Online detection system and method for coal transporting vehicle
By screening, area division and vehicle model information correction of point cloud data of coal transport vehicles, the problems of low control efficiency and inaccurate load calculations of coal transport vehicles are solved, and efficient coal transport control is achieved.
Patent Information
- Application Number
- CN202411895802.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-20
- Publication Date
- 2025-04-29
AI Technical Summary
In the prior art, the control efficiency of coal transport vehicles is low and the load capacity calculation is not accurate enough, resulting in chaos on the site of coal mines or coal storage yards, affecting cost control.
By obtaining point cloud data of coal transport vehicles, filtering outliers, dividing areas, determining model information, and correcting the overweight weight based on the model information to calculate the coal loading capacity.
Accurate coal load inspection of coal transport vehicles has been achieved, the management and control efficiency of coal block transportation has been improved, and the labor intensity of operators has been reduced.
Smart Images
Figure CN119901362A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of radio wave measurement technology, and more specifically, to an online detection system and method for coal transport trucks. Background Art
[0002] With the development of my country's coal mining industry, coal has become one of the main sources of energy in all walks of life. The existing technology mainly relies on on-site staff to monitor, weigh and record the vehicles entering and leaving the coal mine or coal storage yard to grasp the coal carrying capacity of the coal-carrying trucks. Relying solely on manual statistics will not only increase the labor intensity of operators, but also have low automation, coal quantity statistics and control efficiency. Due to the large number of vehicles entering and leaving the coal mine or coal storage yard, it will also affect the accuracy of coal storage statistics. If the vehicles entering and leaving the coal mine or coal storage yard are not controlled, it will cause chaos on the site of the coal mine or coal storage yard, which is not conducive to controlling the cost of the coal mine or coal storage yard. Summary of the invention
[0003] The present invention provides an online detection system and method for coal transport vehicles, which are used to solve the problems of low control efficiency and inaccurate calculation of the carrying capacity of coal transport vehicles in the prior art, including: An acquisition module is used to acquire point cloud data of the tested coal transport vehicle, and filter out outliers in the point cloud data according to the point cloud data of the tested coal transport vehicle; A detection module, used for dividing the tested coal transport vehicle into regions according to the screened point cloud data of the tested coal transport vehicle, and determining the model information of the tested coal transport vehicle according to the point cloud data of the tested coal transport vehicle after the region division; The calculation module is used to obtain the weighed weight of the tested coal-carrying truck, and to correct the weighed weight according to the model information of the tested coal-carrying truck to obtain the coal-carrying capacity of the truck.
[0004] Furthermore, the acquisition module filters out outliers in the point cloud data according to the point cloud data of the tested coal transport truck, including: Obtaining a preset data block, and segmenting the point cloud data of the tested coal transport vehicle according to the preset data block to obtain a plurality of point cloud data blocks; Obtain data points in each point cloud data block, and determine the convex hull of the data points in the point cloud data block based on a convex hull algorithm; Calculate the volume value of the convex hull of the data points in the point cloud data block, and determine the ratio of the convex hull volume value to the point cloud data block volume value according to the volume value of the convex hull of the data points in the point cloud data block to obtain a first parameter; Obtaining the number of data points in the point cloud data block to obtain a second parameter, calculating the ratio of the second parameter to the first parameter, and obtaining a distribution parameter of the point cloud data block; The LOF algorithm is set according to the distribution parameters of the point cloud data block. Value, according to The local anomaly factors of the data points in the point cloud data block are detected based on the LOF algorithm, and the outliers of the point cloud data block are screened out according to the local anomaly factors.
[0005] Furthermore, the LOF algorithm is set according to the distribution parameters of the point cloud data block. Values, including: based on The value calculation formula sets the LOF algorithm according to the distribution parameters of the point cloud data block. value, The value calculation formula is as follows: , in, To preset initial value, is the preset distribution parameter allowable value, is the distribution parameter of the point cloud data block, is the preset range coefficient, () is the natural exponential function.
[0006] Furthermore, the detection module divides the tested coal transport vehicle into regions according to the screened point cloud data of the tested coal transport vehicle, including: Determine the reflectivity of each data point according to the screened point cloud data of the tested coal transport truck, and perform clustering processing on each data point according to the reflectivity based on the k-means clustering algorithm; Determine the cluster center value corresponding to each cluster cluster according to the clustering result, and judge whether the difference between the cluster center value and the preset cluster center threshold is greater than the first preset threshold; If the difference between the cluster center value and the preset cluster center threshold is greater than the first preset threshold, the corresponding point cloud data area is determined as the carriage area; If the difference between the cluster center value and the preset cluster center threshold is less than or equal to the first preset threshold, then determine whether the difference between the cluster center value and the preset cluster center threshold is greater than the second preset threshold; If the difference between the cluster center value and the preset cluster center threshold is greater than a second preset threshold, the corresponding point cloud data area is determined as a coal block area; If the difference between the cluster center value and the preset cluster center threshold is less than or equal to the second preset threshold, the corresponding point cloud data area is determined to be the tire area.
[0007] Furthermore, the detection module determines the model information of the tested coal transport vehicle according to the point cloud data of the tested coal transport vehicle after the area division, including: Obtain a vehicle information database, and establish a training sample set according to vehicle parameter information and corresponding vehicle model information in the vehicle information database; Establishing a vehicle model information evaluation model according to the training sample set and training the vehicle model information evaluation model to obtain a trained vehicle model information evaluation model; The coordinate values of the point cloud data of the carriage area and the tire area are obtained, and the coordinate values of the point cloud data of the carriage area and the tire area are input into the trained vehicle model information evaluation model to obtain the vehicle model information of the tested coal transport vehicle.
[0008] Furthermore, the calculation module corrects the weighed weight according to the model information of the tested coal transport vehicle to obtain the coal loading capacity of the vehicle, including: Determine the deadweight interval value of the tested coal-carrying vehicle according to the vehicle model information of the tested coal-carrying vehicle, calculate the difference between the current weighed weight and the deadweight interval value according to the deadweight interval value of the tested coal-carrying vehicle, and determine the coal load of the vehicle according to the difference between the current weighed weight and the deadweight interval value; Obtaining the point cloud data coordinate values of the coal block area, and calculating the coal block area value according to the point cloud data coordinate values of the coal block area; The coal load of the truck is corrected according to the area value of the coal block to obtain the final coal load of the truck.
[0009] Further, the determining of the deadweight interval value of the tested coal transport vehicle according to the vehicle type information of the tested coal transport vehicle comprises: Determine the category data of the tested coal transport vehicle according to the vehicle information database, and if the category data of the tested coal transport vehicle is the first category, set the first deadweight interval value as the deadweight interval value of the tested coal transport vehicle; If the category data of the tested coal transport truck is the second category, the second deadweight interval value is set as the deadweight interval value of the tested coal transport truck; If the category data of the tested coal-carrying truck is the third category, the third deadweight interval value is set as the deadweight interval value of the tested coal-carrying truck.
[0010] Further, the calculating of the coal block area value according to the point cloud data coordinate value of the coal block area includes: Determine the maximum coordinate value and the minimum coordinate value of the coal block area on the z-axis according to the coordinate value of the point cloud data of the coal block area, and calculate the absolute value of the slope according to the maximum coordinate value and the minimum coordinate value of the coal block area on the z-axis; The angle of the hypotenuse is set according to the absolute value of the slope, and the length of the hypotenuse is set according to the distance between the maximum coordinate value and the minimum coordinate value of the coal block area on the z-axis; Take the height of the hypotenuse as the axis of symmetry, construct a triangle based on the angle and length of the hypotenuse, and calculate the area of the triangle; The width of the rectangle is set according to the height information of the coal truck under test, the length of the rectangle is set according to the distance between the two minimum coordinate values of the coal block area on the z-axis, a rectangle is constructed according to the width and length of the rectangle, and the area value of the rectangle is added to the area value of the triangle to obtain the area value of the coal block.
[0011] Furthermore, the correction of the coal load of the vehicle according to the coal block area value includes: The coal block area threshold is set according to the measured weight of the coal transport truck, and the difference between the coal block area value and the coal block area threshold is calculated; The difference between the coal block area value and the coal block area threshold is normalized, and the difference between the normalized coal block area value and the coal block area threshold is multiplied by the coal load of the vehicle to obtain a corrected value of the coal load of the vehicle.
[0012] In order to achieve the above object, the present invention also provides an online detection method for a coal transport truck, comprising: Acquire the point cloud data of the tested coal transport truck, and filter outliers in the point cloud data according to the point cloud data of the tested coal transport truck; Divide the tested coal transport vehicle into regions according to the screened point cloud data of the tested coal transport vehicle, and determine the model information of the tested coal transport vehicle according to the point cloud data of the tested coal transport vehicle after the regional division; The weighed weight of the tested coal-carrying truck is obtained, and the weighed weight is corrected according to the model information of the tested coal-carrying truck to obtain the coal-carrying capacity of the truck.
[0013] The beneficial effects of the present invention are: By applying the above technical solutions, the present invention analyzes the point cloud data of coal transport vehicles based on laser radar and selects the LOF algorithm. The value can be used to accurately screen out the discrete points of the measured coal transport truck point cloud data. At the same time, the coal carrying capacity of the coal transport truck can be accurately obtained to improve the management and control efficiency of coal block transportation and reduce the labor intensity of operators. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.
[0015] Figure 1 The overall structure diagram of an online detection system for coal transport vehicles proposed in an embodiment of the present invention is shown; Figure 2 A schematic flow chart of an online detection method for a coal transport truck proposed in an embodiment of the present invention is shown. DETAILED DESCRIPTION
[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0017] The present application embodiment provides an online detection system for coal transport vehicles, such as Figure 1 As shown, including: The acquisition module is used to acquire the point cloud data of the tested coal-transporting truck, and filter out the outliers in the point cloud data according to the point cloud data of the tested coal-transporting truck; the detection module is used to divide the tested coal-transporting truck into regions according to the filtered point cloud data, and determine the model information of the tested coal-transporting truck according to the point cloud data of the tested coal-transporting truck after the regional division; the calculation module is used to acquire the weighed weight of the tested coal-transporting truck, and correct the weighed weight according to the model information of the tested coal-transporting truck to obtain the coal-carrying capacity of the truck.
[0018] In this embodiment, the point cloud data of the coal-carrying truck under test is acquired by the LIDAR sensor of the laser radar scanning device, and the discrete noise points of the point cloud data are screened out. The model information of the coal-carrying truck under test is determined based on the point cloud data after the discrete noise points are screened out, and then the accurate coal loading capacity of the truck is obtained to prevent errors in weighing the coal-carrying truck.
[0019] In some embodiments of the present application, the acquisition module filters out outliers in the point cloud data based on the point cloud data of the tested coal-carrying truck, including: acquiring preset data blocks, segmenting the point cloud data of the tested coal-carrying truck according to the preset data blocks, and obtaining a plurality of point cloud data blocks; acquiring data points in each point cloud data block, and determining the convex hull of the data points in the point cloud data block based on the convex hull algorithm; calculating the volume value of the convex hull of the data points in the point cloud data block, and determining the ratio of the convex hull volume value to the point cloud data block volume value according to the volume value of the convex hull of the data points in the point cloud data block to obtain a first parameter; acquiring the number of data points in the point cloud data block to obtain a second parameter, calculating the ratio of the second parameter to the first parameter to obtain the distribution parameter of the point cloud data block; and setting the LOF algorithm according to the distribution parameter of the point cloud data block. Value, according to The local anomaly factors of the data points in the point cloud data block are detected based on the LOF algorithm, and the outliers of the point cloud data block are screened out according to the local anomaly factors.
[0020] In some embodiments of the present application, the LOF algorithm is set according to the distribution parameters of the point cloud data block. Values, including: based on The value calculation formula sets the LOF algorithm according to the distribution parameters of the point cloud data block. value, The value calculation formula is as follows: , in, To preset initial value, is the preset distribution parameter allowable value, is the distribution parameter of the point cloud data block, is the preset range coefficient, () is the natural exponential function.
[0021] In this embodiment, the preset data block is a square block of fixed length. The point cloud data of the coal transport truck under test is evenly divided by the preset data block, and the LOF algorithm is set by the distribution parameters of the point cloud data block. The distribution of the point cloud data in the preset data block is determined by calculating the ratio of the second parameter to the first parameter. The smaller the ratio, the more discrete the point cloud data distribution is, and the greater the probability of discrete points appearing in the point cloud data. The smaller the value, the more effective it is in filtering out discrete points in the point cloud data block.
[0022] In some embodiments of the present application, the detection module divides the tested coal-transporting truck into regions according to the point cloud data of the tested coal-transporting truck after screening, including: determining the reflectivity of each data point according to the point cloud data of the tested coal-transporting truck after screening, and clustering each data point according to the reflectivity based on the k-means clustering algorithm; determining the cluster center value corresponding to each cluster cluster according to the clustering result, and judging whether the difference between the cluster center value and the preset cluster center threshold is greater than the first preset threshold; if the difference between the cluster center value and the preset cluster center threshold is greater than the first preset threshold, then determining the corresponding point cloud data region as the car body region; if the difference between the cluster center value and the preset cluster center threshold is less than or equal to the first preset threshold, then judging whether the difference between the cluster center value and the preset cluster center threshold is greater than the second preset threshold; if the difference between the cluster center value and the preset cluster center threshold is greater than the second preset threshold, then determining the corresponding point cloud data region as the coal block region; if the difference between the cluster center value and the preset cluster center threshold is less than or equal to the second preset threshold, then determining the corresponding point cloud data region as the tire region.
[0023] In this embodiment, the reflectivity represents a measure of how much laser light is reflected by an object at that position. The data points of the point cloud data are clustered by the reflectivity based on the k-means clustering algorithm. The k value is set to 3, and the data points are clustered into three partitions. The area to which the data points belong is determined based on the cluster center value of each partition, thereby achieving accurate partitioning of the coal transport vehicle and extracting the car area, coal block area and tire area of the coal transport vehicle.
[0024] In some embodiments of the present application, the detection module determines the vehicle model information of the tested coal-transporting truck based on the point cloud data of the tested coal-transporting truck after area division, including: obtaining a vehicle information database, and establishing a training sample set based on the vehicle parameter information and the corresponding vehicle model information in the vehicle information database; establishing a vehicle model information evaluation model based on the training sample set and training the vehicle model information evaluation model to obtain a trained vehicle model information evaluation model; obtaining the point cloud data coordinate values of the vehicle compartment area and the tire area, and inputting the point cloud data coordinate values of the vehicle compartment area and the tire area into the trained vehicle model information evaluation model to obtain the vehicle model information of the tested coal-transporting truck.
[0025] In this embodiment, the vehicle type information evaluation model is trained through a preset vehicle information database, the vehicle size information is determined according to the point cloud data coordinate values of the cabin area and tire area of the coal transport vehicle to be tested, and the vehicle type information of the coal transport vehicle to be tested is output through the trained vehicle type information evaluation model.
[0026] In some embodiments of the present application, the calculation module corrects the weighed weight according to the model information of the tested coal-transporting truck to obtain the coal-carrying capacity of the truck, including: determining the deadweight interval value of the tested coal-transporting truck according to the model information of the tested coal-transporting truck, calculating the difference between the current weighed weight and the deadweight interval value according to the deadweight interval value of the tested coal-transporting truck, and determining the coal-carrying capacity of the truck according to the difference between the current weighed weight and the deadweight interval value; obtaining the point cloud data coordinate value of the coal block area, calculating the coal block area value according to the point cloud data coordinate value of the coal block area; and correcting the coal-carrying capacity of the truck according to the coal block area value to obtain the final coal-carrying capacity of the truck.
[0027] In some embodiments of the present application, determining the deadweight interval value of the tested coal-transport truck based on the vehicle model information of the tested coal-transport truck includes: determining the category data of the tested coal-transport truck based on the vehicle information database; if the category data of the tested coal-transport truck is the first category, setting the first deadweight interval value as the deadweight interval value of the tested coal-transport truck; if the category data of the tested coal-transport truck is the second category, setting the second deadweight interval value as the deadweight interval value of the tested coal-transport truck; if the category data of the tested coal-transport truck is the third category, setting the third deadweight interval value as the deadweight interval value of the tested coal-transport truck.
[0028] In this embodiment, the category data of the coal-transporting truck under test is determined through the vehicle model information data in the vehicle information database, the deadweight interval value is set through the category data, the coal loading capacity of the truck is determined through the difference between the weighed weight and the deadweight interval value, and the coal loading capacity of the truck is corrected based on the coal block area value of the coal block area to obtain a more accurate coal loading capacity of the truck.
[0029] In some embodiments of the present application, the method of calculating the area value of a coal block based on the coordinate values of the point cloud data of the coal block area includes: determining the maximum coordinate value and the minimum coordinate value of the coal block area on the z-axis based on the coordinate values of the point cloud data of the coal block area, and calculating the absolute value of the slope based on the maximum coordinate value and the minimum coordinate value of the coal block area on the z-axis; setting the angle of the hypotenuse based on the absolute value of the slope, and setting the length of the hypotenuse based on the distance between the maximum coordinate value and the minimum coordinate value of the coal block area on the z-axis; taking the height of the hypotenuse as the axis of symmetry, constructing a triangle based on the angle and length of the hypotenuse and calculating the area value of the triangle; setting the width of the rectangle based on the height information of the coal transport truck being measured, setting the length of the rectangle based on the distance between the two minimum coordinate values of the coal block area on the z-axis, constructing a rectangle based on the width of the rectangle and the length of the rectangle, and adding the area value of the rectangle to the area value of the triangle to obtain the area value of the coal block.
[0030] In this embodiment, the maximum coordinate value and any minimum coordinate value on the z-axis are determined according to the coordinate value of the point cloud data of the coal block area, the minimum coordinate value is generally 0, the angle of the hypotenuse is set by the absolute value of the slope from the maximum coordinate value to the minimum coordinate value, the length of the hypotenuse is set according to the distance from the maximum coordinate value to the minimum coordinate value, a triangle is constructed with the height of the hypotenuse as the axis of symmetry, the width of the rectangle is set according to the height information of the measured coal transport truck, the length of the rectangle is set by the distance between the two farthest minimum coordinate values of the coal block area on the z-axis, a rectangle is constructed, and the area value of the coal block is obtained by adding the area value of the rectangle to the area value of the triangle.
[0031] In some embodiments of the present application, the correction of the coal loading of a vehicle according to the coal block area value includes: setting a coal block area threshold according to the weighed weight of the coal transporting vehicle under test, and calculating the difference between the coal block area value and the coal block area threshold; normalizing the difference between the coal block area value and the coal block area threshold, and multiplying the difference between the normalized coal block area value and the coal block area threshold by the coal loading of the vehicle to obtain a corrected value of the coal loading of the vehicle.
[0032] In this embodiment, the difference between the coal block area value and the coal block area threshold is normalized and its range is limited to 0-2. The coal load of the vehicle is corrected by multiplying the difference between the normalized coal block area value and the coal block area threshold by the coal load of the vehicle to accurately obtain the coal load of the coal-transporting vehicle.
[0033] Based on the same technical concept, such as Figure 2 As shown, the present invention also provides an online detection method for a coal transport vehicle, comprising: S101, obtaining point cloud data of the tested coal transport truck, and filtering outliers in the point cloud data according to the point cloud data of the tested coal transport truck; S102, dividing the tested coal transport vehicle into regions according to the screened point cloud data of the tested coal transport vehicle, and determining the model information of the tested coal transport vehicle according to the point cloud data of the tested coal transport vehicle after the region division; S103, obtaining the weighed weight of the tested coal-carrying truck, and correcting the weighed weight according to the model information of the tested coal-carrying truck to obtain the coal-carrying capacity of the truck.
[0034] By applying the above technical solutions, the acquisition module of the present invention is used to obtain the point cloud data of the tested coal-carrying truck, and filter out the outliers in the point cloud data according to the point cloud data of the tested coal-carrying truck; the detection module is used to divide the tested coal-carrying truck into regions according to the point cloud data of the tested coal-carrying truck after screening, and determine the model information of the tested coal-carrying truck according to the point cloud data of the tested coal-carrying truck after regional division; the calculation module is used to obtain the weighed weight of the tested coal-carrying truck, and correct the weighed weight according to the model information of the tested coal-carrying truck to obtain the coal-carrying capacity of the truck. The present invention analyzes the point cloud data of the coal-carrying truck based on laser radar, and can accurately obtain the coal-carrying capacity of the coal-carrying truck, improve the control efficiency of coal block transportation, and reduce the labor intensity of operators.
[0035] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.
[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims
1. An online detection system for coal transport vehicles, characterized in that: include: An acquisition module is used to acquire point cloud data of the tested coal transport vehicle, and filter out outliers in the point cloud data according to the point cloud data of the tested coal transport vehicle; A detection module, used for dividing the tested coal transport vehicle into regions according to the screened point cloud data of the tested coal transport vehicle, and determining the model information of the tested coal transport vehicle according to the point cloud data of the tested coal transport vehicle after the regional division; The calculation module is used to obtain the weighed weight of the tested coal-carrying truck, and to correct the weighed weight according to the model information of the tested coal-carrying truck to obtain the coal-carrying capacity of the truck.
2. The coal truck online detection system according to claim 1 is characterized in that: The acquisition module filters out outliers in the point cloud data according to the point cloud data of the tested coal transport truck, including: Obtaining a preset data block, and segmenting the point cloud data of the tested coal transport vehicle according to the preset data block to obtain a plurality of point cloud data blocks; Obtain data points in each point cloud data block, and determine the convex hull of the data points in the point cloud data block based on a convex hull algorithm; Calculate the volume value of the convex hull of the data points in the point cloud data block, and determine the ratio of the convex hull volume value to the point cloud data block volume value according to the volume value of the convex hull of the data points in the point cloud data block to obtain a first parameter; Obtaining the number of data points in the point cloud data block to obtain a second parameter, calculating the ratio of the second parameter to the first parameter, and obtaining a distribution parameter of the point cloud data block; The LOF algorithm is set according to the distribution parameters of the point cloud data block. Value, according to The local anomaly factors of the data points in the point cloud data block are detected based on the LOF algorithm, and the outliers of the point cloud data block are screened out according to the local anomaly factors.
3. The coal truck online detection system according to claim 2 is characterized in that: The LOF algorithm is set according to the distribution parameters of the point cloud data block. Values, including: based on The value calculation formula sets the LOF algorithm according to the distribution parameters of the point cloud data block. value, The value calculation formula is as follows: ， in, To preset initial value, is the preset distribution parameter allowable value, is the distribution parameter of the point cloud data block, is the preset range coefficient, () is the natural exponential function.
4. The coal truck online detection system according to claim 1 is characterized in that: The detection module divides the tested coal transport vehicle into regions according to the screened point cloud data of the tested coal transport vehicle, including: Determine the reflectivity of each data point according to the screened point cloud data of the tested coal transport truck, and perform clustering processing on each data point according to the reflectivity based on the k-means clustering algorithm; Determine the cluster center value corresponding to each cluster cluster according to the clustering result, and judge whether the difference between the cluster center value and the preset cluster center threshold is greater than the first preset threshold; If the difference between the cluster center value and the preset cluster center threshold is greater than the first preset threshold, the corresponding point cloud data area is determined as the carriage area; If the difference between the cluster center value and the preset cluster center threshold is less than or equal to the first preset threshold, then determine whether the difference between the cluster center value and the preset cluster center threshold is greater than the second preset threshold; If the difference between the cluster center value and the preset cluster center threshold is greater than a second preset threshold, the corresponding point cloud data area is determined as a coal block area; If the difference between the cluster center value and the preset cluster center threshold is less than or equal to the second preset threshold, the corresponding point cloud data area is determined to be the tire area.
5. The coal truck online detection system according to claim 4 is characterized in that: The detection module determines the model information of the tested coal transport vehicle according to the point cloud data of the tested coal transport vehicle after the area division, including: Obtain a vehicle information database, and establish a training sample set according to vehicle parameter information and corresponding vehicle model information in the vehicle information database; Establishing a vehicle model information evaluation model according to the training sample set and training the vehicle model information evaluation model to obtain a trained vehicle model information evaluation model; The coordinate values of the point cloud data of the carriage area and the tire area are obtained, and the coordinate values of the point cloud data of the carriage area and the tire area are input into the trained vehicle model information evaluation model to obtain the vehicle model information of the tested coal transport vehicle.
6. The coal truck online detection system according to claim 1 is characterized in that: The calculation module corrects the weighed weight according to the model information of the coal transport vehicle to obtain the coal loading capacity of the vehicle, including: Determine the deadweight interval value of the tested coal-carrying vehicle according to the vehicle model information of the tested coal-carrying vehicle, calculate the difference between the current weighed weight and the deadweight interval value according to the deadweight interval value of the tested coal-carrying vehicle, and determine the coal load of the vehicle according to the difference between the current weighed weight and the deadweight interval value; Obtaining the point cloud data coordinate values of the coal block area, and calculating the coal block area value according to the point cloud data coordinate values of the coal block area; The coal load of the truck is corrected according to the area value of the coal block to obtain the final coal load of the truck.
7. The coal truck online detection system according to claim 6 is characterized in that: The step of determining the deadweight interval value of the tested coal transport vehicle according to the vehicle model information of the tested coal transport vehicle comprises: Determine the category data of the tested coal transport vehicle according to the vehicle information database, and if the category data of the tested coal transport vehicle is the first category, set the first deadweight interval value as the deadweight interval value of the tested coal transport vehicle; If the category data of the tested coal transport truck is the second category, the second deadweight interval value is set as the deadweight interval value of the tested coal transport truck; If the category data of the tested coal-carrying truck is the third category, the third deadweight interval value is set as the deadweight interval value of the tested coal-carrying truck.
8. The coal truck online detection system according to claim 6 is characterized in that: The calculating of the coal block area value according to the point cloud data coordinate value of the coal block area includes: Determine the maximum coordinate value and the minimum coordinate value of the coal block area on the z-axis according to the coordinate value of the point cloud data of the coal block area, and calculate the absolute value of the slope according to the maximum coordinate value and the minimum coordinate value of the coal block area on the z-axis; The angle of the hypotenuse is set according to the absolute value of the slope, and the length of the hypotenuse is set according to the distance between the maximum coordinate value and the minimum coordinate value of the coal block area on the z-axis; Take the height of the hypotenuse as the axis of symmetry, construct a triangle based on the angle and length of the hypotenuse, and calculate the area of the triangle; The width of the rectangle is set according to the height information of the coal truck under test, the length of the rectangle is set according to the distance between the two minimum coordinate values of the coal block area on the z-axis, a rectangle is constructed according to the width and length of the rectangle, and the area value of the rectangle is added to the area value of the triangle to obtain the area value of the coal block.
9. The coal truck online detection system according to claim 8, characterized in that: The method of correcting the coal load of the vehicle according to the coal block area value includes: The coal block area threshold is set according to the measured weight of the coal transport truck, and the difference between the coal block area value and the coal block area threshold is calculated; The difference between the coal block area value and the coal block area threshold is normalized, and the difference between the normalized coal block area value and the coal block area threshold is multiplied by the coal load of the vehicle to obtain a corrected value of the coal load of the vehicle.
10. A method for online detection of coal transport vehicles, characterized in that: include: Acquire the point cloud data of the tested coal transport truck, and filter outliers in the point cloud data according to the point cloud data of the tested coal transport truck; Divide the tested coal transport vehicle into regions according to the screened point cloud data of the tested coal transport vehicle, and determine the model information of the tested coal transport vehicle according to the point cloud data of the tested coal transport vehicle after the regional division; The weighed weight of the tested coal-carrying truck is obtained, and the weighed weight is corrected according to the model information of the tested coal-carrying truck to obtain the coal-carrying capacity of the truck.