A method and system for determining a volume of rock broken by a blast zone

Abstract

The application discloses a method and system for determining a rock breaking volume of a blasting area, and relates to the technical field of data processing. The method comprises the following steps: acquiring vehicle posture data, carriage deformation data and carriage point cloud data of a plurality of trucks in a target blasting area; for each truck, calculating a carriage inclination angle of the truck according to the vehicle posture data, and 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; 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 a rock breaking volume of the truck based on the target point cloud data of the truck; and combining the rock breaking volumes of the trucks to determine a rock breaking volume of the target blasting area. The technical scheme provided by the application can improve the accuracy of determining the rock breaking volume of the blasting area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a blast area rock broken volume determination method and system, an electronic device and a storage medium. BACKGROUND

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

[0003] Currently, the existing blast area rock broken volume determination method usually uses fixed identification standards to analyze the truck compartment images, estimates the volume of the rock fragments loaded in the truck compartment, and thus determines the rock broken volume of the blast area. However, in actual applications, the method of using only the existing fixed identification standards to analyze the truck compartment images to determine the volume of the rock fragments often tends to ignore the volume error of the rock fragments caused by the deformation and tilting of the truck compartment during transportation, thereby resulting in low accuracy in determining the rock broken volume of the blast area. SUMMARY

[0004] The present application provides a blast area rock broken volume determination method and system, which has the effect of improving the accuracy of determining the rock broken volume of the blast area.

[0005] In a first aspect, the present application provides a blast area rock broken volume determination method, comprising:

[0006] Obtaining vehicle posture data, compartment deformation data, and compartment point cloud data of a plurality of trucks in a target blast area;

[0007] For each truck, according to the vehicle posture data, calculating the compartment tilt angle of the truck, and according to the compartment tilt angle, performing posture correction processing on the compartment point cloud data to obtain a point cloud data sample of the truck;

[0008] According to the compartment deformation data, calculating the compartment volume deformation value of the truck, and according to the compartment volume deformation value, performing deformation compensation processing on the point cloud data sample to obtain target point cloud data of the truck;

[0009] Based on the target point cloud data of the truck, determining the volume of the rock fragments of the truck;

[0010] Combining the volumes of the rock fragments of each truck, determining the rock broken volume of the target blast area.

[0011] In a second aspect of the present application, a rock breaking volume determination system for a blasting area is provided, and the system comprises:

[0012] A data acquisition module is configured to acquire vehicle attitude data, carriage deformation data and carriage point cloud data of a plurality of trucks in the target blasting area.

[0013] A data sample determination module is configured to, for each truck, calculate a carriage tilt angle of the truck according to the vehicle attitude data, and perform attitude correction processing on the carriage point cloud data according to the carriage tilt angle to obtain a point cloud data sample of the truck.

[0014] A point cloud data determination module is configured to calculate a carriage volume deformation value of the truck according to the carriage deformation data, and perform 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.

[0015] A breaking volume determination module is configured to determine a rock breaking volume of the truck based on the target point cloud data of the truck, and determine a rock breaking volume of the target blasting area in combination with the rock breaking volumes of the trucks.

[0016] In a third aspect of the present application, an electronic device is provided, which comprises a memory, a processor and a program stored in the memory and executable on the processor, and the program can be loaded and executed by the processor to implement a rock breaking volume determination method for a blasting area.

[0017] In a fourth aspect of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to make the processor implement a rock breaking volume determination method for a blasting area.

[0018] In summary, the one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0019] By adopting the technical scheme, the vehicle attitude data, the carriage deformation data and the carriage point cloud data of the plurality of trucks in the target blasting area are acquired, firstly, the influence of the attitude change of the vehicle in the transportation process on the measurement accuracy is solved, the carriage inclination angle is calculated and the attitude correction processing is performed, and the consistency of the spatial reference datum of the point cloud data is ensured. Secondly, for the deformation problem of the carriage in the loading state, the carriage volume deformation value is calculated and the deformation compensation processing is performed, the interference of the carriage deformation on the measurement result is effectively eliminated, and the spatial accuracy of the point cloud data is improved. On this basis, the single truck gravel volume is calculated by using the target point cloud data corrected and compensated, and the total rock breaking volume of the target blasting area is obtained by accumulation and statistics, and the local measurement to the overall evaluation is realized. The data processing method of the system not only considers various influence factors under the actual transportation condition, but also corrects through multi-dimensional data. By comprehensively considering the error influence of the carriage on the gravel volume during transportation, the accuracy of determining the rock breaking volume of the blasting area is improved. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a flow diagram of a blasting area rock breaking volume determination method provided by an embodiment of the present application;

[0021] Figure 2 is a structural diagram of a blasting area rock breaking volume determination system provided by an embodiment of the present application;

[0022] Figure 3 is a structural diagram of an electronic device provided by an embodiment of the present application.

[0023] BRIEF DESCRIPTION OF DRAWINGS: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0024] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in combination with the drawings in the embodiments of the specification. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments.

[0025] In the description of the embodiments of the present application, the words such as "for example" or "for instance" are used to represent an example, illustration or description. Any embodiment or design scheme described as "for example" or "for instance" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words such as "for example" or "for instance" are intended to present the relevant concept in a specific manner.

[0026] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first", "second", "third", etc. are used only for descriptive purposes and should not be construed as indicating or implying relative importance or implying the indicated technical features. Therefore, the features defined as "first", "second", etc. can be explicitly or implicitly included one or more of the features. The terms "include", "contain", "have" and their variants mean "include but not limited to", unless otherwise specifically emphasized.

[0027] The embodiments of the present application provide a method for determining the rock breaking volume of a blasting area. In one embodiment, referring to Figure 1 , Figure 1 is a flowchart of the method for determining the rock breaking volume of a blasting area provided by the embodiments of the present application. The method can be implemented by relying on a computer program, which can be integrated in an application or run as an independent tool application. The method can also be implemented by relying on a single-chip microcomputer or run on a blasting area rock breaking volume determination system based on the von Neumann system. Specifically, the method can include the following steps:

[0028] Step 101: Obtain vehicle attitude data, carriage deformation data and carriage point cloud data of a plurality of trucks in the target blasting area.

[0029] The vehicle attitude data refers to the vertical acceleration values at the four corner points of the truck carriage, which reflect the spatial attitude characteristics of the carriage during transportation. The vehicle attitude data includes the vertical acceleration values corresponding to the front left corner point, the front right corner point, the rear left corner point and the rear right corner point of the carriage, respectively. These data can be collected by the vertical acceleration sensors installed at the four corner points of the carriage in real time, which can reflect the inclination state of the carriage relative to the horizontal plane. The difference between these vertical acceleration values directly reflects the inclination degree of the carriage in the front-rear direction and the left-right direction.

[0030] The carriage deformation data refers to the structural deformation parameters of the truck carriage during the process of carrying broken stones, mainly including two key indicators of the carriage floor deflection value and the carriage side plate deformation variable. The carriage floor deflection value reflects the vertical deformation degree of the carriage floor under the action of the gravity of broken stones, which can be monitored in real time by the strain sensors installed at the key positions of the floor; the carriage side plate deformation variable represents the horizontal deformation degree of the side plate under the action of the lateral pressure of broken stones, which is obtained by the strain sensors arranged on the surface of the side plate.

[0031] The carriage point cloud data refers to a set of spatial data obtained by scanning the gravel loaded in the truck carriage by a three-dimensional laser scanner. These data record the spatial distribution characteristics of the gravel accumulation in the carriage in the form of three-dimensional coordinate points. Specifically, the carriage point cloud data is composed of a large number of discrete spatial coordinate points, each of which contains accurate spatial position information. The collection of these points collectively constitutes a digital expression of the surface morphology of the gravel in the carriage.

[0032] Specifically, to accurately obtain the rock broken volume of the target blast area, it is necessary to first conduct comprehensive data collection on the truck transporting the gravel in the blast area. Vertical acceleration sensors are arranged at the four corner points of the truck carriage to collect vehicle attitude data in real time, which can reflect the inclination state of the carriage during transportation. At the same time, strain sensors are installed at key positions on the carriage floor and side panels to collect carriage deformation data, including floor deflection values and side panel deformation amounts, which can reflect the deformation of the carriage when carrying gravel. In addition, a three-dimensional laser scanner is installed on the top of the truck to scan the gravel in the carriage and obtain carriage point cloud data, which contains three-dimensional spatial information of the gravel accumulation. The collection devices of these three types of data are managed and data transmission is unified through the vehicle control unit to ensure the synchronization and accuracy of the data. When the truck enters the target blast area for gravel loading and transportation, these sensing devices will continue to work and collect relevant data in real time. Through this multi-dimensional data collection method, not only the basic spatial distribution information of the gravel can be obtained, but also the influence of the attitude change of the vehicle during transportation and the deformation of the carriage on the measurement results can be considered, providing a comprehensive data basis for subsequent data processing and volume calculation, thereby improving the accuracy of the rock broken volume measurement of the blast area.

[0033] Step 102: For each truck, calculate the carriage inclination angle of the truck according to the vehicle attitude data, and perform 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.

[0034] The carriage inclination angle refers to the degree of deviation of the truck carriage relative to the standard horizontal plane, including the front-back inclination angle and the left-right inclination angle. The front-back inclination angle reflects the inclination degree of the carriage in the longitudinal direction, and the left-right inclination angle represents the inclination degree of the carriage in the transverse direction.

[0035] The point cloud data sample refers to the carriage point cloud data after attitude correction processing, which reflects the spatial distribution characteristics of the gravel in the carriage under the standard horizontal state. The point cloud data sample is composed of spatial feature points after coordinate transformation, and each feature point contains corrected target longitudinal coordinates and target transverse coordinates. These coordinate values eliminate the influence of the carriage inclination and accurately reflect the actual spatial position of the gravel under the horizontal reference plane.

[0036] Specifically, to eliminate the influence of the tilt of the vehicle compartment on the volume measurement of the gravel during transportation, the collected point cloud data needs to be processed for attitude correction. First, the tilt angle of the vehicle compartment is calculated based on the vertical acceleration values of the four corner points of the vehicle compartment. By calculating the first acceleration mean of the vertical acceleration values of the two corner points on the front side 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 vehicle compartment, the front and rear tilt angle of the vehicle compartment can be determined. According to the ratio of the vertical acceleration difference between the left and right sides to the width of the vehicle compartment, the left and right tilt angle of the vehicle compartment can be determined. These two angles together constitute the tilt angle of the vehicle compartment. After obtaining the tilt angle of the vehicle compartment, the point cloud data of the vehicle compartment needs to be processed for attitude correction. First, a three-dimensional coordinate system of the vehicle compartment is constructed to determine the spatial coordinates of each feature point in the point cloud data in the coordinate system. Then, the longitudinal coordinates of the feature points are corrected according to the front and rear tilt angle to obtain the target longitudinal coordinates. Then, the lateral coordinates of the feature points are corrected according to the left and right tilt angle to obtain the target lateral coordinates. Finally, based on the corrected target longitudinal coordinates and target lateral coordinates, the point cloud data sample after attitude correction can be obtained. Through this correction process, the point cloud data collected in the tilted state can be converted to data in the standard horizontal state, effectively eliminating the influence of the tilt of the vehicle compartment on the volume measurement of the gravel, and laying a accurate data foundation for subsequent deformation compensation and volume calculation.

[0037] On the basis of the above embodiment, as an optional embodiment, in step 102, the tilt angle of the vehicle compartment is calculated according to the vehicle attitude data. This step can also include the following steps:

[0038] Step 201: Calculate the first acceleration mean of the vertical acceleration values of the two corner points on the front side of the vehicle compartment and the second acceleration mean of the vertical acceleration values of the two corner points on the rear side of the vehicle compartment, and determine the vertical acceleration difference between the front and rear sides of the vehicle based on the first acceleration mean and the second acceleration mean.

[0039] The vertical acceleration values of the two corner points on the front side of the vehicle compartment refer to the acceleration data collected by the vertical acceleration sensors installed at the front left corner point and the front right corner point of the vehicle compartment. These data reflect the acceleration changes of the front part of the vehicle compartment in the vertical direction, including the vertical acceleration values measured at the front left corner point and the front right corner point.

[0040] 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 vehicle compartment, which represents the overall vertical acceleration state of the front part of the vehicle compartment.

[0041] The vertical acceleration values of the two corner points at the rear side of the carriage refer to the acceleration data collected by the vertical acceleration sensors installed at the rear left corner point and the rear right corner point of the carriage. These data reflect the acceleration changes of the rear part of the carriage in the vertical direction, including the vertical acceleration value measured at the rear left corner point and the vertical acceleration value measured at the rear right corner point.

[0042] The second acceleration average refers to the arithmetic average of the vertical acceleration values of the rear left corner point and the rear right corner point of the carriage, which represents the overall vertical acceleration state of the rear part of the carriage.

[0043] The front-rear side vertical acceleration difference refers to the difference between the first acceleration average of the two corner points at the front side of the carriage and the second acceleration average of the two corner points at the rear side, which reflects the degree of inclination of the carriage in the front-rear direction.

[0044] Specifically, to accurately calculate the front-rear inclination of the carriage, it is necessary to first determine the front-rear side vertical acceleration difference. Through the vertical acceleration sensors installed at the four corner points of the carriage, the vertical acceleration values of the front left corner point and the front right corner point, as well as the vertical acceleration values of the rear left corner point and the rear right corner point, can be collected respectively. To eliminate the influence of measurement errors of individual sensors, the mean value calculation method is adopted: the vertical acceleration values of the front left corner point and the front right corner point of the carriage are arithmetically averaged to obtain the first acceleration average, which reflects the overall vertical acceleration state of the front side of the carriage; similarly, the vertical acceleration values of the rear left corner point and the rear right corner point of the carriage are arithmetically averaged to obtain the second acceleration average, which reflects the overall vertical acceleration state of the rear side of the carriage. Then, by calculating the difference between the first acceleration average and the second acceleration average, the front-rear side vertical acceleration difference can be obtained. The sign and size of this difference directly reflect the direction and degree of the front-rear inclination of the carriage: when the difference is positive, it indicates that the front side of the carriage is higher than the rear side, presenting a front-high rear-low state; when the difference is negative, it indicates that the front side of the carriage is lower than the rear side, presenting a front-low rear-high state; the larger the absolute value of the difference, the greater the degree of front-rear inclination. Through this mean value calculation-based method, not only the reliability of the measurement is improved, but also accurate data basis is provided for the subsequent calculation of the front-rear inclination angle.

[0045] Step 202: Calculate the third acceleration average of the vertical acceleration values of the two corner points at the left side of the carriage and the fourth acceleration average of the vertical acceleration values of the two corner points at the right side of the carriage, and determine the left-right side vertical acceleration difference of the truck based on the third acceleration average and the fourth acceleration average.

[0046] The vertical acceleration values of the two corner points on the left side of the carriage refer to the acceleration data collected by the vertical acceleration sensors installed at the front left corner point and the rear left corner point. These data reflect the acceleration changes in the vertical direction of the left side of the carriage, including the vertical acceleration value measured at the front left corner point and the vertical acceleration value measured at the rear left corner point.

[0047] The third acceleration average refers to the arithmetic average of the vertical acceleration values of the front left corner point and the rear left corner point of the carriage, which represents the overall vertical acceleration state of the left side of the carriage.

[0048] The vertical acceleration values of the two corner points on the right side of the carriage refer to the acceleration data collected by the vertical acceleration sensors installed at the front right corner point and the rear right corner point. These data reflect the acceleration changes in the vertical direction of the right side of the carriage, including the vertical acceleration value measured at the front right corner point and the vertical acceleration value measured at the rear right corner point.

[0049] The fourth acceleration average refers to the arithmetic average of the vertical acceleration values of the front right corner point and the rear right corner point of the carriage, which represents the overall vertical acceleration state of the right side of the carriage.

[0050] The left-right side vertical acceleration difference refers to the difference between the third acceleration average of the two corner points on the left side of the carriage and the fourth acceleration average of the two corner points on the right side, which reflects the degree of inclination of the carriage in the left-right direction.

[0051] Specifically, to accurately calculate the left-right inclination of the carriage, it is necessary to first determine the left-right side vertical acceleration difference. Through the vertical acceleration sensors installed at the four corner points of the carriage, the vertical acceleration values of the front left corner point and the rear left corner point, as well as the vertical acceleration values of the front right corner point and the rear right corner point, can be collected. To eliminate the influence of measurement errors of individual sensors, the mean value calculation method is adopted: the vertical acceleration values of the front left corner point and the rear left corner point of the carriage are arithmetically averaged to obtain the third acceleration average, which reflects the overall vertical acceleration state of the left side of the carriage; similarly, the vertical acceleration values of the front right corner point and the rear right corner point of the carriage are arithmetically averaged to obtain the fourth acceleration average, which reflects the overall vertical acceleration state of the right side of the carriage. Then, by calculating the difference between the third acceleration average and the fourth acceleration average, the left-right side vertical acceleration difference is obtained. The sign and size of this difference directly reflect the direction and degree of left-right inclination of the carriage: when the difference is positive, it indicates that the left side of the carriage is higher than the right side, showing a left-high right-low state; when the difference is negative, it indicates that the left side of the carriage is lower than the right side, showing a left-low right-high state; the larger the absolute value of the difference, the greater the degree of left-right inclination. Through this mean value calculation-based method, not only the reliability of the measurement is improved, but also accurate data basis is provided for subsequent calculation of the left-right inclination angle

[0052] Step 203: determining the front-rear inclination angle of the carriage according to the ratio between the front-rear vertical acceleration difference and the length of the carriage.

[0053] The front-rear inclination angle refers to the included angle between the longitudinal center line of the carriage and the horizontal plane, which quantitatively represents the degree of deviation of the carriage from the horizontal state in the front-rear direction. The front-rear inclination angle is calculated by the ratio of the front-rear vertical acceleration difference and the length of the carriage using the inverse tangent function, and the sign and size of the value directly reflect the direction and degree of the front-rear inclination of the carriage.

[0054] Specifically, to accurately determine the inclination angle of the carriage in the front-rear direction, the obtained front-rear vertical acceleration difference needs to be calculated in combination with the actual length of the carriage. Since the front-rear vertical acceleration difference reflects the relative height difference between the front and rear ends of the carriage, and the length of the carriage is a fixed known parameter, the front-rear inclination angle of the carriage can be calculated by the ratio relationship between the two values using the inverse trigonometric function principle. Specifically, the front-rear vertical acceleration difference is divided by the length of the carriage to obtain a ratio, which is actually the tangent value of the front-rear inclination angle. Then the front-rear inclination angle of the carriage is calculated by the inverse tangent function. When the front-rear vertical acceleration difference is positive, the calculated angle is positive, indicating that the carriage is in a front-high rear-low inclination state; when the front-rear vertical acceleration difference is negative, the calculated angle is negative, indicating that the carriage is in a front-low rear-high inclination state; the greater the absolute value of the angle, the greater the degree of front-rear inclination of the carriage. This calculation method based on geometric relationship is not only simple, but also can accurately reflect the actual inclination state of the carriage, providing accurate angle parameters for subsequent point cloud data posture correction, effectively improving the accuracy of the carriage volume measurement.

[0055] Step 204: determining the left-right inclination angle of the carriage according to the ratio between the left-right vertical acceleration difference and the width of the carriage; taking the front-rear inclination angle and the left-right inclination angle as the inclination angle of the carriage of the truck.

[0056] The left-right inclination angle refers to the included angle between the transverse center line of the carriage and the horizontal plane, which quantitatively represents the degree of deviation of the carriage from the horizontal state in the left-right direction. The left-right inclination angle is calculated by the ratio of the left-right vertical acceleration difference and the width of the carriage using the inverse tangent function, and the sign and size of the value directly reflect the direction and degree of the left-right inclination of the carriage.

[0057] Specifically, to accurately determine the inclination angle of the carriage in the left-right direction, the obtained left-right vertical acceleration difference needs to be utilized in combination with the actual width of the carriage for calculation. Since the left-right vertical acceleration difference reflects the relative height difference between the left and right sides of the carriage, and the carriage width is a fixed known parameter, the inclination angle of the carriage in the left-right direction can be calculated by the ratio relationship between the two values using the inverse trigonometric function principle. Specifically, the left-right vertical acceleration difference is divided by the carriage width to obtain a ratio, which is actually the tangent value of the left-right inclination angle, and then the inverse tangent function is used to calculate the left-right inclination angle of the carriage. When the left-right vertical acceleration difference is positive, the calculated angle is positive, indicating that the carriage is in a left-high right-low inclined state; when the left-right vertical acceleration difference is negative, the calculated angle is negative, indicating that the carriage is in a left-low right-high inclined state; the greater the absolute value of the angle, the greater the left-right inclination of the carriage. The calculated front-back inclination angle and left-right inclination angle are used together as the inclination angle of the carriage, and these two angle parameters completely describe the spatial posture of the carriage, providing accurate rotation parameters for subsequent point cloud data correction. Through this calculation method based on geometric relationship, not only the accurate inclination angles of the carriage in two directions can be obtained, but also a reliable basis is provided for accurate posture correction of point cloud data, thereby improving the accuracy of carriage volume measurement.

[0058] On the basis of the above-mentioned embodiments, as an optional embodiment, in step 102: the carriage point cloud data is subjected to posture correction processing according to the carriage inclination angle to obtain the point cloud data sample of the truck. This step can further include the following steps:

[0059] Step 205: constructing a carriage three-dimensional coordinate system and determining the corresponding spatial coordinates of each feature point in the carriage point cloud data in the carriage three-dimensional coordinate system.

[0060] The carriage three-dimensional coordinate system refers to a right-handed coordinate system established with the left front corner point of the carriage floor as the origin, the length direction of the carriage 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. Specifically, this coordinate system selects a fixed feature point on the carriage entity as a reference to describe the spatial position of any point in the carriage by establishing a standard spatial rectangular coordinate system.

[0061] The feature point refers to a set of spatial points in the carriage point cloud data that have obvious geometric features and are important for representing the structure of the carriage.

[0062] Specifically, to accurately describe the structure of the carriage space and calculate the volume of the carriage, a standardized three-dimensional coordinate system of the carriage needs to be established. The coordinate system selects the left front corner point of the carriage floor as the origin, takes the length direction of the carriage 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 the coordinate system not only conforms to the geometric characteristics of the carriage, but also facilitates subsequent data processing and calculation. After determining the coordinate system, each feature point in the carriage point cloud data needs to be mapped to the coordinate system to obtain its corresponding spatial coordinates. Specifically, the original point cloud data collected by the laser radar is converted to the carriage three-dimensional coordinate system through coordinate transformation, which needs to consider the relative relationship between the laser radar installation position and the carriage, and use rotation matrix and translation vector for coordinate system conversion. Then, the converted point cloud data is subjected to feature point extraction, including the edge corner points, edge lines and other key geometric features of the carriage, and the accurate spatial coordinates of these feature points in the carriage three-dimensional coordinate system are determined. Through this standardized coordinate system establishment and feature point mapping, not only the spatial structure of the carriage is uniformly and normatively expressed, but also a reliable spatial position reference is provided for subsequent volume calculation.

[0063] Step 206: For each feature point, according to the front and rear inclination angles in the carriage inclination angle, correct the longitudinal coordinate in the spatial coordinate to obtain the target longitudinal coordinate of the feature point.

[0064] Wherein, the target longitudinal coordinate refers to the coordinate value of the feature point in the X-axis direction of the carriage three-dimensional coordinate system after the front and rear inclination angle correction, which reflects the true longitudinal position of the feature point when the carriage is in a horizontal state. The target longitudinal coordinate is obtained by rotating the spatial coordinates of the original feature point, and its value represents the accurate spatial position of the feature point along the length direction of the carriage after eliminating the influence of the front and rear inclination.

[0065] Specifically, to eliminate the influence of the front and rear tilting of the carriage on the volume measurement, longitudinal correction of the spatial coordinates of the feature points is required. Due to the front and rear tilting angles of the carriage, the longitudinal coordinate values of the feature points in the point cloud data deviate from the actual coordinate values in the horizontal state, which affects the accuracy of subsequent volume calculation. The specific correction process is to correct the longitudinal coordinates of each feature point based on the front and rear tilting angles using coordinate rotation transformation. First, taking the left front corner point of the carriage floor as the rotation center, a rotation matrix is established according to the front and rear tilting angles, which describes the spatial transformation relationship required to convert the tilted state coordinates to 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 points in the horizontal state are obtained through matrix operations. When the front and rear tilting angles are positive, the longitudinal coordinates of the front feature points will be adjusted downward, and the longitudinal coordinates of the rear feature points will be adjusted upward; when the front and rear tilting angles are negative, the adjustment will be 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 tilting on the spatial position measurement, laying a foundation for subsequent transverse coordinate correction and accurate volume calculation, and improving the accuracy of the entire measurement system.

[0066] Step 207: According to the left and right tilting angles in the carriage tilting angles, correct the transverse coordinates in the spatial coordinates 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, determine the point cloud data samples of the truck.

[0067] Among them, the target transverse coordinate refers to the coordinate value of the feature point in the Y-axis direction of the carriage three-dimensional coordinate system after the left and right tilting angle correction, which reflects the true transverse position of the feature point when the carriage is in the horizontal state. The target transverse coordinate is obtained by rotating the original feature point space coordinates, and its value represents the accurate spatial position of the feature point along the width direction of the carriage after eliminating the influence of the left and right tilting.

[0068] Specifically, to further eliminate the influence of the left and right tilting of the carriage on the volume measurement, the transverse coordinates of the feature points need to be corrected on the basis of completing the longitudinal coordinate correction. Due to the left and right tilting angles of the carriage, the transverse coordinate values of the feature points in the point cloud data are deviated from the actual coordinate values in the horizontal state, and such deviation also affects the measurement accuracy. The specific correction process is based on the left and right tilting angles and uses a coordinate rotation transformation method similar to the longitudinal correction. First, taking the left front corner point of the carriage floor as the rotation center, a rotation matrix is established according to the left and right tilting angles, which describes the spatial transformation relationship required to convert the tilted state coordinates into the horizontal state coordinates. Then, the rotation transformation is applied to the spatial coordinates of each feature point, and the target transverse coordinates of the feature points in the horizontal state are obtained through matrix operation. When the left and right tilting angles are positive, the transverse coordinates of the left feature points will be adjusted downward, and the transverse coordinates of the right feature points will be adjusted upward; when the left and right tilting angles are negative, the adjustment is in the opposite direction. After completing the transverse coordinate correction, combined with the obtained target longitudinal coordinates, the accurate spatial positions of the feature points in the horizontal state can be determined, and then the point cloud data samples after complete attitude correction are obtained. Through such two-way coordinate correction, not only the influence of the carriage tilting on the spatial position measurement is completely eliminated, but also the point cloud data samples can truly reflect the geometric features of the carriage, providing a high-precision spatial data basis for subsequent volume calculation.

[0069] Step 103: According to the carriage deformation data, calculate the carriage volume deformation value of the truck, and perform deformation compensation processing on the point cloud data sample according to the carriage volume deformation value to obtain the target point cloud data of the truck.

[0070] The carriage volume deformation value refers to the volume change caused by the deformation of each part of the carriage due to the loading weight of the goods, which quantitatively represents the deviation of the actual volume of the carriage from the ideal state. The carriage volume deformation value is obtained by comprehensively calculating the deformation data of the carriage floor sinking deformation, side plate expansion deformation and other parts, and its numerical value directly reflects the overall deformation degree of the carriage in the loaded state.

[0071] The target point cloud data refers to the three-dimensional point cloud data of the carriage after the inclination angle correction and deformation compensation processing, which reflects the true spatial geometric features of the carriage in the ideal horizontal and non-deformation state. The target point cloud data is obtained by double processing of attitude correction and deformation compensation on the original point cloud data sample, wherein the attitude correction is based on the front and rear inclination angles and the left and right inclination angles for coordinate transformation, and the deformation compensation is based on the carriage volume deformation value for correcting the spatial position of the points.

[0072] Specifically, to accurately measure the volume of the truck's carriage, the deformation of the carriage due to the load during the actual loading process needs to be considered. The carriage deformation data collected by the strain sensors installed around the carriage can reflect the deformation degree of each position of the carriage. First, the carriage volume deformation value is calculated based on the carriage deformation data. This calculation process needs to consider the deformation of the carriage floor and side plates, as well as the influence of these deformations on the overall volume of the carriage. Specifically, the deformation volume in each direction is obtained by multiplying the sinking deformation of the carriage floor and the expansion deformation of the side plates by the corresponding carriage dimensions (length, width, height), and the deformation volumes are superimposed to obtain the carriage volume deformation value. Then, the point cloud data samples are compensated for deformation according to the calculated carriage volume deformation value. The core of the compensation processing is to correct the spatial coordinates of each point in the point cloud data. During the correction process, the correspondence between the position of the point and the deformation position needs to be considered. The coordinates of the points close to the deformation part are adjusted more, while the coordinates of the points far from the deformation part are adjusted less, so as to realize the gradual transition of the deformation. Through this compensation processing based on the measured deformation data, the influence of the carriage deformation on the volume measurement can be effectively eliminated, and the target point cloud data closer to the real state of the carriage is obtained, which significantly improves the accuracy of the carriage volume measurement. At the same time, this compensation method also provides a more reliable data basis for subsequent volume calculation, which helps to realize accurate loading measurement.

[0073] On the basis of the above-mentioned embodiments, as an optional embodiment, in step 103: calculating the carriage volume deformation value of the truck according to the carriage deformation data, this step can further include the following steps:

[0074] Step 301: obtaining the carriage floor area and the carriage side plate height of the truck.

[0075] The carriage floor area refers to the projection 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 carriage floor area is calculated by processing the spatial coordinates of the four corner points (left front corner point, right front corner point, left rear corner point, and right rear corner point) of the target point cloud data, and its value reflects the effective bottom load-bearing area of the carriage when loading goods.

[0076] The carriage side plate height refers to the vertical distance from the upper edge of the carriage side plate to the floor plane, which represents the space size of the carriage in the vertical direction. The carriage side plate height is calculated by the vertical projection distance between the upper edge line of the side plate and the floor plane in the target point cloud data, and its value reflects the effective space height of the carriage when loading goods.

[0077] Specifically, to accurately calculate the volume of the vehicle compartment, it is necessary to first obtain the basic geometric size parameters of the vehicle compartment, i.e., the floor area of the vehicle compartment and the height of the side plate of the vehicle compartment. These parameters are the basic data for calculating the volume of the vehicle compartment and directly affect the accuracy of the measurement results. Through the processing and analysis of the target point cloud data, the spatial coordinates of the four corner points of the floor of the vehicle compartment are first extracted, and the length and width of the floor are calculated according to these corner point coordinates, and the floor area of the vehicle compartment is obtained by multiplying the length and the width. Then, the upper and lower edge lines of the side plate of the vehicle compartment are identified from the target point cloud data, and the height of the side plate of the vehicle compartment is obtained by calculating the vertical distance between the upper edge line of the side plate and the floor plane. In this process, since the pose correction and deformation compensation of the point cloud data have been completed, the floor area and the height of the side plate obtained can accurately reflect the true size of the vehicle compartment. This size parameter acquisition method based on target point cloud data takes into account the influence of the actual state of the vehicle compartment and provides reliable basic data for subsequent volume calculation.

[0078] Step 302: calculating the product of the floor deflection value and the floor area of the vehicle compartment as a first volume deformation variable; calculating the product of the side plate deformation variable and the height of the side plate of the vehicle compartment as a second volume deformation variable; taking the first volume deformation variable and the second volume deformation variable as the volume deformation value of the vehicle compartment.

[0079] The first volume deformation variable refers to the volume change caused by the sinking deformation of the floor of the vehicle compartment in the loaded state, which is determined by the product of the floor deflection value and the floor area of the vehicle compartment. The first volume deformation variable reflects the loss of space volume caused by the sinking deformation of the floor under the weight of the goods, and its value is obtained by multiplying the actual sinking degree of the floor and the affected floor area, which reflects the influence degree of the floor deformation on the overall volume of the vehicle compartment.

[0080] The second volume deformation variable refers to the volume change caused by the outward deformation of the side plate of the vehicle compartment in the loaded state, which is determined by the product of the side plate deformation variable and the height of the side plate. The second volume deformation variable reflects the increase in space volume caused by the lateral deformation of the side plate under the pressure of the goods, and its value is obtained by multiplying the actual outward degree of the side plate and the affected height of the side plate, which reflects the influence degree of the side plate deformation on the overall volume of the vehicle compartment.

[0081] Specifically, to accurately evaluate the volume deformation of the carriage in the loaded state, the deformation of the floor and the side plate to the volume of the carriage needs to be considered comprehensively. Since the carriage will deform when loaded with goods, the deformation mainly manifests as the deflection of the floor and the outward convex deformation of the side plate, which will cause differences between the actual volume and the volume in the ideal state. The specific calculation process is as follows: first, a first volume deformation variable is determined, which is obtained by multiplying the deflection value of the carriage floor by the area of the carriage floor, to obtain the volume change caused by the sinking of the floor. This calculation considers the influence of the overall sinking of the floor on the volume, reflecting the space loss caused by the deformation of the floor. Then, a second volume deformation variable is calculated, which is obtained by multiplying the deformation variable of the carriage side plate by the height of the carriage side plate, to obtain the volume change caused by the outward convex deformation of the side plate. This calculation reflects the influence degree of the deformation of the side plate on the transverse space of the carriage. Finally, the first volume deformation variable and the second volume deformation variable are taken as the carriage volume deformation value, which comprehensively reflects the overall volume change of the carriage in the actual loaded state. Through this step-by-step deformation calculation method, not only can the influence of deformation in different parts on the volume be accurately quantified, but also a data basis is provided for subsequent volume compensation, improving the accuracy of volume measurement.

[0082] On the basis of the above-mentioned embodiments, as an optional embodiment, in step 103: the point cloud data sample is subjected to deformation compensation processing according to the carriage volume deformation value to obtain target point cloud data of the truck. This step can further include the following steps:

[0083] Step 303: Obtain the height between each feature point in the point cloud data sample and the carriage floor, and determine the height compensation amount of each feature point according to the ratio of each height to the first volume deformation variable.

[0084] The height compensation amount refers to the adjustment value of the vertical position of the feature point to eliminate the influence of the deformation of the carriage floor, which is determined by the ratio of the height between the feature point and the carriage floor to the first volume deformation variable. The height compensation amount 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 carriage, and its value decreases with the increase of the height of the feature point from the floor, reflecting the differential influence of the deformation of the floor on feature points at different heights.

[0085] Specifically, in order to accurately compensate the influence of the deformation of the carriage on the spatial position of the feature points, differentiated height compensation needs to be performed 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 variable. The specific calculation process is as follows: first, the vertical height of each feature point relative to the carriage floor is extracted 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 divided by the first volume deformation variable that has been calculated, 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 amount required by each feature point can be determined. The higher 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, the true spatial position of each feature point in the non-deformation state can be accurately restored, and the continuity and rationality of the compensation are ensured, providing more accurate spatial position data for subsequent volume calculation.

[0086] Step 304: Obtain the distance between each feature point in the point cloud data sample and the carriage side plate, and determine the distance compensation amount of each feature point according to the ratio of each distance to the second volume deformation variable.

[0087] In the embodiments of the present application, the distance compensation amount refers to the adjustment value for the lateral position of the feature points to eliminate the influence of the deformation of the carriage side plate, which is determined by the ratio of the distance between the feature points and the carriage side plate to the second volume deformation variable. The distance compensation amount reflects how much numerical correction needs to be made to the lateral coordinates of the feature points to restore the true position of the feature points in the non-deformation state of the carriage, and its value decreases with the increase of the distance of the feature points from the side plate, reflecting the differentiated influence of the side plate deformation on feature points at different lateral positions.

[0088] Specifically, in order to accurately compensate the influence of the deformation of the side plate on the spatial position of the feature points, different distance compensations are needed for feature points at different lateral positions. Since the convex deformation of the side plate will cause the actual lateral position of the feature points to deviate from the ideal state, and this deviation changes with the distance of the feature points from the side plate, it is necessary to establish a corresponding relationship between the lateral distance of the feature points and the deformation variable. The specific calculation process is first to extract the lateral distance of each feature point relative to the side plate from the point cloud data sample, which reflects the relative position of the feature points in the lateral space. Then, the distance of each feature point is divided by the second volume deformation variable calculated, which reflects the degree of influence of the side plate deformation on feature points at different lateral positions. Based on this ratio relationship, the specific distance compensation amount required by each feature point can be determined. The farther the feature point is from the side plate, the less it is affected by the side plate deformation, and the smaller the corresponding compensation amount. Through this compensation calculation method based on distance difference, the true lateral position of each feature point in the non-deformation state can be accurately restored, and the continuity and rationality of the compensation are ensured, providing more accurate spatial position data for subsequent volume calculation. This compensation method considering distance difference effectively improves the accuracy of feature point position correction.

[0089] 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 the target point cloud data.

[0090] Specifically, in order to obtain target point cloud data that accurately reflects the spatial distribution of goods in the non-deformation 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 both floor sinking and side plate convex deformation in the loaded state, these deformations cause the collected feature points to deviate from their actual positions, so it is necessary to restore the true spatial position of the feature points through compensation calculation. The specific calculation process is first to correct the height value of each feature point by subtracting the corresponding height compensation amount from the original height, which eliminates the influence of floor sinking deformation on the vertical position of the feature points; then correct the lateral distance of each feature point by subtracting the corresponding distance compensation amount from the original distance, which eliminates the influence of side plate convex deformation on the lateral position of the feature points. Through these two steps of position correction, the spatial coordinates of the feature points are adjusted to the position in the non-deformation state, forming target point cloud data that accurately reflects the actual spatial distribution of goods.

[0091] Step 104: Determine the gravel volume of the truck based on the target point cloud data of the truck; and determine the rock breaking volume of the target blast area in combination with the gravel volume of each truck.

[0092] The rock volume is the total volume of all rock fragments after blasting in the target blasting area, which is obtained by cumulatively counting the rock volumes of all transport trucks. The rock volume reflects the actual spatial volume of the solid rock after being broken by blasting operation.

[0093] The rock volume is the total volume of all rock fragments after blasting in the target blasting area, which is obtained by cumulatively counting the rock volumes of all transport trucks. The rock volume reflects the actual spatial volume of the solid rock after being broken by blasting operation.

[0094] Specifically, to accurately evaluate the effect of blasting operation and calculate the rock volume, the volume of each truck transporting rock needs to be measured and cumulatively counted. Since the rock produced by the target blasting area is transported in batches, the rock volume measurement of a single truck directly affects the calculation accuracy of the total rock volume of the blasting area, so accurate volume calculation is needed based on the obtained 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 profile and accumulation form of the rock in each truck compartment. By analyzing the three-dimensional spatial distribution of the rock surface feature points, combining the parameters such as the area of the truck bed and the height of the side plate, and using numerical integration method, the rock volume of a single truck is calculated. Then, the rock volumes of all transport vehicles are cumulatively counted to obtain the total rock volume of the target blasting area. This volume calculation method based on point cloud data not only considers the actual accumulation state of the rock, but also improves the calculation accuracy by compensating for the deformation effect, making the final rock volume more accurate and reliable.

[0095] Based on the above embodiment, as an optional embodiment, in step 104, the rock volume of the truck is determined based on the target point cloud data of the truck. This step can further include the following steps:

[0096] Step 401: Obtain the unloaded point cloud data of the truck compartment; based on the unloaded point cloud data of the truck compartment and the target point cloud data, determine the rock point cloud data of the truck.

[0097] The unloaded point cloud data of the truck compartment refers to the set of feature points of the truck compartment structure space obtained by three-dimensional scanning in the unloaded state, which reflects the original spatial form of the truck bed, side plate and their connecting structure. The unloaded point cloud data of the truck compartment contains three-dimensional coordinate information and structural features of each part of the truck compartment, and the data is collected in the standard state without load and deformation, which reflects the reference spatial features of the truck body.

[0098] The gravel point cloud data refers to a three-dimensional point set reflecting only the spatial characteristics of the gravel accumulation, which is extracted by comparing the target point cloud data with the empty carriage point cloud data. The gravel point cloud data excludes the spatial information of the carriage structure and specially describes the surface profile and spatial distribution of the gravel. The gravel point cloud data reflects the actual morphological characteristics of the gravel accumulation in the carriage, and the data is a pure point cloud set obtained after eliminating the influence of the carriage structure, which embodies the real distribution state of the gravel in the three-dimensional space.

[0099] Specifically, in order to accurately obtain the spatial distribution characteristics of the gravel, it is necessary to eliminate the influence of the carriage structure on the point cloud data and realize the accurate extraction of the gravel point cloud data. Since the collected target point cloud data contains both the spatial information of the carriage structure and the gravel, these mixed spatial information will affect the calculation accuracy of the gravel volume, so it is necessary to separate the data by using the reference data in the empty state. The specific processing process is as follows: first, obtain the carriage point cloud data in the empty state, which reflects the structural characteristics and spatial profile of the carriage itself; then, compare and process the carriage empty point cloud data with the obtained target point cloud data in space, identify and match the feature points in the same spatial position, exclude the point cloud data corresponding to the carriage structure, and extract the feature point set reflecting only the spatial distribution of the gravel, so as to obtain the accurate gravel point cloud data. Through this data processing method based on the empty reference, not only the spatial information of the gravel can be accurately separated, but also the interference of the structural 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 empty state effectively improves the extraction accuracy of the gravel point cloud data, and further ensures the accuracy of the volume measurement result.

[0100] Step 402: constructing a three-dimensional model corresponding to the gravel point cloud data of the carriage.

[0101] The three-dimensional model refers to a closed space entity model constructed based on the gravel point cloud data, which converts the discrete feature points into continuous geometric bodies through spatial interpolation and surface reconstruction, and completely describes the spatial morphology of the gravel accumulation. The three-dimensional model reflects the overall spatial profile and volume characteristics of the gravel, and its structure is a continuous geometric body established by considering the surface undulation and spatial distribution characteristics of the gravel, which embodies the real accumulation state of the gravel in the carriage.

[0102] Specifically, to achieve accurate calculation of the volume of gravel, it is necessary to convert discrete gravel point cloud data into a continuous three-dimensional space model, thereby accurately describing the spatial distribution and volume characteristics of the gravel. Since the gravel point cloud data only contains a limited number of spatial sampling points, these discrete point sets cannot be directly used for volume calculation, so a three-dimensional modeling method is needed to construct a complete spatial model. 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, a closed three-dimensional entity model is constructed in combination with the truck bed as the reference surface, which accurately reflects the overall shape and spatial profile of the gravel accumulation. Through this three-dimensional modeling method based on point cloud data, not only is the conversion from discrete point sets to continuous models achieved, but also the consistency of the model with the actual spatial distribution of the gravel is ensured, providing a reliable geometric basis for subsequent volume calculation. This modeling method considering spatial continuity effectively improves the expression accuracy of the spatial characteristics of gravel.

[0103] Step 403: Calculate the volume of the three-dimensional model, and take the volume of the three-dimensional model as the gravel volume of the truck.

[0104] Specifically, to obtain accurate gravel volume values, numerical integration calculation needs to be performed on the constructed three-dimensional model, thereby realizing the conversion from spatial geometric features to volume values. Since the three-dimensional model completely describes the spatial distribution characteristics of the gravel, this closed geometric body has a strict correspondence with the actual gravel volume, so the actual volume of the gravel can be determined by calculating the volume of the three-dimensional model. The specific calculation process is first to divide the three-dimensional model into grids in space, and to discretize the entire model by establishing small volume units; then perform numerical integration on all volume units, and accumulate the volume of each small unit to obtain the volume value of the entire three-dimensional model, which is the gravel volume loaded by the truck. Through this numerical integration-based volume calculation method, not only is the accurate measurement of irregular spatial forms achieved, but also the reliability of the calculation results is ensured, providing accurate data for the statistics of gravel transportation volume.

[0105] On the basis of the above embodiment, as an optional embodiment, in step 104: in combination with the gravel volume of each truck, the rock breaking volume of the target blasting area is determined, and this step can further include the following steps:

[0106] Step 404: Accumulate the gravel volume of each truck to obtain the rock breaking volume of the target blasting area.

[0107] Specifically, to obtain the total rock breaking volume of the target blasting area, the rock breaking volume of all transport vehicles needs to be systematically accumulated and counted, so as to realize the data conversion from single vehicle measurement to overall evaluation. Since the broken rock after blasting needs to be transported in batches, the rock breaking volume measured by a single vehicle is a component of the total rock breaking volume of the target blasting area, and therefore the complete breaking volume needs to be obtained through data accumulation. The specific counting process is to sequentially record the rock breaking volume measured by each completed transport vehicle, to sum up the rock breaking volumes of all transport vehicles through continuous accumulation operation, and finally to obtain the total volume value reflecting the rock breaking effect of the entire target blasting area. Through this calculation method based on accumulation counting, not only the data conversion from local to overall is realized, but also the completeness of the breaking volume counting is guaranteed, which provides comprehensive data support for the blasting operation effect evaluation. This statistical method considering the whole transport process effectively solves the total volume counting problem under the condition of batch transportation, and further improves the accuracy of blasting engineering quantity accounting.

[0108] Reference Figure 2 A rock breaking volume determination system for a blasting area is provided, which comprises a data acquisition module, a data sample determination module, a point cloud data determination module, and a breaking volume determination module, wherein:

[0109] The data acquisition module is configured to acquire vehicle posture data, carriage deformation data, and carriage point cloud data of a plurality of trucks in the target blasting area.

[0110] The data sample determination module is configured to calculate the carriage inclination angle of each truck according to the vehicle posture data, and to perform 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.

[0111] The point cloud data determination module is configured to calculate the carriage volume deformation value of the truck according to the carriage deformation data, and to perform deformation compensation processing on the point cloud data sample according to the carriage volume deformation value to obtain the target point cloud data of the truck.

[0112] The breaking volume determination module is configured to determine the rock breaking volume of the target blasting area based on the target point cloud data of the truck.

[0113] On the basis of the above-mentioned embodiments, the data sample determination module is further configured to calculate a first acceleration average of the vertical acceleration values of the two corner points on the front side of the carriage and a second acceleration average of the vertical acceleration values of the two corner points on the rear side of the carriage, and determine a front-rear side vertical acceleration difference value of the truck based on the first acceleration average and the second acceleration average; calculate a third acceleration average of the vertical acceleration values of the two corner points on the left side of the carriage and a fourth acceleration average of the vertical acceleration values of the two corner points on the right side of the carriage, and determine a left-right side vertical acceleration difference value of the truck based on the third acceleration average and the fourth acceleration average; determine a front-rear tilting angle of the carriage according to a ratio between the front-rear side vertical acceleration difference value and the length of the carriage; determine a left-right tilting angle of the carriage according to a ratio between the left-right side vertical acceleration difference value and the width of the carriage; and take the front-rear tilting angle and the left-right tilting angle as the carriage tilting angle of the truck.

[0114] On the basis of the above-mentioned embodiments, the data sample determination module is further configured to construct a three-dimensional coordinate system of the carriage of the truck, and determine the corresponding spatial coordinates of each feature point in the carriage point cloud data in the three-dimensional coordinate system of the carriage; for each feature point, correct the longitudinal coordinate in the spatial coordinates according to the front-rear tilting angle in the carriage tilting angle to obtain the target longitudinal coordinate of the feature point; correct the transverse coordinate in the spatial coordinates according to the left-right tilting angle in the carriage tilting angle to obtain the target transverse coordinate of the feature point; and determine the point cloud data sample of the truck according to the target longitudinal coordinate and the target transverse coordinate of each feature point.

[0115] On the basis of the above-mentioned embodiments, the point cloud data determination module is further configured to obtain the area of the carriage floor and the height of the carriage side plate of the truck; calculate the product between the carriage floor deflection value and the area of the carriage floor as a first volume deformation variable; calculate the product between the carriage side plate deformation variable and the height of the carriage side plate as a second volume deformation variable; and take the first volume deformation variable and the second volume deformation variable as the carriage volume deformation value of the truck.

[0116] On the basis of the above-mentioned embodiments, the point cloud data determination module is further configured to obtain the height between each feature point in the point cloud data sample and the carriage floor, determine the height compensation amount of each feature point according to the ratio between each height and the first volume deformation variable; obtain the distance between each feature point in the point cloud data sample and the carriage side plate, determine the distance compensation amount of each feature point according to the ratio between each distance and the second volume deformation variable; and subtract the height compensation amount from the height of each feature point correspondingly, and subtract the distance compensation amount from the distance of each feature point correspondingly to obtain the target point cloud data.

[0117] On the basis of the above-mentioned embodiments, the broken volume determination module is further configured to acquire empty point cloud data of a vehicle compartment of the truck; determine broken stone point cloud data of the truck based on the empty point cloud data of the vehicle compartment of the truck and the target point cloud data; construct a three-dimensional model corresponding to the broken stone point cloud data of the truck; calculate the volume of the three-dimensional model, and take the volume of the three-dimensional model as the broken stone volume of the truck.

[0118] On the basis of the above-mentioned embodiments, the broken volume determination module is further configured to accumulate the broken stone volumes of the trucks to obtain the rock broken volume of the target blasting area.

[0119] It should be noted that: the device provided in the above-mentioned embodiments is only taken as an example for the division of the above-mentioned functional modules when realizing its functions, and in actual application, the above-mentioned functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above-described functions. In addition, the device and method embodiments provided in the above-mentioned embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be described here.

[0120] The present application also discloses an electronic device. Referring to Figure 3 , Figure 3 is a structural schematic diagram of an electronic device disclosed by the embodiments of the present application. The electronic device 300 can 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.

[0121] The communication bus 302 is configured to realize the connection and communication between the components.

[0122] The user interface 303 can include a display interface and a camera interface. Optionally, the user interface 303 can further include a standard wired interface and a wireless interface.

[0123] The network interface 304 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0124] The processor 301 can include one or more processing cores. The processor 301 connects various parts within the server through various interfaces and lines, performs 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. Alternatively, the processor 301 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 301 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes operating systems, user interface graphs, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but can be realized by a separate chip.

[0125] The memory 305 can include a random access memory (RAM) and a read-only memory (ROM). Alternatively, 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 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 305 can alternatively be at least one storage device located away from the aforementioned processor 301. Referring to Figure 3 The memory 305 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an application program of a method for determining a broken volume of a fractured rock zone.

[0126] In Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for the user to input, and obtain data input by the user; and the processor 301 can be used to invoke an application program stored in the memory 305 and storing a burst rock breaking volume determination method, which, when executed by one or more processors 301, causes the electronic device 300 to perform the method of one or more of the above-described embodiments. It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, certain steps can be performed in other order or at the same time. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0127] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0128] In the several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There can be another division manner for actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical or other forms.

[0129] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0130] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit. The integrated unit can be implemented in the form of hardware or software functional unit.

[0131] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0132] The above are only exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and practicing the disclosure.

[0133] The present application is intended to cover any variations, uses or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not described in the present disclosure. The specification and examples are only considered as exemplary.

Claims

1. A method for determining the volume of broken rock in a blasting zone, characterized in that, The method comprises the following steps: Obtain vehicle posture data, carriage deformation data, and carriage point cloud data of multiple trucks in a target blasting area; For each truck, calculate the carriage tilt angle of the truck according to the vehicle posture data, and perform posture correction processing on the carriage point cloud data according to the carriage tilt angle to obtain point cloud data samples of the truck; According to the carriage deformation data, calculate the carriage volume deformation value of the truck, and perform deformation compensation processing on the point cloud data samples according to the carriage volume deformation value to obtain target point cloud data of the truck; Determine the rock breaking volume of the target blasting area based on the target point cloud data of each truck; The carriage deformation data includes a carriage floor deflection value and a carriage side plate deformation variable, and the calculation of the carriage volume deformation value of the truck based on the carriage deformation data comprises: Obtain the carriage floor area and the carriage side plate height of the truck; Calculate the product of the carriage floor deflection value and the carriage floor area as a first volume deformation variable; Calculate the product of the carriage side plate deformation variable and the carriage side plate height as a second volume deformation variable; Take the first volume deformation variable and the second volume deformation variable as the carriage volume deformation value of the truck; The deformation compensation processing on the point cloud data samples according to the carriage volume deformation value to obtain the target point cloud data of the truck comprises: Obtain the height of each feature point in the point cloud data samples and the carriage 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 samples and the carriage side plate, 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. The vehicle posture data includes the vertical acceleration values of four corner points in the carriage of the truck, and the calculation of the carriage tilt angle of the truck based on the vehicle posture data comprises:

2. The blast zone rock breaking volume determination method of claim 1, wherein, Calculate the first acceleration average of the vertical acceleration values of the two front corner points of the carriage and the second acceleration average of the vertical acceleration values of the two rear corner points of the carriage, and determine the front-rear vertical acceleration difference value of the truck based on the first acceleration average and the second acceleration average; Calculate the third acceleration average of the vertical acceleration values of the two left corner points of the carriage and the fourth acceleration average of the vertical acceleration values of the two right corner points of the carriage, and determine the left-right vertical acceleration difference value of the truck based on the third acceleration average and the fourth acceleration average; Determine the front-rear tilt angle of the carriage according to the ratio between the front-rear vertical acceleration difference value and the length of the carriage; Determine the left-right tilt angle of the carriage according to the ratio between the left-right vertical acceleration difference value and the width of the carriage; ​ The front and rear inclination angles and the left and right inclination angles are taken as the truck carriage inclination angles.

3. The blast zone rock breaking volume determination method of claim 1, wherein, The posture correction processing of the carriage point cloud data according to the carriage inclination angles is performed to obtain the point cloud data sample of the truck. A carriage three-dimensional coordinate system of the truck is constructed, and the corresponding spatial coordinates of each feature point in the carriage point cloud data in the carriage three-dimensional coordinate system are determined. For each feature point, the longitudinal coordinate in the spatial coordinates is corrected according to the front and rear inclination angles in the carriage inclination angles to obtain the target longitudinal coordinate of the feature point. The transverse coordinate in the spatial coordinates is corrected according to the left and right inclination angles in the carriage inclination angles 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.

4. The blast zone rock breaking volume determination method of claim 1, wherein, The gravel volume of the truck is determined based on the target point cloud data of the truck, including: Obtaining the carriage empty point cloud data of the truck; Based on the carriage empty point cloud data and the target point cloud data of the truck, the gravel point cloud data of the truck is determined; A three-dimensional model corresponding to the gravel point cloud data of the truck is constructed; The volume of the three-dimensional model is calculated, and the volume of the three-dimensional model is taken as the gravel volume of the truck.

5. The blast zone rock breaking volume determination method of claim 1, wherein, The rock breaking volume of the target blasting area is determined by combining the gravel volumes of each truck, including: The gravel volumes of each truck are added to obtain the rock breaking volume of the target blasting area.

6. A blast zone rock breaking volume determination system characterized by, The system comprises: A data acquisition module is configured to acquire vehicle posture data, carriage deformation data, and carriage point cloud data of a plurality of trucks in a target blasting area; A data sample determination module is configured to, for each truck, calculate a carriage inclination angle of the 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 is configured to calculate a carriage volume deformation value of the truck according to the carriage deformation data, and perform 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; A breaking volume determination module is configured to determine a gravel volume of the truck based on the target point cloud data of the truck, and determine a rock breaking volume of the target blasting area by combining the gravel volumes of each truck; The carriage deformation data includes a carriage floor deflection value and a carriage side plate deformation variable, and the carriage volume deformation value of the truck is calculated according to the carriage deformation data, including: The carriage floor area and the carriage side plate height of the truck are obtained; The product of the carriage floor deflection value and the carriage floor area is calculated as a first volume deformation variable; The product of the carriage side plate deformation variable and the carriage side plate height is calculated as a second volume deformation variable; The first volume deformation variable and the second volume deformation variable are taken as the carriage volume deformation value of the truck; The target point cloud data of the truck is obtained by performing deformation compensation processing on the point cloud data sample according to the carriage volume deformation value, including: Obtaining the height between each feature point in the point cloud data sample and the vehicle compartment bottom plate, determining the height compensation amount of each feature point according to the ratio of each height to the first volume deformation variable; Obtaining the distance between each feature point in the point cloud data sample and the vehicle compartment side plate, determining the distance compensation amount of each feature point according to the ratio of each distance to the second volume deformation variable; Correspondingly subtracting the height compensation amount from the height of each feature point and correspondingly subtracting the distance compensation amount from the distance of each feature point to obtain the target point cloud data.

7. An electronic device, comprising: The electronic device comprises 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 to enable the electronic device to perform the blast zone rock breaking volume determination method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores instructions, when the instructions are executed, the blast zone rock breaking volume determination method according to any one of claims 1-5 is executed.

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