

[0096] Among them, K is the number of sections in the preset detection road where the road smoothness reference coefficient obtained from all analyses is greater than or equal to the preset road smoothness warning reference coefficient, which can be obtained by counting the number of sections where the road smoothness reference coefficient is greater than or equal to the preset road smoothness warning reference coefficient. G is the total number of detection sections in the preset detection road, which can be obtained by counting the road bumpy characteristic values ​​obtained from the analysis in step S2. α is a preset proportion coefficient, which is preset based on experience.

[0097] See also Figure 3 As shown, in one embodiment, a road roughness detection system based on laser scanning is provided, which is used in a road roughness detection method based on laser scanning, and the system includes:

[0098] Mobile vehicle, a vehicle used to move on the road at a preset speed, generally an ordinary car is used as the mobile vehicle;

[0099] A pan / tilt platform is installed on the mobile vehicle to ensure that the equipment installed thereon is always kept level, which can ensure that the laser scanner does not tilt during the collection of road surface coordinate data, thereby avoiding deviation of the collected road surface coordinate data;

[0100] A laser scanner, mounted on the pan-tilt platform, is used to scan the road surface and collect the coordinate information of the road surface. The coordinate data of the road surface can be collected at a preset frequency;

[0101] An acceleration sensor, mounted on the laser scanner, for collecting acceleration information in the up and down directions of the laser scanner;

[0102] A noise sensor is installed near the tires of the mobile vehicle and is used to collect noise information generated by the tires and the road surface during the movement of the mobile vehicle;

[0103] Early warning module, used to execute early warning signals for production;

[0104] The analysis control module is used to analyze the information collected by the acceleration sensor and the noise sensor to obtain the road bump characteristic value. When the road bump characteristic value exceeds the preset warning bump characteristic value, the coordinate information corresponding to the road section collected by the laser scanner is analyzed to determine whether a warning signal is generated that the road section needs to be repaired. A comprehensive analysis is performed on all sections in the preset detection road that generate road flatness reference coefficients to determine whether a warning signal is generated that the preset detection road needs maintenance, thereby achieving the purpose of laser scanning technology to detect the flatness of the highway. During the collection process, the mobile vehicle can be kept driving at high speed on the highway while fully detecting and analyzing the flatness of the highway, and the "freeze" caused by too much data that the analysis control module needs to process can be avoided.

[0105] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A road roughness detection method based on laser scanning, characterized in that: The method comprises: S1, controlling the mobile vehicle to move at a preset speed on the road, the laser scanner collects the coordinate information of the road surface at a preset frequency, the acceleration sensor collects the acceleration information of the laser scanner in the up and down directions in real time, and the noise sensor collects the noise information generated by the tire and the road surface in real time; S2, the analysis control module analyzes the information collected by the acceleration sensor and the noise sensor to obtain a road bump characteristic value. When the road bump characteristic value exceeds the preset warning bump characteristic value, it indicates that the road flatness of the corresponding road section may not meet the standard, and step S3 is executed; S3, the analysis control module analyzes the information collected by the laser scanner of the road section corresponding to the warning signal generated in step S2, obtains the road smoothness reference coefficient of the road section, and determines whether the road smoothness of the road section meets the standard. If not, a warning signal is generated indicating that the road section needs to be repaired.

2. The road roughness detection method based on laser scanning according to claim 1, characterized in that: The process of obtaining that the road smoothness of the corresponding road section may not meet the standard includes: According to the acceleration value collected by the acceleration sensor and the noise decibel value collected by the noise sensor, the acceleration change curve A over time is obtained respectively. s (t) and the noise decibel value changing with time curve L s (t); During the preset time period, the acceleration time curve A s (t) and the noise decibel value changing with time curve L s (t) Analyze and obtain the road bump characteristic value Q corresponding to the driving section of the mobile vehicle within the preset time period Δt ; The road bump characteristic value Q Δt and the preset warning turbulence characteristic value Q std Make a comparison; If there is Q Δt ≥Q std , indicating that the road smoothness of the corresponding road section may not meet the standard, executing step S3, and simultaneously analyzing the information collected by the acceleration sensor and the noise sensor in the next preset time period; On the contrary, the information collected by the acceleration sensor and the noise sensor in the next preset time period is analyzed.

3. The road roughness detection method based on laser scanning according to claim 2 is characterized in that: The process of obtaining the road bump characteristic value corresponding to the road section traveled by the mobile vehicle within the preset time period includes: The road bump characteristic value Q corresponding to the driving section of the mobile vehicle within the preset time period is obtained by analysis and calculation through formula (1): Δt ; Wherein, [t1, t2] is a preset time period, Δt=t2-t1, τ1 and τ2 are weight coefficients corresponding to the factors affecting the road bumpy characteristic value.

4. The road roughness detection method based on laser scanning according to claim 2 is characterized in that: The process of generating the warning signal that the road section needs to be repaired includes: S31, establishing a rectangular coordinate system for analyzing all coordinate information collected by the laser scanner at a preset frequency within a preset time period [t1, t2], and obtaining a road transverse surface curve diagram corresponding to each collection node; S32, analyzing the road transverse surface curve graphs corresponding to each collection node in turn to obtain the road transverse flatness parameters corresponding to each collection node of the road section; S33, comparing and analyzing the road transverse surface curve graphs corresponding to each acquisition node to obtain the road longitudinal smoothness parameters of the road section; S34, obtaining a road smoothness reference coefficient of the road section by analyzing the road transverse smoothness parameters and the road longitudinal smoothness parameters corresponding to each acquisition node of the road section; S35. Compare the road smoothness reference coefficient of the road section with the preset road smoothness danger reference coefficient. If the road smoothness reference coefficient of the road section is greater than or equal to the preset road smoothness danger reference coefficient, generate a warning signal that the road section needs to be repaired.

5. The road roughness detection method based on laser scanning according to claim 4 is characterized in that: In step S32, the process of obtaining the road transverse flatness parameters corresponding to each acquisition node of the road section includes: In the preset time period [t1, t2], the laser scanner collects N road transverse surface curve graphs, and obtains M Y-axis corresponding coordinate data at equal intervals along the X axis for each road transverse surface curve graph; The road transverse flatness parameter σ corresponding to the i-th collection node is obtained by analysis and calculation through formula (2): i ; Among them, i∈[1,N], j∈[1,M], W i,j is the coordinate data corresponding to the j-th Y axis obtained in the i-th road transverse surface curve diagram, It is the average value of all Y-axis corresponding coordinates obtained in the i-th road transverse surface curve diagram.

6. The road roughness detection method based on laser scanning according to claim 5 is characterized in that: In step S33, the process of obtaining the longitudinal roughness parameter of the road section includes: The longitudinal roughness parameter ρ of the road section is obtained by analyzing formulas (3)-(5): s ; Among them, E i is the area characteristic parameter of the i-th road transverse surface curve, f i (l) is the lateral surface curve of the i-th road, It is the average area characteristic parameter of N road transverse surface curves within a preset time period.

7. The road roughness detection method based on laser scanning according to claim 6 is characterized in that: In step S34, the process of obtaining the road smoothness reference coefficient of the road section includes: The road roughness reference coefficient P of the road section is obtained by analyzing formula (6): s ; Among them, ω1 and ω2 are weight coefficients corresponding to the parameters affecting the road smoothness reference coefficient.

8. The road roughness detection method based on laser scanning according to claim 1, characterized in that: The method further comprises: S4. Comprehensively analyzing all sections of the preset detection road for which the road flatness reference coefficients are generated by the analysis control module, comparing all the generated road flatness reference coefficients with the preset road flatness warning reference coefficients in turn, and counting the number of sections for which the road flatness reference coefficients are greater than or equal to the preset road flatness warning reference coefficients. If the number K obtained by the statistics is greater than or equal to G*α, a warning signal indicating that the preset detection road needs maintenance is generated; Among them, K is the number of sections in the preset detection road where the road flatness reference coefficient is greater than or equal to the preset road flatness warning reference coefficient, G is the total number of detection sections in the preset detection road, and α is the preset proportion coefficient.

9. A road roughness detection system based on laser scanning, characterized in that: A road roughness detection method based on laser scanning according to any one of claims 1 to 8, the system comprising: Mobile vehicles are vehicles used to move on roads at a preset speed; A pan / tilt head, installed on the mobile vehicle, to ensure that the equipment installed thereon always remains level; A laser scanner, mounted on the pan-tilt platform, is used to scan the road surface and collect the coordinate information of the road surface; An acceleration sensor, mounted on the laser scanner, for collecting acceleration information in the up and down directions of the laser scanner; A noise sensor is installed near the tires of the mobile vehicle and is used to collect noise information generated by the tires and the road surface during the movement of the mobile vehicle; Early warning module, used to execute early warning signals for production; The analysis control module is used to analyze the information collected by the acceleration sensor and the noise sensor to obtain the road bump characteristic value. When the road bump characteristic value exceeds the preset warning bump characteristic value, the coordinate information corresponding to the road section collected by the laser scanner is analyzed to determine whether a warning signal is generated that the road section needs to be repaired. A comprehensive analysis is performed on all sections in the preset detection road that generate road flatness reference coefficients to determine whether a warning signal is generated that the preset detection road needs maintenance.

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