A method for detecting road flatness for driverless mining trucks

Abstract

The present invention provides a method for detecting the road flatness of driverless mining trucks, belonging to the technical field of road detection. The method includes: designing the original elevation data of the path corresponding to the starting point and the target point of the driving path of the driverless mining truck, and establishing a three-dimensional road surface model, wherein the three-dimensional road surface model is used to reflect the elevation of each position point on the road surface; dividing the three-dimensional road surface model into multiple sub-regions, collecting the three-dimensional data of each sub-region at the current moment, and splicing the three-dimensional data of the adjacent sub-regions collected; dividing the road sections according to the splicing result, determining the road surface hierarchical structure of each divided road section; analyzing the cross-section line of the road surface hierarchical structure to determine the flatness of the corresponding divided road section, and further determining the comprehensive flatness of the designed path, so as to realize the detection of road flatness. It solves the problem that the road flatness cannot be detected in real time, reducing the safety of the mining truck driving.

Description

Technical Field

[0001] The present invention relates to the technical field of road detection, and particularly relates to a road flatness detection method for driverless mining trucks. Background Art

[0002] At present, the detection and evaluation of road surface quality are very important. After the road is paved, due to reasons such as terrain settlement, deformation, rain erosion, vehicle driving, and heavy object rolling, it is easy to cause deformation of the roadside of the road, forming pits or protrusions, etc., which cause road deformation, and then affect the safety of driverless mining trucks driving on the road surface. Therefore, it is necessary to detect the flatness of the road for a long time. The traditional 3M ruler detection method is used for the detection of road flatness. A ruler with a length of 3 meters is placed on the road to check the fitting situation between the ruler and the road surface to judge the flatness of the road surface. However, this method can only judge the flatness problem of the road surface in the area within 3 meters, and cannot detect the area beyond 3 meters. Due to the lack of standards, the detection results have large errors, and a large amount of manpower and material resources are required, and it is impossible to perform real-time detection of road flatness, reducing the safety of mining truck driving.

[0003] Therefore, the present invention proposes a road flatness detection method for driverless mining trucks. Summary of the Invention

[0004] The present invention provides a road flatness detection method for driverless mining trucks. By dividing the constructed three-dimensional road surface model into multiple sub-regions, collecting the three-dimensional data of the sub-regions, splicing the collected three-dimensional data of adjacent sub-regions, dividing the splicing result into road sections, determining the road surface hierarchy structure of each road section, analyzing the cross-section line of the road surface hierarchy structure, determining the flatness of each road section, and thus determining the comprehensive flatness of the designed path, realizing road flatness detection, so as to solve the problems in the background art that due to the lack of standards, the detection results have large errors, and a large amount of manpower and material resources are required, and it is impossible to perform real-time detection of road flatness, reducing the safety of mining truck driving.

[0005] The present invention proposes a road flatness detection method for driverless mining trucks, and the method includes:

[0006] Step 1: Establish a three-dimensional road surface model according to the original elevation data of the designed path corresponding to the starting point and the target point of the driving path of the driverless mining truck, wherein the three-dimensional road surface model is used to reflect the elevation of each position point on the road surface;

[0007] Step 2: Divide the three-dimensional road surface model into multiple sub-regions, collect the three-dimensional data of each sub-region at the current moment, and splice the collected three-dimensional data of adjacent sub-regions;

[0008] Step 3: Perform road section division based on the splicing result to determine the pavement hierarchical structure of each divided road section;

[0009] Step 4: Analyze the cross-section line of the pavement hierarchical structure to determine the flatness of the corresponding divided road section, and then determine the comprehensive flatness of the designed path to achieve road flatness detection.

[0010] Preferably, based on the original elevation data of the designed path corresponding to the starting point and the target point of the driving path of the driverless mining truck, a three-dimensional pavement model is established, including:

[0011] Obtain the two-dimensional drawing of the designed path corresponding to the starting point and the target point of the driving path of the driverless mining truck;

[0012] Extract the center line and multiple other closed area blocks corresponding to the designed path from the two-dimensional drawing;

[0013] Mark multiple points on the center line, obtain the original elevation data of the multiple marked points, and generate a three-dimensional center line model of the designed path based on the center line, the multiple marked points, and the original elevation data of the multiple marked points;

[0014] Construct a three-dimensional pavement model of the designed path based on the positional relationship between the three-dimensional center line model and each other closed area block.

[0015] Preferably, divide the three-dimensional pavement model into multiple sub-regions, collect the three-dimensional data of each sub-region at the current moment, and splice the three-dimensional data of adjacent sub-regions collected, including:

[0016] Divide the three-dimensional pavement model along the driving direction and the measurement width direction into multiple sub-regions;

[0017] Determine the traffic flow parameter of each sub-region, and set the three-dimensional data collection period of each sub-region according to the traffic flow parameter;

[0018] Real-time detect and collect the three-dimensional data of the current moment of each sub-region according to the three-dimensional data collection period of each sub-region;

[0019] Splice the three-dimensional data collected at the same moment of adjacent regions.

[0020] Preferably, real-time detect and collect the three-dimensional data of the current moment of each sub-region according to the three-dimensional data collection period of each sub-region, including:

[0021] Perform pavement component characteristic analysis on each sub-region, and select a low-pass filter for each sub-region according to the analysis result;

[0022] Set the filtering parameters of the selected low-pass filter for each sub-region according to the three-dimensional data acquisition period of each sub-region;

[0023] Perform frequency-domain low-pass filtering on each sub-region based on the low-pass filter with the set filtering parameters for each sub-region, and obtain the low-frequency component data and high-frequency component data of each sub-region;

[0024] Obtain the slow deformation information of each sub-region based on the low-frequency component data, and at the same time obtain the local drastic change information and texture feature information of each sub-region based on the high-frequency component data;

[0025] Obtain the three-dimensional data of each sub-region based on the slow deformation information, local drastic change information, and texture feature information of each sub-region.

[0026] Preferably, perform road section division according to the stitching result, and determine the pavement hierarchical structure of each divided road section, including:

[0027] Divide the design path into multiple pavement sections based on a preset interval distance according to the stitching result;

[0028] Obtain the pavement description parameters of each pavement section according to the three-dimensional data of each pavement section, and determine the pavement structure layer type of each pavement section according to the pavement description parameters;

[0029] Retrieve the hierarchical structure data of the pavement section of this type according to the pavement structure layer type of each pavement section;

[0030] Perform data mapping on the hierarchical structure data of each pavement section to determine the pavement hierarchical structure of each pavement section.

[0031] Preferably, perform cross-section line analysis on the pavement hierarchical structure to determine the flatness of the corresponding divided road section, and further determine the comprehensive flatness of the design path to realize road flatness detection, including:

[0032] Obtain the structure data corresponding to the pavement hierarchical structure and perform preprocessing of smoothing filtering on it to obtain the current structure data after preprocessing;

[0033] Compare the current structure data with the standard structure data to determine the complete hierarchical structure and incomplete hierarchical structure in the pavement hierarchical structure;

[0034] Obtain the target pavement data of the incomplete structural hierarchical structure, and determine the cross-section line distribution of the incomplete structural hierarchical structure according to the data distribution rule of the target pavement data;

[0035] Determine the flatness of each divided road section according to the cross-section line distribution, and perform weighted calculation according to the road section area of each divided road section and the flatness of this divided road section to determine the comprehensive flatness of the design path.

[0036] Preferably, determining the distribution of the cross-section lines of the incomplete structural hierarchy according to the data distribution rule of the target road surface data includes:

[0037] Determining abnormal road surface data according to the data distribution rule of the target road surface data;

[0038] Obtaining the longitudinal section profile of the road surface based on the abnormal road surface data;

[0039] Obtaining the longitudinal section line of the road surface based on the longitudinal section profile of the road surface;

[0040] Determining the distribution of the cross-section lines of the incomplete structural hierarchy according to the distribution position of the longitudinal section line of the road surface in the incomplete structural hierarchy.

[0041] Preferably, after dividing the road sections according to the splicing result and determining the road surface hierarchy of each divided road section, before analyzing the cross-section lines of the road surface hierarchy to determine the flatness of the corresponding divided road section, and then determining the comprehensive flatness of the designed path to realize road flatness detection, it further includes:

[0042] Obtaining the road surface data corresponding to the road surface hierarchy of each divided road section;

[0043] Classifying the road surface data of each divided road section according to the preset road surface design attributes to generate data clusters;

[0044] Respectively obtaining the data attributes corresponding to each data cluster;

[0045] Constructing an analysis sequence for each data cluster, and placing the data cluster on the analysis sequence for analysis to obtain an analysis result;

[0046] Obtaining the sequence attributes of each data cluster according to the analysis result, and generating an attribute comparison chain based on the sequence attributes and data attributes of each data cluster;

[0047] Determining the abnormal attribute mapping nodes in the attribute comparison chain of each data cluster;

[0048] Obtaining multiple data elements of the status data corresponding to the abnormal attribute mapping nodes;

[0049] Obtaining the logical mapping relationship between adjacent two data elements, and analyzing the connection degree between adjacent two data elements according to the logical mapping relationship;

[0050] If the connection degree is less than the preset index, determining that the status data corresponding to the abnormal attribute mapping node is abnormal, otherwise, determining that the status data corresponding to the abnormal attribute mapping node is normal;

[0051] On the basis of determining the abnormal state data corresponding to the abnormal attribute mapping nodes, defect state data is obtained, and the defect state data is corrected to obtain a correction result;

[0052] According to the correction result, the corrected road surface data of each divided section is obtained, and the corrected road surface data is used as sample data for cross-section line analysis of the road surface hierarchy.

[0053] Preferably, after dividing the three-dimensional road surface model into multiple sub-regions, collecting three-dimensional data of each sub-region at the current moment, and before splicing the three-dimensional data of adjacent sub-regions collected, it further includes:

[0054] Obtain the road surface structure and road surface surrounding reference building data of each sub-region, evaluate the complexity of the divided road conditions of each sub-region according to the road surface structure, and determine the visual impact coefficient of the divided road surface of each sub-region based on the road surface surrounding reference building data;

[0055] Determine the curve distribution and straight road distribution of the divided road surface of each sub-region, and determine the set of spatial direct viewing angle changes from beginning to end of the divided road surface of each sub-region according to the curve distribution and straight road distribution;

[0056] Determine the spatial direction aggregation feature and spatial range perception feature of the divided road surface of the sub-region according to the set of spatial direct viewing angle changes from beginning to end of the divided road surface of each sub-region;

[0057] Calculate the qualification index of the divided road conditions of each sub-region according to the visual impact coefficient, divided road condition complexity of the divided road surface of each sub-region, and the spatial direction aggregation feature and spatial range perception feature of the divided road surface of the sub-region:

[0058] ; where represents the qualification index of the divided road conditions of the i-th sub-region, represents the visual impact coefficient of the divided road surface of the i-th sub-region, represents the visual visible range of the divided road surface of the i-th sub-region, represents the visual blind spot range of the divided road surface of the i-th sub-region, represents the complexity of the divided road conditions of the divided road surface of the i-th sub-region, represents the spatial direction aggregation feature of the divided road surface of the i-th sub-region, represents the spatial range perception feature of the divided road surface of the i-th sub-region, represents the openness index of the divided road surface of the i-th sub-region;

[0059] If the qualification index of the road condition of each sub-region is greater than or equal to the preset index, it is determined that the road condition of the sub-region is qualified; otherwise, it is determined that the road condition of the sub-region is unqualified.

[0060] Re-divide the target sub-region with unqualified road condition.

[0061] Other features and advantages of the present invention will be described in the following specification, and in part will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the written specification, claims, and drawings.

[0062] The technical solutions of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0063] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0064] Figure 1 is a flowchart of a method for detecting road flatness of an unmanned mining truck in an embodiment of the present invention;

[0065] Figure 2 is another flowchart of a method for detecting road flatness of an unmanned mining truck in an embodiment of the present invention. Detailed Embodiments

[0066] The following describes the preferred embodiments of the present invention with reference to the drawings. It should be understood that the preferred embodiments described herein are only used to explain and illustrate the present invention and are not used to limit the present invention.

[0067] Embodiment 1:

[0068] The present invention provides a method for detecting road flatness of an unmanned mining truck. As Figure 1 shown, the method includes:

[0069] Step 1: Design the original elevation data of the path corresponding to the starting point and the target point of the driving path of the unmanned mining truck, and establish a three-dimensional road surface model, where the three-dimensional road surface model is used to reflect the elevation of each position point on the road surface;

[0070] Step 2: Divide the three-dimensional road surface model into multiple sub-regions, collect the three-dimensional data of each sub-region at the current moment, and splice the three-dimensional data of the adjacent sub-regions collected;

[0071] Step 3: Perform road section division according to the splicing result, and determine the road surface layer structure of each divided road section.

[0072] Step 4: Conduct a cross-sectional line analysis on the road surface hierarchy, determine the flatness of the corresponding divided sections, and then determine the comprehensive flatness of the designed path to achieve road flatness detection.

[0073] In this embodiment, the elevation data refers to the distance from a point along the plumb line to the base surface.

[0074] In this embodiment, the 3D road surface model reflects the positional relationship between the center line and other closed areas. For example, the green belt is 45 degrees south of north along the traveling direction of the center line, and the motor vehicle lanes are on both sides of the center line, adjacent to the center line.

[0075] In this embodiment, the multiple sub-regions refer to dividing the road according to a preset width and length, where the preset width is 5 meters and the preset length is 20 meters.

[0076] In this embodiment, splicing the 3D data means splicing the data of adjacent regions. For example, if region A and region B are adjacent, splicing the vehicle track data of the two regions can observe whether there is an uneven road surface in the two parts.

[0077] In this embodiment, flatness means that the road surface is not absolutely flat, and the difference data between the unevenness and the standard level is the flatness.

[0078] In this embodiment, according to the proportion of the road surface flatness of each section in the section area, the higher the proportion, the higher the road surface flatness of each section, and the higher the weight of the section in the comprehensive flatness of the designed path can be obtained.

[0079] Among them, the smaller the difference between the road surface flatness and the standard road surface flatness, the higher the road surface flatness and the flatter the road surface.

[0080] In this embodiment, the 3D data includes vehicle track data, texture data, and crack data of the road surface.

[0081] In this embodiment, the road surface hierarchy refers to the cushion, base course, and surface course of the road surface. The surface course is generally asphalt or cement concrete, and also includes the depression degree and protrusion condition of the road surface.

[0082] In this embodiment, the cross-sectional line is the contour line of cracks or depressions on the road surface, which can show the undulations of the straight and curved surfaces of the road.

[0083] In this embodiment, the cross-section line analysis of the road surface hierarchy refers to obtaining the structural data corresponding to the road surface hierarchy and performing preprocessing of smoothing filtering on it to obtain the current structural data after preprocessing. Comparing the current structural data with the standard structural data to determine the complete hierarchy and incomplete hierarchy in the road surface hierarchy, obtaining the target road surface data of the incomplete structural hierarchy, determining the cross-section line distribution of the incomplete structural hierarchy according to the data distribution rule of the target road surface data, and determining the flatness of each divided road section according to the cross-section line distribution.

[0084] If the range of the cross-section line distribution is wide, it indicates that the road section is uneven.

[0085] The beneficial effects of the above technical solution are as follows: By establishing a three-dimensional model and performing regional division, real-time collection of three-dimensional data for each region, splicing the three-dimensional data of adjacent sub-regions, determining the road surface hierarchy, and performing cross-section line analysis on the road surface hierarchy, the road flatness can be detected in real time, greatly improving the safety of driverless mining trucks when driving on the road.

[0086] Embodiment 2:

[0087] The present invention provides a method for detecting the road flatness of a driverless mining truck. As Figure 2 shown, according to the starting point of the driving path and the target point of the driving path of the driverless mining truck, the original elevation data of the corresponding designed path is designed, and a three-dimensional road surface model is established, including:

[0088] S01: Obtain the two-dimensional drawing of the designed path corresponding to the starting point of the driving path and the target point of the driving path of the driverless mining truck;

[0089] S02: Extract the center line and multiple other closed area blocks corresponding to the designed path according to the two-dimensional drawing;

[0090] S03: Mark multiple points on the center line, obtain the original elevation data of multiple marked points, and generate a three-dimensional center line model of the designed path according to the center line, multiple marked points and the original elevation data of multiple marked points;

[0091] S04: Construct a three-dimensional road surface model of the designed path based on the positional relationship between the three-dimensional center line model and each other closed area block.

[0092] In this embodiment, determine the starting point and target point of the driving route of the driverless mining truck, design the driving direction and driving route, and according to the plane drawing of the driving direction and driving route.

[0093] In this embodiment, the center line is the line where the road in the middle of the designed path is located.

[0094] In this embodiment, the multiple other closed area blocks refer to motor vehicle lanes, sidewalks, and green belts.

[0095] In this embodiment, the three-dimensional center line model is a three-dimensional solid model representing the line where the road in the very middle of the road is located.

[0096] In this embodiment, the three-dimensional road surface model reflects the positional relationship between the center line and other closed areas. For example, the green belt is at 45 degrees south by east along the traveling direction of the center line, and the motor vehicle lanes are on both sides of the center line, adjacent to the center line.

[0097] The beneficial effects of the above technical solution are as follows: By extracting the center line and multiple other closed area blocks from the two-dimensional drawing of the designed path, marking the points on the center line, generating a three-dimensional center line model based on the marked points and the elevation data of the marked points, and constructing a three-dimensional road surface model based on the positional relationship between the three-dimensional center line model and each other closed area block, the road can be intuitively represented by a model, which is convenient for regional division of the road.

[0098] Embodiment 3:

[0099] The present invention provides a method for detecting the road flatness of an unmanned mining vehicle. The three-dimensional road surface model is divided into multiple sub-regions, and the three-dimensional data of each sub-region at the current moment is collected, and the three-dimensional data of adjacent sub-regions collected is spliced, including:

[0100] The three-dimensional road surface model is divided along the driving direction and the measurement width direction into multiple sub-regions;

[0101] Determine the traffic flow parameters of each sub-region, and set the three-dimensional data collection period of each sub-region according to the traffic flow parameters;

[0102] Detect and collect the three-dimensional data of the current moment of the sub-region in real time according to the three-dimensional data collection period of each sub-region;

[0103] Splice the three-dimensional data collected at the same moment of adjacent regions.

[0104] In this embodiment, the driving direction can be straight from south to north.

[0105] In this embodiment, the measurement width direction is the direction of measuring the width of the road horizontally, and can be from east to west.

[0106] Among them, whether it is the driving direction or the measurement width direction, the three-dimensional road surface model is divided by a preset length and width. The preset length is 20 meters, and the preset width is 5 meters.

[0107] In this embodiment, the traffic flow parameter refers to the number of vehicles passing through each sub-region within a certain period of time. For example, 5 vehicles pass through region A within 30 minutes.

[0108] In this embodiment, the acquisition period is 30 minutes.

[0109] Among them, the greater the traffic flow, the shorter the acquisition period. Because the more vehicles pass through, the higher the degree of wear on the road surface, so the acquisition frequency also needs to be higher.

[0110] The beneficial effects of the above technical solution are as follows: The three-dimensional road surface model is divided into multiple sub-regions according to the driving direction and the measurement width direction, and the three-dimensional data acquisition period of each sub-region is set according to the traffic flow parameter of each sub-region to perform real-time detection on the three-dimensional data of each sub-region, and the three-dimensional data of each sub-region at the current moment can be obtained, which can make the data updated quickly and facilitate the efficient detection of road flatness.

[0111] Embodiment 4:

[0112] The present invention provides a method for detecting road flatness for driverless mining trucks, which detects and acquires the three-dimensional data of each sub-region at the current moment in real time according to the three-dimensional data acquisition period of each sub-region, including:

[0113] Perform an analysis on the pavement composition characteristics of each sub-region, and select a low-pass filter for each sub-region according to the analysis results;

[0114] Set the filtering parameters of the selected low-pass filter for each sub-region according to the three-dimensional data acquisition period of each sub-region;

[0115] Based on the low-pass filter with the set filtering parameters for each sub-region, perform frequency-domain low-pass filtering on the sub-region to obtain the low-frequency component data and high-frequency component data of each sub-region;

[0116] Obtain the slow deformation information of each sub-region based on the low-frequency component data, and at the same time obtain the local drastic change information and texture feature information of each sub-region based on the high-frequency component data;

[0117] Obtain the three-dimensional data of the sub-region based on the slow deformation information, local drastic change information, and texture feature information of each sub-region.

[0118] In this embodiment, the pavement composition characteristics refer to the composition components of the road surface, such as asphalt, tarmac road, or concrete.

[0119] In this embodiment, the low-pass filter is a filter that can pass signals with frequencies lower than the selected cut-off frequency and attenuate signals with frequencies higher than the cut-off frequency.

[0120] In this embodiment, the filtering parameter refers to the attenuation frequency of the low-pass filter.

[0121] In this embodiment, the low-frequency component data includes the slow deformation information of each sub-region, where the slow deformation information includes the tire marks of the wheels and the footprints of pedestrians.

[0122] In this embodiment, the high-frequency component data includes the local drastic change information and texture feature information of each sub-region. The local drastic change information includes deep pits and cracks, and the texture feature information includes vibration information. According to the vibration amplitude, it can be obtained whether the road surface texture is flat or has lines.

[0123] The beneficial effects of the above technical solution are as follows: By selecting the low-pass filter of each sub-region and setting the filtering parameter, performing frequency-domain low-pass filtering on the sub-region, obtaining the low-frequency component data and high-frequency component data of each sub-region, and obtaining the three-dimensional data of each sub-region, the real-time three-dimensional data of each sub-region can be accurately obtained, making the measurement result more accurate.

[0124] Embodiment 5:

[0125] The present invention provides a method for detecting the road surface flatness of an unmanned mining truck. According to the splicing result, the road section is divided, and the road surface hierarchical structure of each divided road section is determined, including:

[0126] Based on a preset interval distance, the design path is divided into multiple road surface segments according to the splicing result;

[0127] According to the three-dimensional data of each road surface segment, the road surface description parameters of the road surface segment are obtained, and according to the road surface description parameters, the road surface structure layer types of each road surface segment are determined;

[0128] Data mapping is performed according to the road data of each road surface segment to determine the road surface hierarchical structure of each road surface segment.

[0129] In this embodiment, the preset interval distance is 10 meters.

[0130] In this embodiment, the road surface description parameter is a parameter of the road surface. In order to determine the parameter of the road surface flatness, it includes the composition structure components of the road surface. For example, the middle position of the first road section is 5 cm lower than the normal road surface, which proves that the road surface of this section is uneven and needs to be repaired.

[0131] In this embodiment, the road surface structure layer types are interlocking structure and concrete structure, where the interlocking structure firmly interlocks the gravel by means of external force.

[0132] If the road surface structure layer type is an interlocking structure, then the road surface layer structure mainly consists of asphalt gravel and clay as the main components.

[0133] The beneficial effects of the above technical solution are as follows: By obtaining the road surface parameters of each section, determining the road surface structure type of each road section, and thus obtaining the road surface hierarchy of each road section, the structure layer type of each road section can be quickly obtained, and the selection of materials during repair can be accelerated.

[0134] Embodiment 6:

[0135] The present invention provides a method for detecting the road surface flatness of an unmanned mining truck. By analyzing the cross-section line of the road surface hierarchy, determining the flatness of the corresponding divided sections, and further determining the comprehensive flatness of the designed path, road surface flatness detection is realized, including:

[0136] Obtain the structure data corresponding to the road surface hierarchy and perform smoothing filter preprocessing on it to obtain the current structure data after preprocessing;

[0137] Compare the current structure data with the standard structure data to determine the complete hierarchy and incomplete hierarchy in the road surface hierarchy;

[0138] Obtain the target road surface data of the incomplete structure hierarchy, and determine the cross-section line distribution of the incomplete structure hierarchy according to the data distribution rule of the target road surface data;

[0139] Determine the flatness of each divided section according to the cross-section line distribution, and perform weighted calculation according to the road section area of each divided section and the flatness of the divided section to determine the comprehensive flatness of the designed path.

[0140] In this embodiment, smoothing filtering is to perform filtering processing on the structure data to make the data tend to a stable state, which is convenient for observing the data.

[0141] In this embodiment, the complete hierarchy means that there are no cracks or potholes on the road section.

[0142] In this embodiment, the incomplete hierarchy means that there are cracks or potholes on the road section.

[0143] In this embodiment, the data distribution rule is, for example, the target road surface data is 3, 1, 2, 1, 3, 2, 1, 2, where 3 is the standard road surface data, then 2 and 1 are the irregular road surface data in the data, 1, 2, 1 represents that there is a pothole on the road surface, and there are raised parts in the pothole, and 2, 1, 2 represents that there is a pothole on the road surface.

[0144] Obtain the cross-section line distribution according to the raised parts and pothole parts of the road surface.

[0145] In this embodiment, the weighted calculation is based on the proportion of the road surface flatness of each road section in the road section area. The higher the proportion, the higher the road surface flatness of each road section, and thus the higher the weight of each road section in the comprehensive flatness of the designed path can be obtained.

[0146] Among them, the smaller the difference between the road surface flatness and the standard road surface flatness, the higher the road surface flatness, and the smoother the road surface.

[0147] The beneficial effects of the above technical solution are as follows: By determining the complete hierarchical structure and incomplete hierarchical structure of the road surface to determine the cross-section line distribution of the road surface, obtaining the flatness of each divided road section, and thus determining the comprehensive flatness of the path, the flatness of the road surface can be accurately determined, which increases the safety of the driverless mining truck when driving on the road surface.

[0148] Embodiment 7:

[0149] The present invention provides a method for detecting the road surface flatness of a driverless mining truck. According to the data distribution rule of the target road surface data, the cross-section line distribution of the incomplete structural hierarchical structure is determined, including:

[0150] Determine abnormal road surface data according to the data distribution rule of the target road surface data;

[0151] Obtain the road surface longitudinal section profile based on the abnormal road surface data;

[0152] Obtain the road surface longitudinal section line based on the road surface longitudinal section profile;

[0153] Determine the cross-section line distribution of the incomplete structural hierarchical structure according to the distribution position of the road surface longitudinal section line in the incomplete structural hierarchical structure.

[0154] In this embodiment, the abnormal road surface data refers to data different from the normal road surface data. For example, if the normal road surface data is 4 ± 0.5, then the data measured less than 3.5 or greater than 4.5 belongs to the abnormal road surface data.

[0155] In this embodiment, the longitudinal section profile is where there are potholes or protrusions on the road surface.

[0156] The beneficial effects of the above technical solution are as follows: By obtaining the road surface longitudinal section profile based on the abnormal data of the road surface, thereby obtaining the road surface longitudinal section line, and finally determining the cross-section line distribution of the incomplete structural hierarchical structure, the incomplete structure of the road surface can be accurately obtained, which is convenient for observing the road surface, improving the repair efficiency, and accelerating the repair speed.

[0157] Embodiment 8:

[0158] The present invention provides a method for detecting the road surface flatness of driverless mining vehicles. Before performing road section division based on the splicing result, determining the road surface hierarchy of each divided section, analyzing the cross-section line of the road surface hierarchy to determine the flatness of the corresponding divided section, and further determining the comprehensive flatness of the designed path to achieve road surface flatness detection, the method further includes:

[0159] Obtain the road surface data corresponding to the road surface hierarchy of each divided section;

[0160] Classify the road surface data of each divided section according to the preset road surface design attributes to generate data clusters;

[0161] Respectively obtain the data attributes corresponding to each data cluster;

[0162] Construct an analysis sequence for each data cluster, place the data cluster on the analysis sequence for analysis, and obtain an analysis result;

[0163] Obtain the sequence attributes of each data cluster according to the analysis result, and generate an attribute comparison chain based on the sequence attributes and data attributes of each data cluster;

[0164] Determine the abnormal attribute mapping nodes in the attribute comparison chain of each data cluster;

[0165] Obtain multiple data elements of the status data corresponding to the abnormal attribute mapping nodes;

[0166] Obtain the logical mapping relationship between two adjacent data elements, and analyze the connection degree between two adjacent data elements according to the logical mapping relationship;

[0167] If the connection degree is less than the preset index, determine that the status data corresponding to the abnormal attribute mapping node is abnormal; otherwise, determine that the status data corresponding to the abnormal attribute mapping node is normal;

[0168] On the basis of determining that the status data corresponding to the abnormal attribute mapping node is abnormal, obtain the defect status data, perform data correction on the defect status data, and obtain a correction result;

[0169] Obtain the corrected road surface data of each divided section according to the correction result, and use the corrected road surface data as the sample data for analyzing the cross-section line of the road surface hierarchy.

[0170] In this example, the road surface data can be the coordinate position data of a certain point on the road surface, or the object-related data representing different objects on the road surface. For example, if there is a vehicle parked on the road surface, it is necessary to determine the floor area, width, length, and height of the vehicle.

[0171] In this example, the road surface design attributes represent the basic attributes of different road surfaces, which are divided into qualitative and quantitative. Qualitative attributes include the characteristics, categories, and names of objects on the road surface, such as sidewalks and lanes. Quantitative attributes represent the length, width, and quantity of objects on the road surface. For example, there are 5 sidewalks on the designed path.

[0172] In this example, a data cluster represents a collection of multiple road surface data, and the number of road surface data in each data cluster is not the same.

[0173] In this embodiment, data attributes refer to the attributes of road surface data. For example, some data represents position, some data represents the attributes of things, such as the sidewalk or lane in the middle of the road surface, and some data represents the characteristics of things, such as the width and length of the sidewalk.

[0174] In this embodiment, the analysis sequence represents the carrier for running data clusters, specifically referring to a program that can analyze the data cluster sequence.

[0175] In this embodiment, multiple data elements of status data refer to multiple data included in the abnormal attribute mapping node at a certain point in time.

[0176] In this embodiment, the logical mapping relationship refers to the mapping relationship between two adjacent data. For example, if the second data can be obtained through the first data, then the relationship between these two data is one-to-one.

[0177] In this embodiment, the cohesion refers to the cohesion between two adjacent elements. For example, if the first data and the second data both represent the width of the sidewalk, then the cohesion between these two elements is 100%.

[0178] In this embodiment, the abnormal attribute mapping node represents a node with abnormal attributes.

[0179] In this embodiment, sequence attributes are such that some sequences are a set, representing the distance between each sidewalk, and some sequences are characters, representing the numbers of the locations of each area of multiple regions. For example, the green belt is at position 1.

[0180] In this embodiment, status data anomaly means that the data is incomplete or there are potholes and bumps on the road surface, resulting in data anomalies.

[0181] In this embodiment, the preset index is 90%.

[0182] In this embodiment, defective status data refers to incomplete data that needs to be corrected to complete the incomplete data.

[0183] The beneficial effects of the above technical solution are as follows: By dividing the road surface data into multiple data clusters, determining the sequence attributes and data attributes of each data cluster, obtaining abnormal attribute nodes to determine the abnormally state data, and determining and correcting the defective data in the abnormal data, the road condition of the road surface can be accurately obtained, facilitating the real-time repair of the road surface with abnormal road conditions and improving safety.

[0184] Embodiment 9:

[0185] The present invention provides a method for detecting the road surface flatness of an unmanned mining vehicle. After dividing the three-dimensional road surface model into multiple sub-regions, and before collecting the three-dimensional data of each sub-region at the current moment and splicing the three-dimensional data of adjacent sub-regions collected, it further includes:

[0186] Obtaining the road surface structure and the data of the reference buildings around the road surface of each sub-region, evaluating the complexity of the divided road conditions of each sub-region according to the road surface structure, and determining the visual influence coefficient of the divided road surface of each sub-region based on the data of the reference buildings around the road surface;

[0187] Determining the curve distribution and straight road distribution of the divided road surface of each sub-region, and determining the set of spatial direct viewing angle changes from the beginning to the end of the divided road surface of each sub-region according to the curve distribution and straight road distribution;

[0188] Determining the spatial direction aggregation feature and the spatial range perception feature of the divided road surface of the sub-region according to the set of spatial direct viewing angle changes from the beginning to the end of the divided road surface of each sub-region;

[0189] Calculating the qualification index of the divided road conditions of each sub-region according to the visual influence coefficient, the complexity of the divided road conditions, the spatial direction aggregation feature and the spatial range perception feature of the divided road surface of the sub-region:

[0190] ; where represents the qualification index of the divided road conditions of the i-th sub-region, represents the visual influence coefficient of the divided road surface of the i-th sub-region, represents the visual visible range of the divided road surface of the i-th sub-region, represents the visual blind area range of the divided road surface of the i-th sub-region, represents the complexity of the divided road conditions of the divided road surface of the i-th sub-region, represents the spatial direction aggregation feature of the divided road surface of the i-th sub-region, represents the spatial range perception feature of the divided road surface of the i-th sub-region, represents the openness index of the divided road surface of the i-th sub-region;

[0191] If the qualification index of the road conditions in each sub-region is greater than or equal to the preset index, it is determined that the road conditions of the sub-region are qualified; otherwise, it is determined that the road conditions of the sub-region are unqualified.

[0192] Redivide the target sub-region with unqualified road conditions.

[0193] In this embodiment, the road surface structure means that the road surface is a straight lane, a downhill section, a turning section, or a downhill slope.

[0194] In this embodiment, the reference building data around the road surface, for example, there is a green belt around the road surface, and the floor area, diameter, and perimeter of the green belt.

[0195] In this embodiment, the complexity of the road conditions, for example, if the road surface has only straight sections, then the complexity of the road conditions of the road surface is small. If the road surface includes straight sections, downhill sections, and turning sections, it means that the complexity of the road conditions of the road surface is high.

[0196] In this embodiment, the visual impact coefficient, for example, if there is a green belt beside the road surface and the green belt has no obstruction to the line of sight, then the visual impact coefficient is 0. If there is a coal bunker beside the road surface and the coal bunker blocks half of the line of sight, then the visual impact coefficient is 0.5.

[0197] In this embodiment, the set of changes in the spatial direct viewing angle is that if the road surface is a straight lane, then it is looking straight ahead. If there is a bend ahead, then it is necessary to look in the direction of the bend. During the entire driving process, the set in which the viewing angle changes between direct viewing and side viewing back and forth.

[0198] In this embodiment, the spatial direction aggregation feature refers to a comprehensive judgment of the directions around a position and the direction of the driving road.

[0199] In this embodiment, the spatial range perception feature refers to the perception of the spatial characteristics such as the distance and orientation of the road surface, and the size and shape of the objects around the road surface.

[0200] In this embodiment, the preset index is 1.

[0201] The beneficial effects of the above technical solution are as follows: By obtaining the structure of the road surface and the data of the surrounding buildings, the complexity of the road conditions and the visual impact coefficient of each sub-region are determined. According to the distribution of straight lanes and bends on the road surface, the spatial direction aggregation feature and the spatial range perception feature of the sub-region are determined, so as to determine the qualification index of the divided section, and it can be determined whether the road surface division is qualified, which enhances the safety of driverless mining vehicles.

[0202] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A method for detecting the road surface flatness of driverless mining trucks, characterized in that, The method includes: Step 1: Design the original elevation data of the path corresponding to the starting point and the target point of the driving path of the driverless mining truck, and establish a three-dimensional road surface model, where the three-dimensional road surface model is used to reflect the elevation of each position point on the road surface; Step 2: Divide the three-dimensional road surface model into multiple sub-regions, collect the three-dimensional data of each sub-region at the current moment, and splice the three-dimensional data of the adjacent sub-regions collected; Step 3: Perform road section division according to the splicing result, and determine the road surface hierarchy of each divided road section; Step 4: Analyze the cross-section line of the road surface hierarchy to determine the flatness of the corresponding divided road section, and then determine the comprehensive flatness of the designed path to achieve road flatness detection; Among them, before step 4, it also includes: Obtain the road surface data corresponding to the road surface hierarchy of each divided road section; Classify the road surface data of each divided road section according to the preset road surface design attributes to generate data clusters; Obtain the data attributes corresponding to each data cluster respectively; Construct the analysis sequence of each data cluster, and place the data cluster on the analysis sequence for analysis to obtain the analysis result; Obtain the sequence attributes of each data cluster according to the analysis result, and generate an attribute comparison chain based on the sequence attributes and data attributes of each data cluster; Determine the abnormal attribute mapping nodes in the attribute comparison chain of each data cluster; Obtain multiple data elements of the status data corresponding to the abnormal attribute mapping nodes; Obtain the logical mapping relationship between two adjacent data elements, and analyze the connection degree between two adjacent data elements according to the logical mapping relationship; If the connection degree is less than the preset index, determine that the status data corresponding to the abnormal attribute mapping node is abnormal, otherwise, determine that the status data corresponding to the abnormal attribute mapping node is normal; Obtain the defect status data on the basis of determining that the status data corresponding to the abnormal attribute mapping node is abnormal, and perform data correction on the defect status data to obtain the correction result; Obtain the corrected road surface data of each divided road section according to the correction result, and use the corrected road surface data as the sample data for analyzing the cross-section line of the road surface hierarchy.

2. The road surface flatness detection method for driverless mining trucks according to claim 1, characterized in that Design the original elevation data of the path corresponding to the starting point and the target point of the driving path of the driverless mining truck, and establish a three-dimensional road surface model, including: Obtain the two-dimensional drawing of the designed path corresponding to the starting point and the target point of the driving path of the driverless mining truck; Extract the center line and multiple other closed area blocks corresponding to the designed path according to the two-dimensional drawing; Mark multiple points on the center line, obtain the original elevation data of multiple marked points, and generate a three-dimensional center line model of the designed path according to the center line, multiple marked points and the original elevation data of multiple marked points; Construct a three-dimensional road surface model of the designed path based on the positional relationship between the three-dimensional center line model and each other closed area block.

3. The road surface flatness detection method for driverless mining trucks according to claim 1, wherein Divide the three-dimensional road surface model into multiple sub-regions, collect the three-dimensional data of each sub-region at the current moment, and splice the three-dimensional data of the adjacent sub-regions collected, including: Divide the three-dimensional road surface model along the driving direction and the measurement width direction into multiple sub-regions; Determine the traffic flow parameters of each sub-region, and set the three-dimensional data acquisition period of each sub-region according to the traffic flow parameters; Detect and collect the three-dimensional data of the current moment of each sub-region in real time according to the three-dimensional data acquisition period of each sub-region; Stitch the three-dimensional data collected at the same moment in adjacent regions.

4. The road surface flatness detection method for driverless mining trucks according to claim 3, wherein Detect and collect the three-dimensional data of the current moment of each sub-region in real time according to the three-dimensional data acquisition period of each sub-region, including: Conduct an analysis on the road surface composition characteristics of each sub-region, and select a low-pass filter for each sub-region according to the analysis results; Set the filtering parameters of the selected low-pass filter for each sub-region according to the three-dimensional data acquisition period of each sub-region; Perform frequency-domain low-pass filtering on each sub-region based on the low-pass filter with the set filtering parameters for each sub-region to obtain the low-frequency component data and high-frequency component data of each sub-region; Obtain the slow deformation information of each sub-region based on the low-frequency component data, and at the same time obtain the local drastic change information and texture feature information of each sub-region based on the high-frequency component data; Obtain the three-dimensional data of each sub-region based on the slow deformation information, local drastic change information, and texture feature information of each sub-region.

5. The road surface flatness detection method for driverless mining trucks according to claim 1, characterized in that, Conduct road section division according to the stitching result, and determine the road surface hierarchy of each divided road section, including: Divide the design path into multiple road surface sections based on a preset interval distance according to the stitching result; Obtain the road surface description parameters of each road surface section according to the three-dimensional data of each road surface section, and determine the road surface structure layer types of each road surface section according to the road surface description parameters; Retrieve the hierarchy data of the road surface section of this type according to the road surface structure layer type of each road surface section; Perform data mapping on the hierarchy data of each road surface section to determine the road surface hierarchy of each road surface section.

6. The road surface flatness detection method for driverless mining trucks according to claim 1, characterized in that Conduct cross-section line analysis on the road surface hierarchy to determine the flatness of the corresponding divided road section, and further determine the comprehensive flatness of the design path to achieve road flatness detection, including: Obtain the structure data corresponding to the road surface hierarchy and perform smoothing filtering preprocessing on it to obtain the current structure data after preprocessing; Compare the current structure data with the standard structure data to determine the complete hierarchy and incomplete hierarchy in the road surface hierarchy; Obtain the target road surface data of the incomplete structure hierarchy, and determine the cross-section line distribution of the incomplete structure hierarchy according to the data distribution rule of the target road surface data; Determine the flatness of each divided road section according to the cross-section line distribution, and perform weighted calculation according to the road section area of each divided road section and the flatness of this divided road section to determine the comprehensive flatness of the design path.

7. The road surface flatness detection method for driverless mining trucks according to claim 6, characterized in that, Determine the cross-section line distribution of the incomplete structure hierarchy according to the data distribution rule of the target road surface data, including: Determine the abnormal road surface data according to the data distribution rule of the target road surface data; Obtain the road surface longitudinal section profile based on the abnormal road surface data; Obtain the road surface longitudinal section line based on the road surface longitudinal section profile; Determine the cross-section line distribution of the incomplete structure hierarchy according to the distribution position of the road surface longitudinal section line in the incomplete structure hierarchy.

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