Method and system for determining broken volume of rock in blasting area

Abstract

The invention discloses an explosion area rock crushing volume determination method and system, and relates to the technical field of data processing. The method comprises the following steps: acquiring vehicle attitude data, carriage deformation data and carriage point cloud data of a plurality of trucks in a target explosion area; for each truck, calculating a carriage inclination angle of the truck according to the vehicle attitude data, and performing attitude correction processing on the carriage point cloud data according to the carriage inclination angle to obtain a point cloud data sample of the truck; calculating a carriage volume deformation value of the truck according to the carriage deformation data, and performing deformation compensation processing on the point cloud data sample according to the carriage volume deformation value to obtain target point cloud data of the truck; determining the gravel volume of the truck based on the target point cloud data of the truck; and determining the rock breaking volume of the target explosion area by combining the broken stone volume of each truck. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of determining the broken volume of the rock in the blasting area is achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, system, electronic device and storage medium for determining rock crushing volume in an explosion area. Background Art

[0002] With the development of industrialization and modernization, blasting operations in the mining and construction industries have become increasingly frequent. In the mining and construction industries, the handling and transportation of blasted rocks is an important link. After blasting, rock fragments need to be quickly and accurately evaluated and transported to ensure operational efficiency and economic benefits. In this process, trucks are widely used to transport these rock fragments.

[0003] At present, the existing method for determining the rock crushing volume in the blasting area usually uses a fixed recognition standard to analyze the truck compartment image, estimate the volume of the crushed rocks loaded in the compartment, and thus determine the rock crushing volume in the blasting area. However, in practical applications, only using the existing fixed recognition standard to analyze the truck compartment image to determine the crushed rock volume often tends to ignore the error in the crushed rock volume caused by the deformation and tilt of the truck compartment during transportation, resulting in low accuracy in determining the rock crushing volume in the blasting area. Summary of the invention

[0004] The present application provides a method and system for determining the rock crushing volume in a blasting area, which has the effect of improving the accuracy of determining the rock crushing volume in the blasting area.

[0005] In a first aspect, the present application provides a method for determining rock crushing volume in a blasting area, comprising: Obtain vehicle posture data, compartment deformation data, and compartment point cloud data of multiple trucks in the target explosion area; For each of the trucks, the inclination angle of the truck compartment is calculated according to the vehicle posture data, and the posture correction processing is performed on the truck compartment point cloud data according to the inclination angle of the truck compartment to obtain a point cloud data sample of the truck; Calculating the volume deformation value of the truck compartment according to the truck compartment deformation data, and performing deformation compensation processing on the point cloud data sample according to the truck compartment volume deformation value to obtain target point cloud data of the truck; determining a gravel volume of the truck based on the target point cloud data of the truck; The rock crushing volume of the target blasting area is determined by combining the rock crushing volume of each truck.

[0006] In a second aspect of the present application, a system for determining rock crushing volume in a blasting area is provided, the system comprising: A data acquisition module is used to acquire vehicle posture data, compartment deformation data, and compartment point cloud data of multiple trucks in the target explosion area; A data sample determination module is used to calculate the inclination angle of the carriage of each truck according to the vehicle posture data, and perform posture correction processing on the carriage point cloud data according to the carriage inclination angle to obtain a point cloud data sample of the truck; A point cloud data determination module, used to calculate the compartment volume deformation value of the truck according to the compartment deformation data, and perform deformation compensation processing on the point cloud data sample according to the compartment volume deformation value to obtain target point cloud data of the truck; The crushing volume determination module is used to determine the crushed rock volume of the truck based on the target point cloud data of the truck; and determine the rock crushing volume of the target blasting area in combination with the crushed rock volumes of each truck.

[0007] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a method for determining rock crushing volume in a blasting area when loaded and executed by the processor.

[0008] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a method for determining rock crushing volume in a blasting area.

[0009] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By adopting the above technical solution, the vehicle posture data, compartment deformation data and compartment point cloud data of multiple trucks in the target blasting area are obtained. First, the influence of the posture change during vehicle transportation on the measurement accuracy is solved. By calculating the inclination angle of the compartment and performing posture correction processing, the consistency of the spatial reference datum of the point cloud data is ensured. Secondly, in view of the deformation problem of the compartment under the loading state, by calculating the deformation value of the compartment volume and implementing deformation compensation processing, the interference of the compartment deformation on the measurement results is effectively eliminated, and the spatial accuracy of the point cloud data is improved. On this basis, the volume of single-vehicle crushed stone is calculated using the corrected and compensated target point cloud data, and the total volume of rock crushing in the target blasting area is obtained by cumulative statistics, realizing the transition from local measurement to overall evaluation. This systematic data processing method not only takes into account various influencing factors under actual transportation conditions, but also makes corrections through multi-dimensional data. By comprehensively considering the error influence of the truck compartment on the volume of crushed stone during transportation, the accuracy of determining the rock crushing volume in the blasting area is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic flow chart of a method for determining rock crushing volume in a blasting area provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a system for determining rock crushing volume in a blasting area provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0011] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0012] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0013] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0014] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0015] The present application embodiment provides a method for determining the rock crushing volume in the blasting area. In one embodiment, please refer to Figure 1 , Figure 1 The flowchart of the method for determining the rock crushing volume in the blasting area provided in the embodiment of the present application can be implemented by a computer program, which can be integrated in an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run in a von Neumann system-based rock crushing volume determination system in the blasting area. Specifically, the method can include the following steps: Step 101: Obtain vehicle posture data, vehicle compartment deformation data, and vehicle compartment point cloud data of multiple trucks in the target explosion area.

[0016] Among them, vehicle attitude data refers to the vertical acceleration values ​​at the four corners of the truck compartment, which reflect the spatial attitude characteristics of the compartment during transportation. The vehicle attitude data includes the vertical acceleration values ​​corresponding to the front left corner, front right corner, rear left corner, and rear right corner of the compartment. The vertical acceleration sensors installed at the four corners of the compartment collect these data in real time, which can reflect the tilt state of the compartment relative to the horizontal plane. The difference in these vertical acceleration values ​​directly reflects the degree of tilt of the compartment in the front-to-back direction and the left-to-right direction.

[0017] The deformation data of the carriage refers to the structural deformation parameters generated by the truck carriage in the process of carrying gravel, mainly including two key indicators: the deflection value of the carriage floor and the deformation variable of the carriage side panel. The deflection value of the carriage floor reflects the degree of vertical deformation of the carriage floor under the gravity of the gravel, which can be monitored in real time by strain sensors installed at key positions of the floor; the deformation variable of the carriage side panel represents the degree of horizontal deformation of the side panel under the lateral pressure of the gravel, which is collected by strain sensors installed on the surface of the side panel.

[0018] The carriage point cloud data refers to the spatial data set obtained by scanning the gravel loaded in the carriage of a truck through a 3D laser scanner. These data record the spatial distribution characteristics of the gravel accumulation in the carriage in the form of 3D coordinate points. Specifically, the carriage point cloud data is composed of a large number of discrete spatial coordinate points, each of which contains precise spatial position information. The collection of these points together constitutes a digital expression of the surface morphology of the gravel in the carriage.

[0019] Specifically, in order to accurately obtain the rock crushing volume in the target blasting area, it is first necessary to conduct all-round data collection on the trucks transporting gravel in the blasting area. Vertical acceleration sensors are set at the four corners of the truck compartment to collect vehicle posture data in real time. The posture data can reflect the tilt state of the compartment during transportation; at the same time, strain sensors are installed at key positions of the floor and side panels of the compartment to collect deformation data of the compartment, including the deflection value of the floor and the deformation value of the side panels. These data can reflect the deformation of the compartment when carrying gravel; in addition, a three-dimensional laser scanner is installed on the top of the truck to scan the gravel in the compartment and obtain the point cloud data of the compartment. The point cloud data contains the three-dimensional spatial information of the gravel accumulation. The collection devices of these three types of data are uniformly managed and transmitted through the on-board control unit to ensure the synchronization and accuracy of the data. When the truck enters the target blasting area for loading and transporting gravel, these sensor devices will continue to work and collect relevant data in real time. This multi-dimensional data collection method can not only obtain the basic spatial distribution information of crushed rock, but also take into account the impact of vehicle posture changes and car body deformation during transportation on the measurement results, providing a comprehensive data basis for subsequent data processing and volume calculation, thereby improving the accuracy of rock crushing volume measurement in the blasting area.

[0020] Step 102: For each truck, the inclination angle of the truck compartment is calculated according to the vehicle posture data, and the posture correction processing of the compartment point cloud data is performed according to the inclination angle of the compartment to obtain a point cloud data sample of the truck.

[0021] The tilt angle of the carriage refers to the degree of deviation of the carriage from the standard horizontal plane, including the front-to-back tilt angle and the left-to-right tilt angle. The front-to-back tilt angle reflects the tilt of the carriage in the longitudinal direction, while the left-to-right tilt angle represents the tilt of the carriage in the lateral direction.

[0022] Point cloud data samples refer to the point cloud data of the carriage after attitude correction, which reflects the spatial distribution characteristics of the gravel in the carriage under the standard horizontal state. The point cloud data samples are composed of spatial feature points after coordinate transformation. Each feature point contains the corrected target longitudinal coordinates and target transverse coordinates. These coordinate values ​​eliminate the influence of the carriage tilt and accurately reflect the actual spatial position of the gravel under the horizontal reference plane.

[0023] Specifically, in order to eliminate the influence of the tilt of the carriage on the measurement of the gravel volume during vehicle transportation, the collected point cloud data needs to be subjected to attitude correction processing. First, the tilt angle of the carriage is calculated based on the vertical acceleration values ​​of the four corner points of the carriage: by calculating the first acceleration mean of the vertical acceleration values ​​of the two corner points on the front side of the carriage and the second acceleration mean of the vertical acceleration values ​​of the two corner points on the rear side, the vertical acceleration difference between the front and rear sides can be obtained; similarly, the third acceleration mean of the vertical acceleration values ​​of the two corner points on the left side and the fourth acceleration mean of the vertical acceleration values ​​of the two corner points on the right side are calculated to obtain the vertical acceleration difference between the left and right sides. Then, according to the ratio of the vertical acceleration difference between the front and rear sides to the length of the carriage, the front and rear tilt angle of the carriage can be determined; according to the ratio of the vertical acceleration difference between the left and right sides to the width of the carriage, the left and right tilt angles of the carriage can be determined. These two angles together constitute the tilt angle of the carriage. After obtaining the tilt angle of the carriage, the carriage point cloud data needs to be subjected to posture correction processing: first, construct a three-dimensional coordinate system for the carriage and determine the spatial coordinates of each feature point in the point cloud data in the coordinate system; then, correct the longitudinal coordinates of the feature points according to the front and rear tilt angles to obtain the target longitudinal coordinates; then, correct the transverse coordinates of the feature points according to the left and right tilt angles to obtain the target transverse coordinates. Finally, based on the corrected target longitudinal coordinates and target transverse coordinates, a point cloud data sample with posture correction can be obtained. Through this correction processing, the point cloud data collected in an inclined state can be converted into data in a standard horizontal state, effectively eliminating the influence of the carriage tilt on the gravel volume measurement, and laying an accurate data foundation for subsequent deformation compensation and volume calculation.

[0024] Based on the above embodiment, as an optional embodiment, in step 102: calculating the inclination angle of the truck compartment according to the vehicle posture data, this step may also include the following steps: Step 201: Calculate a first acceleration mean of the vertical acceleration values ​​of the two corner points on the front side of the vehicle and a second acceleration mean of the vertical acceleration values ​​of the two corner points on the rear side of the vehicle, and determine the front and rear vertical acceleration difference of the truck based on the first acceleration mean and the second acceleration mean.

[0025] The vertical acceleration values ​​of the two corner points at the front of the car refer to the acceleration data collected in real time by the vertical acceleration sensors installed at the front left corner point and the front right corner point of the car. These data reflect the acceleration changes of the front of the car in the vertical direction, including the vertical acceleration values ​​measured at the front left corner point and the vertical acceleration values ​​measured at the front right corner point.

[0026] The first acceleration mean refers to the arithmetic mean of the vertical acceleration values ​​of the front left corner point and the front right corner point of the car, which represents the overall vertical acceleration state of the front part of the car.

[0027] The vertical acceleration values ​​of the two corner points at the rear of the car refer to the acceleration data collected in real time by the vertical acceleration sensors installed at the rear left corner point and rear right corner point of the car. These data reflect the acceleration changes of the rear of the car in the vertical direction, including the vertical acceleration values ​​measured at the rear left corner point and the rear right corner point.

[0028] The second acceleration mean refers to the arithmetic mean of the vertical acceleration values ​​of the rear left corner point and the rear right corner point of the car, which represents the overall vertical acceleration state of the rear part of the car.

[0029] The front and rear vertical acceleration difference refers to the difference between the first acceleration average of the two corner points on the front side of the car and the second acceleration average of the two corner points on the rear side. It reflects the degree of inclination of the car in the front and rear direction.

[0030] Specifically, in order to accurately calculate the front and rear tilt of the car, it is necessary to first determine the vertical acceleration difference between the front and rear sides of the car. Through the vertical acceleration sensors installed at the four corners of the car, the vertical acceleration values ​​of the front left corner and the front right corner, as well as the vertical acceleration values ​​of the rear left corner and the rear right corner can be collected respectively. In order to eliminate the influence of the measurement error of a single sensor, the mean calculation method is adopted: the vertical acceleration values ​​of the front left corner and the front right corner of the car are arithmetic averaged to obtain the first acceleration mean, which reflects the overall vertical acceleration state of the front side of the car; similarly, the vertical acceleration values ​​of the rear left corner and the rear right corner of the car are arithmetic averaged to obtain the second acceleration mean, which reflects the overall vertical acceleration state of the rear side of the car. Then, by calculating the difference between the first acceleration mean and the second acceleration mean, the vertical acceleration difference between the front and rear sides can be obtained. The positive and negative sign and size of this difference directly reflect the direction and degree of the front and rear tilt of the car: when the difference is positive, it means that the front of the car is higher than the rear, showing a state of high front and low back; when the difference is negative, it means that the front of the car is lower than the rear, showing a state of low front and high back; the larger the absolute value of the difference, the greater the degree of front and rear tilt. This method based on mean calculation not only improves the reliability of measurement, but also provides an accurate data basis for the subsequent calculation of the front and rear tilt angles.

[0031] Step 202: Calculate the third acceleration mean of the vertical acceleration values ​​of the two corner points on the left side of the vehicle and the fourth acceleration mean of the vertical acceleration values ​​of the two corner points on the right side of the vehicle, and determine the vertical acceleration difference between the left and right sides of the truck based on the third acceleration mean and the fourth acceleration mean.

[0032] The vertical acceleration values ​​of the two corner points on the left side of the car refer to the acceleration data collected in real time by the vertical acceleration sensors installed at the front left corner point and the rear left corner point of the car. These data reflect the acceleration changes on the left side of the car in the vertical direction, including the vertical acceleration values ​​measured at the front left corner point and the vertical acceleration values ​​measured at the rear left corner point.

[0033] The third acceleration mean refers to the arithmetic mean of the vertical acceleration values ​​of the front left corner point and the rear left corner point of the car, which represents the overall vertical acceleration state of the left side of the car.

[0034] The vertical acceleration values ​​of the two corner points on the right side of the car refer to the acceleration data collected in real time by the vertical acceleration sensors installed at the front right corner point and the rear right corner point of the car. These data reflect the acceleration changes on the right side of the car in the vertical direction, including the vertical acceleration values ​​measured at the front right corner point and the vertical acceleration values ​​measured at the rear right corner point.

[0035] The fourth acceleration mean refers to the arithmetic mean of the vertical acceleration values ​​of the front right corner point and the rear right corner point of the car, which represents the overall vertical acceleration state of the right side of the car.

[0036] The vertical acceleration difference between the left and right sides refers to the difference between the average of the third acceleration of the two corner points on the left side of the car and the average of the fourth acceleration of the two corner points on the right side. It reflects the degree of inclination of the car in the left and right directions.

[0037] Specifically, in order to accurately calculate the left and right tilt of the car, it is necessary to first determine the vertical acceleration difference between the left and right sides of the car. Through the vertical acceleration sensors installed at the four corners of the car, the vertical acceleration values ​​of the front left corner and the rear left corner, as well as the vertical acceleration values ​​of the front right corner and the rear right corner can be collected respectively. In order to eliminate the influence of the measurement error of a single sensor, the mean calculation method is adopted: the vertical acceleration values ​​of the front left corner and the rear left corner of the car are arithmetic averaged to obtain the third acceleration mean, which reflects the overall vertical acceleration state of the left side of the car; similarly, the vertical acceleration values ​​of the front right corner and the rear right corner of the car are arithmetic averaged to obtain the fourth acceleration mean, which reflects the overall vertical acceleration state of the right side of the car. Then, by calculating the difference between the third acceleration mean and the fourth acceleration mean, the vertical acceleration difference between the left and right sides can be obtained. The positive or negative and size of this difference directly reflect the direction and degree of the left-right tilt of the car: when the difference is positive, it means that the left side of the car is higher than the right side, showing a state of left high and right low; when the difference is negative, it means that the left side of the car is lower than the right side, showing a state of left low and right high; the larger the absolute value of the difference, the greater the degree of left-right tilt. This method based on mean calculation not only improves the reliability of measurement, but also provides an accurate data basis for the subsequent calculation of left and right tilt angles. Step 203: Determine the front-rear tilt angle of the vehicle body according to the ratio between the front-rear vertical acceleration difference and the length of the vehicle body.

[0038] The front-to-back tilt angle refers to the angle between the longitudinal centerline of the car and the horizontal plane, which quantitatively represents the degree to which the car deviates from the horizontal state in the front-to-back direction. The front-to-back tilt angle is calculated by the ratio of the front-to-back vertical acceleration difference to the length of the car using the inverse tangent function. The positive and negative value and size of the value directly reflect the direction and degree of the front-to-back tilt of the car.

[0039] Specifically, in order to accurately determine the tilt angle of the car in the front-to-back direction, it is necessary to use the obtained front-to-back vertical acceleration difference and combine it with the actual length of the car for calculation. Since the front-to-back vertical acceleration difference reflects the relative height difference between the front and rear ends of the car, and the length of the car is a fixed known parameter, the front-to-back tilt angle of the car can be calculated by the ratio relationship between these two values ​​and the inverse trigonometric function principle. Specifically, the front-to-back vertical acceleration difference is divided by the length of the car to obtain a ratio, which is actually the tangent value of the front-to-back tilt angle, and then the front-to-back tilt angle of the car is calculated by the inverse tangent function. When the front-to-back vertical acceleration difference is positive, the calculated angle is positive, indicating that the car is in a tilted state with the front high and the back low; when the front-to-back vertical acceleration difference is negative, the calculated angle is negative, indicating that the car is in a tilted state with the front low and the back high; the larger the absolute value of the angle, the greater the degree of front-to-back tilt of the car. This calculation method based on geometric relationships is not only simple to calculate, but also can accurately reflect the actual tilt state of the car, providing precise angle parameters for subsequent point cloud data posture correction, effectively improving the accuracy of car volume measurement.

[0040] Step 204: Determine the left and right tilt angles of the compartment according to the ratio between the left and right vertical acceleration differences and the width of the compartment; and use the front and rear tilt angles and the left and right tilt angles as the compartment tilt angles of the truck.

[0041] The left-right tilt angle refers to the angle between the transverse centerline of the carriage and the horizontal plane, which quantitatively represents the degree to which the carriage deviates from the horizontal state in the left-right direction. The left-right tilt angle is calculated by the ratio of the left-right vertical acceleration difference to the width of the carriage using the inverse tangent function. The positive and negative value and size of the value directly reflect the direction and degree of the left-right tilt of the carriage.

[0042] Specifically, in order to accurately determine the tilt angle of the car in the left and right directions, it is necessary to use the obtained left and right vertical acceleration difference and combine it with the actual width of the car for calculation. Since the left and right vertical acceleration difference reflects the relative height difference between the left and right sides of the car, and the width of the car is a fixed known parameter, the left and right tilt angles of the car can be calculated by the ratio relationship between the two values ​​and the inverse trigonometric function principle. Specifically, the left and right vertical acceleration difference is divided by the width of the car to obtain a ratio, which is actually the tangent value of the left and right tilt angles, and then the left and right tilt angles of the car are calculated by the inverse tangent function. When the left and right vertical acceleration difference is positive, the calculated angle is positive, indicating that the car is in a tilted state with the left high and the right low; when the left and right vertical acceleration difference is negative, the calculated angle is negative, indicating that the car is in a tilted state with the left low and the right high; the larger the absolute value of the angle, the greater the left and right tilt degree of the car. The calculated front and rear tilt angles and left and right tilt angles are used together as the car tilt angle. These two angle parameters fully describe the spatial posture of the car and provide accurate rotation parameters for subsequent point cloud data correction. Through this calculation method based on geometric relationships, not only can the precise tilt angles of the car body in two directions be obtained, but it also provides a reliable basis for achieving precise posture correction of point cloud data, thereby improving the accuracy of car body volume measurement.

[0043] Based on the above embodiment, as an optional embodiment, in step 102: performing posture correction processing on the carriage point cloud data according to the carriage inclination angle to obtain a point cloud data sample of the truck, this step may also include the following steps: Step 205: construct a three-dimensional coordinate system for the truck compartment, and determine the spatial coordinates corresponding to each feature point in the compartment point cloud data in the three-dimensional coordinate system for the compartment.

[0044] The three-dimensional coordinate system of the carriage refers to a right-handed coordinate system established with the left front corner of the carriage floor as the origin, the length of the carriage as the positive direction of the X axis, the width as the positive direction of the Y axis, and the height as the positive direction of the Z axis. Specifically, this coordinate system selects fixed feature points on the carriage entity as the reference, and describes the spatial position of any point in the carriage by establishing a standard spatial rectangular coordinate system.

[0045] Feature points refer to the set of spatial points in the car point cloud data that have obvious geometric features and are of great significance to the characterization of the car structure.

[0046] Specifically, in order to accurately describe the spatial structure of the car and calculate the volume of the car, it is necessary to establish a standardized three-dimensional coordinate system for the car. This coordinate system selects the left front corner of the car floor as the origin, takes the length direction of the car as the positive direction of the X-axis, the width direction as the positive direction of the Y-axis, and the height direction as the positive direction of the Z-axis, forming a right-handed coordinate system. This way of establishing a coordinate system not only conforms to the geometric characteristics of the car, but also facilitates subsequent data processing and calculation. After determining the coordinate system, it is necessary to map each feature point in the car point cloud data to the coordinate system to obtain their corresponding spatial coordinates. Specifically, the original point cloud data collected by the laser radar is converted to the three-dimensional coordinate system of the car through coordinate transformation. This process needs to consider the relative relationship between the installation position of the laser radar and the car, and use the rotation matrix and translation vector to transform the coordinate system. Then, the feature points of the converted point cloud data are extracted, including key geometric features such as corner points and edge lines of the car, and the precise spatial coordinates of these feature points in the three-dimensional coordinate system of the car are determined. Through this standardized coordinate system establishment and feature point mapping, not only the spatial structure of the car is expressed in a unified and standardized manner, but also a reliable spatial position reference is provided for subsequent volume calculations.

[0047] Step 206: For each feature point, the longitudinal coordinate in the space coordinate is corrected according to the front-rear tilt angle in the vehicle body tilt angle to obtain the target longitudinal coordinate of the feature point.

[0048] The target longitudinal coordinate refers to the coordinate value of the feature point in the X-axis direction in the three-dimensional coordinate system of the car after the front and rear tilt angle is corrected. It reflects the true longitudinal position of the feature point when the car is in a horizontal state. The target longitudinal coordinate is obtained by rotating the spatial coordinates of the original feature point. Its value represents the precise spatial position of the feature point along the length direction of the car after eliminating the influence of the front and rear tilt.

[0049] Specifically, in order to eliminate the influence of the front and rear tilt of the car body on the volume measurement, the spatial coordinates of the feature points need to be corrected longitudinally. Due to the front and rear tilt angle of the car body, there is a deviation between the longitudinal coordinate values ​​of the feature points in the point cloud data and the coordinate values ​​in the actual horizontal state, which will affect the accuracy of the subsequent volume calculation. The specific correction process is based on the front and rear tilt angle, and the longitudinal coordinates of each feature point are corrected by the method of coordinate rotation transformation. First, the left front corner point of the car body floor is used as the rotation center, and a rotation matrix is ​​established according to the front and rear tilt angle. The matrix describes the spatial transformation relationship required to convert the tilted state coordinates into the horizontal state coordinates. Then, this rotation transformation is applied to the spatial coordinates of each feature point, and the target longitudinal coordinates of the feature point in the horizontal state are obtained through matrix operation. When the front and rear tilt angle is positive, the longitudinal coordinates of the front feature point will be adjusted downward, and the longitudinal coordinates of the rear feature point will be adjusted upward; when the front and rear tilt angle is negative, the adjustment is made in the opposite direction. Through this coordinate correction based on geometric transformation, the feature points collected in the tilted state can be accurately restored to the horizontal state, eliminating the influence of the front and rear tilt on the spatial position measurement, laying the foundation for the subsequent lateral coordinate correction and accurate volume calculation, and improving the accuracy of the entire measurement system.

[0050] Step 207: According to the left and right tilt angles in the car body tilt angle, the transverse coordinates in the space coordinates are corrected to obtain the target transverse coordinates of the feature points; according to the target longitudinal coordinates and the target transverse coordinates of each feature point, the point cloud data sample of the truck is determined.

[0051] The target transverse coordinate refers to the coordinate value of the feature point in the Y-axis direction in the three-dimensional coordinate system of the car after the left and right tilt angles are corrected. It reflects the true transverse position of the feature point when the car is in a horizontal state. The target transverse coordinate is obtained by rotating the spatial coordinates of the original feature point. Its value represents the precise spatial position of the feature point along the width direction of the car after eliminating the influence of left and right tilt.

[0052] Specifically, in order to further eliminate the influence of the left and right tilt of the car body on the volume measurement, it is necessary to correct the transverse coordinates of the feature points on the basis of completing the longitudinal coordinate correction. Due to the left and right tilt angles of the car body, the transverse coordinate values ​​of the feature points in the point cloud data deviate from the coordinate values ​​in the actual horizontal state, and this deviation will also affect the measurement accuracy. The specific correction process is based on the left and right tilt angles, and a coordinate rotation transformation method similar to the longitudinal correction is used. First, the left front corner point of the car body floor is used as the rotation center, and a rotation matrix is ​​established according to the left and right tilt angles. The matrix describes the spatial transformation relationship required to convert the tilted state coordinates into the horizontal state coordinates. Then, this rotation transformation is applied to the spatial coordinates of each feature point, and the target transverse coordinates of the feature point in the horizontal state are obtained through matrix operation. When the left and right tilt angles are positive, the transverse coordinates of the left feature point will be adjusted downward, and the transverse coordinates of the right feature point will be adjusted upward; when the left and right tilt angles are negative, the adjustment is performed in the opposite direction. After completing the transverse coordinate correction, combined with the obtained target longitudinal coordinates, the accurate spatial position of the feature point in the horizontal state can be determined, and then the point cloud data sample with complete posture correction can be obtained. Through this bidirectional coordinate correction, not only the influence of the car tilt on the spatial position measurement is completely eliminated, but also the point cloud data samples can truly reflect the geometric characteristics of the car, providing a high-precision spatial data foundation for subsequent volume calculation.

[0053] Step 103: Calculate the volume deformation value of the truck compartment according to the deformation data of the truck compartment, and perform deformation compensation processing on the point cloud data samples according to the deformation value of the truck compartment to obtain the target point cloud data of the truck.

[0054] The volume deformation value of the carriage refers to the volume change caused by the deformation of various parts of the carriage due to the weight of the cargo loading. It quantifies the degree of deviation of the actual volume of the carriage from the ideal state. The volume deformation value of the carriage is obtained by comprehensive calculation of the deformation data of multiple parts such as the sinking deformation of the carriage floor and the expansion deformation of the side panels. Its value directly reflects the overall deformation degree of the carriage under the loading state.

[0055] The target point cloud data refers to the three-dimensional point cloud data of the car after the tilt angle correction and deformation compensation processing, which reflects the real spatial geometric characteristics of the car in an ideal horizontal and non-deformed state. The target point cloud data is obtained by performing posture correction and deformation compensation on the original point cloud data samples. The posture correction is based on the coordinate transformation of the front and rear tilt angles and the left and right tilt angles, and the deformation compensation is based on the volume deformation value of the car to correct the spatial position of the point.

[0056] Specifically, in order to accurately measure the volume of the truck compartment, it is necessary to consider the deformation effect of the compartment caused by the load-bearing during the actual loading process. The deformation data of the compartment collected by the strain sensors installed around the compartment can reflect the degree of deformation at each position of the compartment. First, the deformation value of the compartment volume is calculated based on the deformation data of the compartment. The calculation process needs to consider the deformation of the compartment floor and side panels, as well as the impact of these deformations on the overall volume of the compartment. Specifically, the sinking deformation of the compartment floor and the expansion deformation of the side panels are multiplied by the corresponding compartment dimensions (length, width, height) to obtain the deformation volume in each direction, and these deformation volumes are superimposed to obtain the deformation value of the compartment volume. Then, the deformation compensation processing is performed on the point cloud data sample according to the calculated compartment volume deformation value. The core of the compensation processing is to correct the spatial coordinates of each point in the point cloud data. In the correction process, it is necessary to consider the correspondence between the position of the point and the deformation position, make larger coordinate adjustments to the points close to the deformation site, and make smaller adjustments to the points far away from the deformation site, so as to achieve a gradual transition of the deformation. Through this compensation processing based on measured deformation data, the influence of car deformation on volume measurement can be effectively eliminated, and target point cloud data closer to the actual state of the car can be obtained, which significantly improves the accuracy of car volume measurement. At the same time, this compensation method also provides a more reliable data basis for subsequent volume calculations, which helps to achieve accurate load measurement.

[0057] Based on the above embodiment, as an optional embodiment, in step 103: calculating the volume deformation value of the truck compartment according to the compartment deformation data, this step may also include the following steps: Step 301: Obtain the floor area and side panel height of the truck compartment.

[0058] The floor area of ​​the carriage refers to the projected area of ​​the carriage floor on the horizontal plane, which is determined by the product of the actual length and width of the carriage floor. The floor area of ​​the carriage is calculated by processing the spatial coordinates of the four corner points (left front corner, right front corner, left rear corner, and right rear corner) of the floor in the target point cloud data. Its value reflects the effective bottom bearing area of ​​the carriage when loading goods.

[0059] The height of the side panel of the carriage refers to the vertical distance from the upper edge of the side panel of the carriage to the plane of the bottom plate, which represents the spatial dimension of the carriage in the vertical direction. The height of the side panel of the carriage is calculated by the vertical projection distance between the upper edge line of the side panel and the plane of the bottom plate in the target point cloud data, and its value reflects the effective spatial height of the carriage when loading goods.

[0060] Specifically, in order to accurately calculate the volume of the car, it is necessary to first obtain the basic geometric size parameters of the car, namely the area of ​​the car floor and the height of the car side panels. These parameters are the basic data for calculating the volume of the car, which directly affect the accuracy of the measurement results. The parameters are obtained by processing and analyzing the target point cloud data. First, the spatial coordinates of the four corner points of the car floor are extracted, and the length and width of the floor are calculated according to these corner point coordinates. The length and width are multiplied to obtain the area of ​​the car floor. Then, the upper and lower edge lines of the car side panels are identified from the target point cloud data, and the height of the car side panels is obtained by calculating the vertical distance between the upper edge line of the side panel and the bottom plane. In this process, since the posture correction and deformation compensation of the point cloud data have been completed, the obtained bottom plate area and side panel height can accurately reflect the real size of the car. This method of obtaining size parameters based on target point cloud data takes into account the influence of the actual state of the car and provides reliable basic data for subsequent volume calculations.

[0061] Step 302: Calculate the product of the deflection value of the car floor and the area of ​​the car floor as the first volume deformation; calculate the product of the deformation value of the car side panel and the height of the car side panel as the second volume deformation; use the first volume deformation and the second volume deformation as the volume deformation value of the truck car compartment.

[0062] Among them, the first volume deformation refers to the volume change caused by the sinking deformation of the car floor under loading, which is determined by the product of the deflection value of the car floor and the area of ​​the car floor. The first volume deformation reflects the spatial volume loss caused by the sinking deformation of the floor when bearing the weight of the cargo. Its value is obtained by multiplying the actual sinking degree of the floor by the affected floor area, reflecting the degree of influence of the floor deformation on the overall volume of the car.

[0063] The second volumetric deformation refers to the volume change caused by the convex deformation of the side panel under loading, which is determined by the product of the deformation of the side panel and the height of the side panel. The second volumetric deformation reflects the increase in spatial volume caused by the lateral deformation of the side panel when subjected to cargo squeezing. Its value is obtained by multiplying the actual convexity of the side panel by the height of the affected side panel, reflecting the degree of influence of the deformation of the side panel on the overall volume of the compartment.

[0064] Specifically, in order to accurately evaluate the volume deformation of the compartment under loading, it is necessary to comprehensively consider the influence of the deformation of the bottom plate and the side plate on the compartment volume. Since the compartment will deform when loading goods, this deformation is mainly manifested as the sinking deflection of the bottom plate and the convex deformation of the side plate. These deformations will cause the actual volume to differ from the volume under the ideal state. The specific calculation process is to first determine the first volume deformation value, and multiply the deflection value of the compartment bottom plate by the area of ​​the compartment bottom plate to obtain the volume change caused by the sinking of the bottom plate. This calculation takes into account the influence of the overall sinking of the bottom plate on the volume and reflects the space loss caused by the deformation of the bottom plate. Then calculate the second volume deformation value, and multiply the deformation value of the compartment side plate by the height of the compartment side plate to obtain the volume change caused by the convex deformation of the side plate. This calculation reflects the degree of influence of the side plate deformation on the lateral space of the compartment. Finally, the first volume deformation value and the second volume deformation value are used as the compartment volume deformation value, which comprehensively reflects the overall volume change of the compartment under the actual loading state. This step-by-step deformation calculation method can not only accurately quantify the impact of deformation of different parts on volume, but also provide a data basis for subsequent volume compensation, thereby improving the accuracy of volume measurement.

[0065] Based on the above embodiment, as an optional embodiment, in step 103: performing deformation compensation processing on the point cloud data sample according to the compartment volume deformation value to obtain the target point cloud data of the truck, this step may also include the following steps: Step 303: Obtain the height between each feature point in the point cloud data sample and the vehicle floor, and determine the height compensation amount of each feature point according to the ratio of each height to the first volume deformation amount.

[0066] Among them, the height compensation refers to the adjustment value of the vertical position of the feature point to eliminate the influence of the deformation of the car floor. It is determined by the ratio of the height between the feature point and the car floor to the first volume deformation. The height compensation reflects how much the vertical coordinate of the feature point needs to be corrected to restore the true position of the feature point in the state of no deformation of the car. Its value decreases as the height of the feature point from the floor increases, reflecting the differentiated influence of the floor deformation on feature points of different heights.

[0067] Specifically, in order to accurately compensate for the influence of the deformation of the carriage on the spatial position of the feature points, it is necessary to perform differentiated height compensation for feature points of different heights. Since the sinking deformation of the carriage floor will cause the actual height of the feature points to deviate from the ideal state, and this deviation changes with the increase of the height of the feature points from the floor, it is necessary to establish a corresponding relationship between the height of the feature points and the deformation amount. The specific calculation process is to first extract the vertical height of each feature point relative to the carriage floor from the point cloud data sample, and these height values ​​reflect the relative position of the feature points in space. Then, the height of each feature point is calculated by ratio with the calculated first volume deformation amount, and this ratio reflects the degree of influence of the floor deformation on feature points of different heights. Based on this ratio relationship, the specific height compensation required for each feature point can be determined. The larger the height of the feature point, the less it is affected by the floor deformation, and the smaller the corresponding compensation amount. Through this compensation calculation method based on height difference, not only can the real spatial position of each feature point in the non-deformed state be accurately restored, but also the continuity and rationality of the compensation are guaranteed, providing more accurate spatial position data for subsequent volume calculation.

[0068] Step 304: Obtain the distance between each feature point in the point cloud data sample and the side panel of the vehicle compartment, and determine the distance compensation amount of each feature point according to the ratio of each distance to the second volume deformation amount.

[0069] Among them, in the embodiment of the present application, the distance compensation refers to the adjustment value of the lateral position of the feature point to eliminate the influence of the deformation of the side panel of the car body, which is determined by the ratio of the distance between the feature point and the side panel of the car body to the second volume deformation. The distance compensation reflects how much the lateral coordinate of the feature point needs to be corrected to restore the true position of the feature point in the state of no deformation of the car body. Its value decreases as the distance between the feature point and the side panel increases, reflecting the differentiated influence of the deformation of the side panel on the feature points at different lateral positions.

[0070] Specifically, in order to accurately compensate for the influence of the deformation of the side panel of the car body on the spatial position of the feature point, it is necessary to perform differentiated distance compensation for the feature points at different lateral positions. Since the convex deformation of the side panel of the car body will cause the actual lateral position of the feature point to deviate from the ideal state, and this deviation changes with the change of the distance between the feature point and the side panel, it is necessary to establish a corresponding relationship between the lateral distance of the feature point and the deformation amount. The specific calculation process is to first extract the lateral distance of each feature point relative to the car body side panel from the point cloud data sample, and these distance values ​​reflect the relative position of the feature point in the lateral space. Then, the distance of each feature point is calculated by the ratio of the calculated second volume deformation amount, and this ratio reflects the degree of influence of the side panel deformation on the feature points at different lateral positions. Based on this ratio relationship, the specific distance compensation required for each feature point can be determined. The farther the feature point is from the side panel, the less it is affected by the deformation of the side panel, and the smaller the corresponding compensation amount. Through this compensation calculation method based on distance difference, not only can the true lateral position of each feature point in the non-deformed state be accurately restored, but also the continuity and rationality of the compensation are guaranteed, providing more accurate spatial position data for subsequent volume calculation. This compensation method that takes distance differences into consideration effectively improves the accuracy of feature point position correction.

[0071] Step 305: Subtract the height compensation amount from the height of each feature point, and subtract the distance compensation amount from the distance of each feature point to obtain target point cloud data.

[0072] Specifically, in order to obtain the target point cloud data that accurately reflects the spatial distribution of cargo in the non-deformed state of the carriage, it is necessary to systematically correct the position of the feature points in the point cloud data sample. Since the carriage has deformations such as the sinking of the bottom plate and the convexity of the side plate when loaded, these deformations cause the position of the collected feature points to deviate from the actual position, so it is necessary to restore the real spatial position of the feature points through compensation calculation. The specific calculation process is to first correct the height value of each feature point, subtract the corresponding height compensation amount from its original height, and this subtraction operation eliminates the influence of the bottom plate sinking deformation on the vertical position of the feature point; then correct the lateral distance of each feature point, subtract the corresponding distance compensation amount from its original distance, and this subtraction operation eliminates the influence of the convexity deformation of the side plate on the lateral position of the feature point. Through these two steps of position correction, the spatial coordinates of the feature points are adjusted to the position in the non-deformed state, forming target point cloud data that accurately reflects the actual spatial distribution of the cargo.

[0073] Step 104: Based on the target point cloud data of the truck, determine the crushed rock volume of the truck; and determine the rock crushing volume of the target blasting area by combining the crushed rock volumes of each truck.

[0074] Among them, the crushed stone volume refers to the spatial volume of the rock fragments after blasting loaded in a single truck compartment. It is calculated by performing three-dimensional spatial analysis on the feature points reflecting the surface contour of the crushed stone in the target point cloud data. The crushed stone volume reflects the actual space occupied by the accumulation of crushed stone in the compartment. Its value is calculated after considering the irregular accumulation form of the crushed stone, surface fluctuations and deformation compensation of the compartment, reflecting the actual load of a single vehicle.

[0075] The rock crushing volume refers to the total volume of all rock fragments after blasting in the target blasting area. It is obtained by accumulating the volume of rock fragments in all transport trucks. The rock crushing volume reflects the actual spatial volume after the blasting operation breaks the solid rock mass.

[0076] Specifically, in order to accurately evaluate the effect of blasting operations and calculate the volume of rock crushing, it is necessary to measure the volume of each truck transporting gravel and make cumulative statistics. Since the gravel produced in the target blasting area is transported in batches, the measurement result of the gravel volume of a single truck is directly related to the calculation accuracy of the rock crushing volume in the entire blasting area. Therefore, it is necessary to perform accurate volume calculation based on the acquired target point cloud data. The specific calculation process first uses the target point cloud data after deformation compensation and position correction to calculate the spatial contour and accumulation form of the gravel in each truck compartment. By analyzing the three-dimensional spatial distribution of the feature points on the surface of the gravel, combined with parameters such as the floor area and the side panel height of the compartment, the numerical integration method is used to calculate the gravel volume of a single truck. Then, the gravel volume of all transport vehicles is cumulatively counted to obtain the overall rock crushing volume of the target blasting area. This volume calculation method based on point cloud data not only takes into account the actual accumulation state of the gravel, but also improves the calculation accuracy by compensating for the influence of deformation, making the final rock crushing volume more accurate and reliable.

[0077] Based on the above embodiment, as an optional embodiment, in step 104: determining the gravel volume of the truck based on the target point cloud data of the truck, this step may also include the following steps: Step 401: Acquire the empty point cloud data of the truck compartment; determine the gravel point cloud data of the truck compartment based on the empty point cloud data of the truck compartment and the target point cloud data.

[0078] Among them, the empty car point cloud data refers to the set of spatial feature points of the car structure obtained through 3D scanning when the truck is not loaded with gravel. It reflects the original spatial form of the car floor, side panels and their connection structures. The empty car point cloud data contains the 3D coordinate information and structural features of each part of the car. The data is collected in the standard state of no load and no deformation of the car, reflecting the reference spatial characteristics of the car body.

[0079] Gravel point cloud data refers to the three-dimensional point set that reflects only the spatial characteristics of gravel accumulation after comparing the target point cloud data with the empty carriage point cloud data. It excludes the spatial information of the carriage structure and specifically describes the surface contour and spatial distribution of gravel. Gravel point cloud data reflects the actual morphological characteristics of gravel accumulation in the carriage. Its data is a pure point cloud set obtained after eliminating the influence of the carriage structure, reflecting the real distribution state of gravel in three-dimensional space.

[0080] Specifically, in order to accurately obtain the spatial distribution characteristics of gravel, it is necessary to eliminate the influence of the structure of the carriage itself on the point cloud data and realize the accurate extraction of gravel point cloud data. Since the collected target point cloud data contains the spatial information of the carriage structure and gravel at the same time, these mixed spatial information will affect the calculation accuracy of the gravel volume, so it is necessary to separate the data through the reference data in the unloaded state. The specific processing process is to first obtain the point cloud data of the carriage in the unloaded state of the truck, which reflects the structural characteristics and spatial contours of the carriage itself; then the unloaded point cloud data of the carriage is spatially compared and processed with the acquired target point cloud data, and the feature points corresponding to the carriage structure are excluded by identifying and matching the feature points at the same spatial position, and the feature point set that only reflects the spatial distribution of gravel is extracted, so as to obtain accurate gravel point cloud data. Through this data processing method based on unloaded reference, not only can the spatial information of gravel be accurately separated, but also the interference of the structure characteristics of the carriage on the volume calculation is avoided, which provides a pure data basis for the subsequent gravel volume calculation. This data processing method considering the unloaded state effectively improves the extraction accuracy of gravel point cloud data, thereby ensuring the accuracy of volume measurement results.

[0081] Step 402: construct a three-dimensional model corresponding to the gravel point cloud data of the truck.

[0082] The 3D model refers to a closed space entity model built based on the gravel point cloud data. It converts discrete feature points into continuous geometric bodies through spatial interpolation and surface reconstruction, and fully describes the spatial form of gravel accumulation. The 3D model reflects the overall spatial contour and volume characteristics of the gravel. Its structure is a continuous geometric body established after considering the surface fluctuations and spatial distribution characteristics of the gravel, reflecting the actual accumulation state of the gravel in the carriage.

[0083] Specifically, in order to achieve accurate calculation of gravel volume, it is necessary to convert the discrete gravel point cloud data into a continuous three-dimensional spatial model to accurately describe the spatial distribution and volume characteristics of gravel. Since the gravel point cloud data only contains a limited number of spatial sampling points, these discrete point sets are difficult to be directly used for volume calculation, so it is necessary to construct a complete spatial model through a three-dimensional modeling method. The specific modeling process first performs spatial interpolation processing on the gravel point cloud data, and establishes a continuous spatial surface by analyzing the positional relationship between adjacent feature points; then, based on the interpolated spatial surface and the car floor as the reference surface, a closed three-dimensional solid model is constructed, which accurately reflects the overall shape and spatial contour of the gravel accumulation. Through this three-dimensional modeling method based on point cloud data, not only the conversion from discrete point sets to continuous models is realized, but also the consistency between the model and the actual spatial distribution of gravel is guaranteed, providing a reliable geometric basis for subsequent volume calculation. This modeling method that considers spatial continuity effectively improves the expression accuracy of the spatial characteristics of gravel.

[0084] Step 403: Calculate the volume of the three-dimensional model, and use the volume of the three-dimensional model as the gravel volume of the truck.

[0085] Specifically, in order to obtain accurate values ​​of the volume of gravel, it is necessary to perform numerical integration calculations on the constructed three-dimensional model, thereby realizing the conversion from spatial geometric features to volume values. Since the three-dimensional model fully describes the spatial distribution characteristics of gravel, this closed geometric body has a strict correspondence with the actual volume of gravel, so the actual volume of gravel can be determined by calculating the volume of the three-dimensional model. The specific calculation process is to first grid the three-dimensional model in space, and discretize the entire model by establishing tiny volume units; then perform numerical integration on all volume units, accumulate the volume of each tiny unit, and obtain the volume value of the entire three-dimensional model, which is the volume of gravel loaded on the truck. Through this volume calculation method based on numerical integration, not only the accurate measurement of irregular spatial forms is achieved, but also the reliability of the calculation results is guaranteed, providing accurate data for the statistics of gravel transportation volume.

[0086] Based on the above embodiment, as an optional embodiment, in step 104: combining the rock crushing volume of each truck to determine the rock crushing volume of the target blasting area, this step may also include the following steps: Step 404: Accumulate the rock crushing volumes of each truck to obtain the rock crushing volume of the target blasting area.

[0087] Specifically, in order to obtain the total rock crushing volume in the target blasting area, it is necessary to systematically accumulate statistics on the crushed rock volume of all transport vehicles, so as to realize the data conversion from single vehicle measurement to overall evaluation. Since the crushed rock after blasting needs to be transported in batches, the measurement results of the crushed rock volume of a single vehicle are all components of the total rock crushing volume in the target blasting area, so it is necessary to obtain the complete crushed volume through data accumulation. The specific statistical process is to sequentially record the crushed rock volume measured by each truck that has completed the transportation, and through continuous accumulation operations, sum up the crushed rock volume of all transport vehicles, and finally obtain the total volume value reflecting the rock crushing effect of the entire target blasting area. Through this calculation method based on cumulative statistics, not only the data conversion from local to overall is realized, but also the integrity of the crushed volume statistics is guaranteed, providing comprehensive data support for the evaluation of the blasting operation effect. This statistical method that considers the entire transportation process effectively solves the problem of total amount statistics under batch transportation conditions, thereby improving the accuracy of blasting engineering quantity accounting.

[0088] Reference Figure 2 , is a system for determining rock crushing volume in an explosion area provided in an embodiment of the present application, the system comprising: a data acquisition module, a data sample determination module, a point cloud data determination module, and a crushing volume determination module, wherein: A data acquisition module is used to acquire vehicle posture data, compartment deformation data, and compartment point cloud data of multiple trucks in the target explosion area; A data sample determination module is used to calculate the inclination angle of the truck compartment according to the vehicle posture data for each truck, and perform posture correction processing on the compartment point cloud data according to the inclination angle of the compartment to obtain a point cloud data sample of the truck; The point cloud data determination module is used to calculate the volume deformation value of the truck compartment according to the deformation data of the compartment, and to perform deformation compensation processing on the point cloud data sample according to the deformation value of the compartment volume to obtain the target point cloud data of the truck; The crushing volume determination module is used to determine the crushed rock volume of the truck based on the target point cloud data of the truck; and to determine the rock crushing volume of the target blasting area by combining the crushed rock volumes of each truck.

[0089] On the basis of the above embodiment, the data sample determination module is also used to calculate the first acceleration mean of the vertical acceleration values ​​of the two corner points on the front side of the car and the second acceleration mean of the vertical acceleration values ​​of the two corner points on the rear side of the car, and determine the front and rear vertical acceleration difference of the truck based on the first acceleration mean and the second acceleration mean; calculate the third acceleration mean of the vertical acceleration values ​​of the two corner points on the left side of the car and the fourth acceleration mean of the vertical acceleration values ​​of the two corner points on the right side of the car, and determine the left and right vertical acceleration difference of the truck based on the third acceleration mean and the fourth acceleration mean; determine the front and rear inclination angle of the car according to the ratio between the front and rear vertical acceleration difference and the length of the car; determine the left and right inclination angle of the car according to the ratio between the left and right vertical acceleration difference and the width of the car; and use the front and rear inclination angle and the left and right inclination angle as the car body inclination angle of the truck.

[0090] On the basis of the above embodiment, the data sample determination module is also used to construct a three-dimensional coordinate system of the truck compartment, and determine the spatial coordinates corresponding to each feature point in the compartment point cloud data in the three-dimensional coordinate system of the compartment; for each feature point, the longitudinal coordinate in the spatial coordinate is corrected according to the front and rear tilt angle in the compartment tilt angle to obtain the target longitudinal coordinate of the feature point; the transverse coordinate in the spatial coordinate is corrected according to the left and right tilt angle in the compartment tilt angle to obtain the target transverse coordinate of the feature point; the point cloud data sample of the truck is determined according to the target longitudinal coordinate and the target transverse coordinate of each feature point.

[0091] Based on the above embodiments, the point cloud data determination module is also used to obtain the truck's cabin floor area and cabin side panel height; calculate the product of the cabin floor deflection value and the cabin floor area as the first volume deformation variable; calculate the product of the cabin side panel deformation variable and the cabin side panel height as the second volume deformation variable; and use the first volume deformation variable and the second volume deformation variable as the truck's cabin volume deformation values.

[0092] On the basis of the above embodiments, the point cloud data determination module is also used to obtain the height between each feature point in the point cloud data sample and the car floor, and determine the height compensation amount of each feature point according to the ratio of each height to the first volume deformation variable; obtain the distance between each feature point in the point cloud data sample and the car side panel, and determine the distance compensation amount of each feature point according to the ratio of each distance to the second volume deformation variable; subtract the height compensation amount from the height of each feature point, and subtract the distance compensation amount from the distance of each feature point to obtain the target point cloud data.

[0093] Based on the above embodiment, the crushed volume determination module is also used to obtain the empty point cloud data of the truck compartment; determine the gravel point cloud data of the truck based on the empty point cloud data of the truck compartment and the target point cloud data; construct a three-dimensional model corresponding to the gravel point cloud data of the truck; calculate the volume of the three-dimensional model, and use the volume of the three-dimensional model as the gravel volume of the truck.

[0094] On the basis of the above embodiment, the crushed volume determination module is further used to accumulate the crushed rock volumes of each truck to obtain the rock crushed volume of the target blasting area.

[0095] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0096] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0097] The communication bus 302 is used to realize the connection and communication between these components.

[0098] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0099] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0100] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0101] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 The memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program for a method for determining rock crushing volume in a blasting area.

[0102] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program stored in the memory 305 for a method for determining the rock crushing volume in the blasting area. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0103] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0108] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0109] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

Claims

1. A method for determining the rock crushing volume in a blasting area, characterized in that: include: Obtain vehicle posture data, compartment deformation data, and compartment point cloud data of multiple trucks in the target explosion area; For each of the trucks, the inclination angle of the truck compartment is calculated according to the vehicle posture data, and the posture correction processing is performed on the truck compartment point cloud data according to the inclination angle of the truck compartment to obtain a point cloud data sample of the truck; Calculating the volume deformation value of the truck compartment according to the truck compartment deformation data, and performing deformation compensation processing on the point cloud data sample according to the truck compartment volume deformation value to obtain target point cloud data of the truck; determining a gravel volume of the truck based on the target point cloud data of the truck; The rock crushing volume of the target blasting area is determined by combining the rock crushing volume of each truck.

2. The method for determining rock crushing volume in the blasting area according to claim 1, characterized in that: The vehicle posture data includes vertical acceleration values ​​corresponding to four corner points in the compartment of the truck, and the calculating the inclination angle of the compartment of the truck according to the vehicle posture data includes: Calculating a first acceleration average of vertical acceleration values ​​of two corner points on the front side of the vehicle and a second acceleration average of vertical acceleration values ​​of two corner points on the rear side of the vehicle, and determining a front and rear vertical acceleration difference of the truck based on the first acceleration average and the second acceleration average; Calculating a third acceleration average of vertical acceleration values ​​of two corner points on the left side of the carriage and a fourth acceleration average of vertical acceleration values ​​of two corner points on the right side of the carriage, and determining a vertical acceleration difference between the left and right sides of the truck based on the third acceleration average and the fourth acceleration average; Determining the front-rear tilt angle of the carriage according to the ratio between the front-rear vertical acceleration difference and the length of the carriage; Determining the left and right tilt angles of the carriage according to the ratio between the left and right vertical acceleration differences and the width of the carriage; The front-rear tilt angle and the left-right tilt angle are used as the vehicle body tilt angle of the truck.

3. The method for determining rock crushing volume in the blasting area according to claim 1, characterized in that: The performing posture correction processing on the carriage point cloud data according to the carriage inclination angle to obtain the point cloud data sample of the truck includes: Constructing a three-dimensional coordinate system of the truck compartment, and determining the spatial coordinates corresponding to each feature point in the compartment point cloud data in the three-dimensional coordinate system of the truck compartment; For each of the feature points, the longitudinal coordinate in the space coordinate is corrected according to the front-rear tilt angle in the car body tilt angle to obtain the target longitudinal coordinate of the feature point; According to the left and right tilt angles in the tilt angle of the carriage, the lateral coordinates in the space coordinates are corrected to obtain the target lateral coordinates of the feature point; A point cloud data sample of the truck is determined according to the target longitudinal coordinate and the target transverse coordinate of each of the feature points.

4. The method for determining rock crushing volume in the blasting area according to claim 1, characterized in that: The compartment deformation data includes a compartment floor deflection value and a compartment side plate deformation value. The calculation of the compartment volume deformation value of the truck according to the compartment deformation data includes: Obtaining the floor area and side panel height of the truck; Calculating the product of the deflection value of the carriage floor and the area of ​​the carriage floor as the first volume deformation; Calculating the product of the deformation amount of the carriage side panel and the height of the carriage side panel as the second volume deformation amount; The first volume deformation amount and the second volume deformation amount are used as the compartment volume deformation value of the truck.

5. The method for determining rock crushing volume in the blasting area according to claim 4, characterized in that: The step of performing deformation compensation processing on the point cloud data sample according to the compartment volume deformation value to obtain target point cloud data of the truck includes: Acquire the height between each feature point in the point cloud data sample and the vehicle compartment floor, and determine the height compensation amount of each feature point according to the ratio of each height to the first volume deformation amount; Acquire the distance between each feature point in the point cloud data sample and the side panel of the carriage, and determine the distance compensation amount of each feature point according to the ratio of each distance to the second volume deformation amount; The height compensation amount is subtracted from the height of each feature point, and the distance compensation amount is subtracted from the distance of each feature point to obtain the target point cloud data.

6. The method for determining rock crushing volume in a blasting area according to claim 1, characterized in that: The determining the gravel volume of the truck based on the target point cloud data of the truck includes: Acquire the empty compartment point cloud data of the truck; Determining gravel point cloud data of the truck based on the empty compartment point cloud data and the target point cloud data; Constructing a three-dimensional model corresponding to the gravel point cloud data of the truck; The volume of the three-dimensional model is calculated, and the volume of the three-dimensional model is used as the gravel volume of the truck.

7. The method for determining rock crushing volume in a blasting area according to claim 1, characterized in that: The step of determining the rock crushing volume of the target blasting area by combining the rock crushing volume of each truck includes: The rock crushing volume of each truck is accumulated to obtain the rock crushing volume of the target blasting area.

8. A system for determining rock crushing volume in a blasting area, characterized in that: The system comprises: A data acquisition module is used to acquire vehicle posture data, compartment deformation data, and compartment point cloud data of multiple trucks in the target explosion area; A data sample determination module is used to calculate the inclination angle of the carriage of each truck according to the vehicle posture data, and perform posture correction processing on the carriage point cloud data according to the carriage inclination angle to obtain a point cloud data sample of the truck; A point cloud data determination module, used to calculate the compartment volume deformation value of the truck according to the compartment deformation data, and perform deformation compensation processing on the point cloud data sample according to the compartment volume deformation value to obtain target point cloud data of the truck; The crushing volume determination module is used to determine the crushed rock volume of the truck based on the target point cloud data of the truck; and determine the rock crushing volume of the target blasting area in combination with the crushed rock volumes of each truck.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method for determining the rock crushing volume in the blasting area as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method for determining the rock crushing volume in the blasting area according to any one of claims 1 to 7 is executed.

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