Coal inventory method, system and equipment based on radar point cloud data and medium

Abstract

The invention relates to a coal inventory method, system and device based on radar point cloud data and a medium. The method comprises the following steps: acquiring first point cloud data generated by scanning a site where a coal pile is located by a radar; taking a certain height above the ground of the site as a marker bed, and processing the second point cloud data corresponding to the marker bed according to a density clustering algorithm to obtain a clustering result; respectively judging whether each clustering result is a coal pile or not; if the clustering result is a coal pile, expanding the third point cloud data corresponding to the clustering result to obtain fourth point cloud data of the whole coal pile; and calculating the volume of the coal pile based on the fourth point cloud data of the whole coal pile. According to the technical scheme provided by the invention, errors caused by interference of a non-target object in a traditional measurement method are avoided. According to the method, the accuracy of coal quantity calculation is improved, the reliability and credibility of data are enhanced, and powerful support is provided for fine management of coal mine enterprises and effective supervision of supervision organizations.

Description

Technical Field

[0001] The present invention relates to the field of volume detection, and in particular to a coal counting method, system, equipment and medium based on radar point cloud data. Background Art

[0002] In the process of coal production, transportation and sales, the inventory of coal in the coal bunker is crucial for coal mining companies, groups and regulatory agencies. Specifically:

[0003] 1. Regulatory needs: Coal mine regulatory agencies need to monitor the production and sales of coal mines in real time to prevent coal mines from concealing or underreporting raw coal production and commercial coal sales. These behaviors not only violate relevant laws and regulations, but may also lead to overcapacity production of coal mines and increase safety hazards. Accurate coal bunker coal quantity data helps regulatory agencies to identify problems in a timely manner and take corresponding regulatory measures to ensure the compliance of coal mines.

[0004] 2. Enterprise management needs: Coal mining enterprises or groups need to master the production progress and arrange production plans reasonably. Accurate data on coal storage can assist enterprises or groups in rationally arranging key links such as mining work plans and transportation and marketing scheduling. With timely and accurate coal quantity information, enterprises can optimize resource allocation, improve production efficiency, reduce costs, and enhance market competitiveness.

[0005] At present, one of the commonly used coal volume measurement technologies in coal bunkers is the radar point cloud measurement method. This method generates point cloud data of the target area by emitting radar waves and receiving reflected signals, and then calculates the volume of the object. However, the existing radar point cloud measurement technology has some significant limitations:

[0006] 1. Lack of logical judgment: The existing radar point cloud measurement method does not have the logic to detect the target object, but instead performs a comprehensive calculation of the volume of all objects. This means that when there are non-target objects (such as debris, equipment, etc.) in the coal bunker, the volume of these non-target objects will be included, resulting in inaccurate calculation results.

[0007] 2. Large error: Since it is impossible to distinguish between target objects and non-target objects, the existing measurement method is prone to large errors in complex environments, affecting the accuracy of coal quantity calculation. Especially when there are many different substances in the coal bunker, this error will be further amplified.

[0008] In summary, the existing coal quantity measurement technology in coal bunkers has obvious deficiencies in practical applications and cannot meet the high requirements of coal mining enterprises, groups and regulatory agencies for the accuracy of coal quantity data. Therefore, it is urgent to develop a new measurement technology that can accurately identify target objects and exclude the influence of non-target objects to improve the accuracy and reliability of coal quantity inventory. Summary of the invention

[0009] In order to overcome the problems existing in the related art, the present invention provides a coal counting method, system, equipment and medium based on radar point cloud data.

[0010] According to a first aspect of an embodiment of the present invention, a method for collecting coal based on radar point cloud data is provided, comprising:

[0011] Acquire first point cloud data generated by radar scanning the site where the coal pile is located;

[0012] A certain height above the ground of the site is used as a landmark layer, and the second point cloud data corresponding to the landmark layer is processed according to a density clustering algorithm to obtain a clustering result;

[0013] Determine respectively whether each of the clustering results is a coal pile;

[0014] If the clustering result is a coal pile, the third point cloud data corresponding to the clustering result is expanded to obtain fourth point cloud data of the entire coal pile;

[0015] The volume of the coal pile is calculated based on the fourth point cloud data of the entire coal pile.

[0016] Further, judging whether each of the clustering results is a coal pile respectively includes:

[0017] In a spatial rectangular coordinate system, respectively calculating the distance between each point in the clustering result and a plurality of straight lines;

[0018] Calculate the proportion of points whose difference between all distances between points in each clustering result and a straight line is less than a threshold, and take the maximum value of the proportion as the symmetry factor of the clustering result;

[0019] According to the comparison result between the symmetry factor and the threshold, it is judged whether the corresponding clustering result is a coal pile.

[0020] Further, the third point cloud data corresponding to the clustering result is expanded to obtain fourth point cloud data of the entire coal pile, which specifically includes:

[0021] Obtaining the elevation of the ground according to the first point cloud data analysis;

[0022] According to the comparison result of the distance between each point in the first point cloud data and a point in the third point cloud data and a threshold, the third point cloud data is extended upward to the top of the coal pile and downward to the elevation of the ground.

[0023] Further, after obtaining the first point cloud data generated by the radar scanning of the site where the coal pile is located, the method further includes:

[0024] The first point cloud data is subjected to coarse-graining processing.

[0025] According to a second aspect of an embodiment of the present invention, a coal panning system based on radar point cloud data is provided, comprising:

[0026] A data acquisition module, used to acquire first point cloud data generated by radar scanning the site where the coal pile is located;

[0027] A clustering analysis module, used for taking a certain height above the ground of the site as a landmark layer, and processing the second point cloud data corresponding to the landmark layer according to a density clustering algorithm to obtain a clustering result;

[0028] A coal pile determination module, used to determine whether each of the clustering results is a coal pile;

[0029] a data expansion module, configured to expand the third point cloud data corresponding to the clustering result to obtain fourth point cloud data of the entire coal pile if the clustering result is a coal pile;

[0030] A volume calculation module is used to calculate the volume of the coal pile based on the fourth point cloud data of the entire coal pile.

[0031] Furthermore, the coal pile judgment module is specifically used for:

[0032] In a spatial rectangular coordinate system, respectively calculating the distance between each point in the clustering result and a plurality of straight lines;

[0033] Calculate the proportion of points whose difference between all distances between points in each clustering result and a straight line is less than a threshold, and take the maximum value of the proportion as the symmetry factor of the clustering result;

[0034] According to the comparison result between the symmetry factor and the threshold, it is judged whether the corresponding clustering result is a coal pile.

[0035] Furthermore, the data expansion module is specifically used for:

[0036] Obtaining the elevation of the ground according to the first point cloud data analysis;

[0037] According to the comparison result of the distance between each point in the first point cloud data and a point in the third point cloud data and a threshold, the third point cloud data is extended upward to the top of the coal pile and downward to the elevation of the ground.

[0038] Furthermore, the system includes:

[0039] The data processing module is used to perform coarse-graining processing on the first point cloud data after the data acquisition module acquires the first point cloud data generated by the radar scanning of the site where the coal pile is located.

[0040] According to a third aspect of an embodiment of the present invention, a terminal device is provided, including:

[0041] Processor; and

[0042] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is caused to execute the method as described above.

[0043] According to a fourth aspect of an embodiment of the present invention, there is provided a non-transitory machine-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to execute the method as described above.

[0044] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0045] The present invention can realize accurate inventory of the total amount of coal piles, avoiding the errors caused by interference from non-target objects in traditional measurement methods. This method not only improves the accuracy of coal quantity calculation, but also enhances the reliability and credibility of data, providing strong support for the refined management of coal mining enterprises and the effective supervision of regulatory agencies.

[0046] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0048] Figure 1 It is a flow chart of a coal panning method based on radar point cloud data according to an exemplary embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of the radar point cloud scanning results of the coal pile site environment;

[0050] Figure 3 It is a schematic diagram for calculating the distance between a point and a straight line;

[0051] Figure 4 It is a structural block diagram of a coal panning system based on radar point cloud data according to an exemplary embodiment of the present invention.

[0052] Figure 5 It is a schematic diagram showing the structure of a computing device according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0053] The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0054] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0055] It should be understood that although the terms "first", "second", "third", etc. may be used to describe various information in the present invention, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0056] The technical solutions of the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0057] Figure 1 It is a flow chart of a coal panning method based on radar point cloud data according to an exemplary embodiment of the present invention.

[0058] See also Figure 1 , the method comprising:

[0059] 110. Obtain the first point cloud data generated by radar scanning the site where the coal pile is located.

[0060] Specifically, in one embodiment, the coal pile is piled in a coal bunker, and a radar can be installed at a high position in the coal bunker to ensure that the radar scanning range can cover the entire coal pile. The radar scans the surrounding environment at a fixed frequency. For example, the scanning frequency of a certain type of radar is about once every 3-5 minutes, and the amount of point cloud data for each scan is about 3 million points (x, y, and z are the three directions of the scanning data, respectively, following the Cartesian coordinate system, where z is the vertical coordinate perpendicular to the ground). To ensure the timeliness of the analysis, the quantity and volume of the coal pile must be calculated before the next radar scan. The point cloud scanning diagram of the coal bunker is shown in the figure. Figure 2 shown.

[0061] Optionally, in this embodiment, to speed up the analysis, after step 110, the method further includes:

[0062] 111. Perform coarse-graining processing on the first point cloud data.

[0063] Specifically, coarse-graining means using one point to represent multiple points around it. In one embodiment, if the original data precision is 0.01m, the data precision is processed to 0.5m, that is, all points in the original data with coordinates ranging from 0≤x<0.5m, 0≤y<0.5m, 0≤z<0.5m are processed into one point with coordinates of x=0.25m, y=0.25m, z=0.25m. The coarse-grained data is deduplicated, and the amount of processed data is less than 1 / 10 of the original data, thereby speeding up the analysis.

[0064] 120. A certain height above the ground of the site is taken as a landmark layer, and the second point cloud data corresponding to the landmark layer is processed according to a density clustering algorithm to obtain a clustering result.

[0065] Specifically, Figure 2 As shown, since the point cloud data includes both the target object coal pile and non-target objects such as coal stackers, ground, pillars, walls, etc., the point cloud data of various objects are continuous and not easy to classify. In this embodiment, a certain height above the ground of the site is used as a marker layer. The marker layer needs to basically include all objects, and the objects should be as unconnected as possible.

[0066] In one embodiment, if the point cloud data is coarse-grained, the process of obtaining the point cloud data of the marker layer is as follows: the projection of each coarse-grained point on the ground is relative to the square of the precision. For example, if the precision is 0.5m, the projection area of ​​each point is 0.25m. 2 , calculate the projection area of ​​all coarse-grained points on the ground (no repeated calculation), recorded as S. Calculate the projection area of ​​the coarse-grained data from the current height to the top part on the ground according to the set height. When the projection area exceeds 0.8S, the data corresponding to the current height is set as the marker layer.

[0067] The marker layer corresponds to the original data and is clustered according to density (such as DBSCAN). The clustering radius can be the same as the processing accuracy of the coarse-grained processing, such as 0.5m.

[0068] 130. Determine whether each clustering result is a coal pile.

[0069] Specifically, in this step, the shape features of different objects reflected in the point cloud data can be used to analyze the clustering results of each category identified in step 120, and determine whether it is a coal pile for object identification. For example, a trained target recognition algorithm can be used, but such methods have high requirements on equipment. The applicant has found through research that the outer surfaces of interfering objects such as pillars and coal stackers in the coal pile site are generally relatively flat, while the surface of the coal pile presents an irregular curved surface. Based on further analysis of the point cloud data of these two surfaces, a simple target category judgment method is obtained. Specifically, the distance difference between each point in the coal pile and the straight line is large, while the distance difference between other interfering objects and the straight line is large.

[0070] Optionally, step 130 specifically includes:

[0071] 1301. In a spatial rectangular coordinate system, respectively calculate the distance between each point in the clustering result and a plurality of straight lines;

[0072] 1302. Calculate the proportion of points whose difference between all distances between a point in each clustering result and a straight line is less than a threshold, and use the maximum value of the proportion as the symmetry factor of the clustering result;

[0073] 1303. Determine whether the corresponding clustering result is a coal pile based on the comparison result between the symmetry factor and the threshold.

[0074] Specifically, based on the above analysis, in one embodiment, step 130 is implemented by the following specific steps:

[0075] 1. If Figure 3 As shown, construct a straight line y = tan (α) x, where -90° ≤ α < 90°, with an interval of 1°, for a total of 180 straight lines;

[0076] 2. Calculate the distance from each original point to each straight line in the clustered cone object;

[0077] 3. Count the proportion of points whose distance difference from the original point to the straight line with angle α is less than 0.05m α ;

[0078] 4. Calculate r α The maximum value of , which is the symmetry factor μ of this type of object;

[0079] 5. If μ>5%, the object is considered to be a man-made object, not a coal pile.

[0080] Follow the above steps to judge each type of object separately and filter out the collection of coal piles.

[0081] 140. If the clustering result is a coal pile, the third point cloud data corresponding to the clustering result is expanded to obtain fourth point cloud data of the entire coal pile.

[0082] Specifically, since the clustering results are in a marker layer at a certain height above the ground, in this step, the clustering results of the coal pile need to be expanded from the marker layer upward to the top of the coal pile, and from the marker layer downward to the ground, so as to obtain the point cloud data of the entire coal pile.

[0083] Optionally, in this embodiment, step 140 specifically includes:

[0084] 1401. Analyze the first point cloud data to obtain the elevation of the ground;

[0085] 1402. Based on the comparison result of the distance between each point in the first point cloud data and a point in the third point cloud data and a threshold, the third point cloud data is extended upward to the top of the coal pile and downward to the elevation of the ground.

[0086] Specifically, the elevation of the ground can be determined by the height value of the point cloud data. Due to the presence of coverings on the ground, the ground point cloud data scanned by the radar will contain noise points. Therefore, the lowest value of the point cloud data cannot be directly used as the elevation of the floor. Instead, a certain height above the lowest value is taken as the elevation of the floor. In a specific embodiment, the cumulative distribution function F can be constructed based on the z value in the point cloud data. z (z)=P(Z≤z), the function value is F z The elevation at (z) = 0.2 is the elevation of the bottom plate.

[0087] Calculate the minimum distance from each layer point to the current coal pile respectively. If the minimum distance is less than the threshold (such as 0.5m), the point is considered to belong to the current coal pile. If the minimum distance from the point to multiple coal piles is less than the threshold, the point is considered to belong to the nearest coal pile. Follow this step to calculate all points above the marker layer and below the marker layer to the ground elevation.

[0088] 150. Calculate the volume of the coal pile based on the fourth point cloud data of the entire coal pile.

[0089] Specifically, the volume calculation based on the point cloud data of the target can be achieved by using a variety of existing technologies, such as three-dimensional fitting reconstruction, etc. When the accuracy requirement is not high, in order to reduce the calculation complexity, the following method can also be used: After the point cloud data is coarsened to a certain accuracy (taking 0.1m as an example) according to the aforementioned method, the x values ​​are arranged, and the areas composed of y and z coordinates corresponding to each x value are calculated respectively. The area corresponding to each x is accumulated and multiplied by the accuracy of 0.1m, and the volume of each coal pile can be estimated.

[0090] The embodiment of the present invention provides a method for counting coal based on radar point cloud data, which can realize accurate counting of the total amount of coal piles and avoid errors caused by interference from non-target objects in traditional measurement methods. This method not only improves the accuracy of coal quantity calculation, but also enhances the reliability and credibility of data, providing strong support for the refined management of coal mining enterprises and the effective supervision of regulatory agencies.

[0091] Figure 4 It is a structural block diagram of a coal panning system based on radar point cloud data according to an exemplary embodiment of the present invention.

[0092] See also Figure 4 , the system comprises:

[0093] A data acquisition module, used to acquire first point cloud data generated by radar scanning the site where the coal pile is located;

[0094] A clustering analysis module, used for taking a certain height above the ground of the site as a landmark layer, and processing the second point cloud data corresponding to the landmark layer according to a density clustering algorithm to obtain a clustering result;

[0095] A coal pile determination module, used to determine whether each of the clustering results is a coal pile;

[0096] a data expansion module, configured to expand the third point cloud data corresponding to the clustering result to obtain fourth point cloud data of the entire coal pile if the clustering result is a coal pile;

[0097] A volume calculation module is used to calculate the volume of the coal pile based on the fourth point cloud data of the entire coal pile.

[0098] Optionally, in this embodiment, the coal pile judgment module is specifically used to:

[0099] In a spatial rectangular coordinate system, respectively calculating the distance between each point in the clustering result and a plurality of straight lines;

[0100] Calculate the proportion of points whose difference between all distances between points in each clustering result and a straight line is less than a threshold, and take the maximum value of the proportion as the symmetry factor of the clustering result;

[0101] According to the comparison result between the symmetry factor and the threshold, it is judged whether the corresponding clustering result is a coal pile.

[0102] Optionally, in this embodiment, the data expansion module is specifically used to:

[0103] Obtaining the elevation of the ground according to the first point cloud data analysis;

[0104] According to the comparison result of the distance between each point in the first point cloud data and a point in the third point cloud data and a threshold, the third point cloud data is extended upward to the top of the coal pile and downward to the elevation of the ground.

[0105] Optionally, in this embodiment, the system further includes:

[0106] The data processing module is used to perform coarse-graining processing on the first point cloud data after the data acquisition module acquires the first point cloud data generated by the radar scanning of the site where the coal pile is located.

[0107] Regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0108] Figure 5 It is a schematic diagram showing the structure of a computing device according to an exemplary embodiment of the present invention.

[0109] See also Figure 5 , the computing device 500 includes a memory 510 and a processor 520 .

[0110] The processor 520 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0111] The memory 510 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage. Among them, ROM can store static data or instructions required by the processor 520 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory 510 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 510 may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (e.g., DVD-ROM, double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. The computer-readable storage medium does not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0112] The memory 510 stores executable codes, and when the executable codes are processed by the processor 520 , the processor 520 can execute part or all of the methods described above.

[0113] In addition, the method according to the present invention may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing part or all of the steps in the above method of the present invention.

[0114] Alternatively, the present invention may also be implemented as a non-temporary machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) on which executable code (or computer program, or computer instruction code) is stored. When the executable code (or computer program, or computer instruction code) is executed by a processor of an electronic device (or computing device, server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present invention.

[0115] The scheme of the present invention has been described in detail above with reference to the accompanying drawings. In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. Those skilled in the art should also be aware that the actions and modules involved in the specification are not necessarily required by the present invention. In addition, it can be understood that the steps in the method of the embodiment of the present invention can be adjusted in order, merged and deleted according to actual needs, and the modules in the device of the embodiment of the present invention can be merged, divided and deleted according to actual needs.

[0116] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or a combination of both.

[0117] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system and method according to multiple embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0118] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein are selected to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for collecting coal based on radar point cloud data, characterized in that: include: Acquire first point cloud data generated by radar scanning the site where the coal pile is located; A certain height above the ground of the site is used as a landmark layer, and the second point cloud data corresponding to the landmark layer is processed according to a density clustering algorithm to obtain a clustering result; Determine respectively whether each of the clustering results is a coal pile; If the clustering result is a coal pile, the third point cloud data corresponding to the clustering result is expanded to obtain fourth point cloud data of the entire coal pile; The volume of the coal pile is calculated based on the fourth point cloud data of the entire coal pile.

2. The method according to claim 1, characterized in that Determining whether each of the clustering results is a coal pile respectively includes: In a spatial rectangular coordinate system, respectively calculating the distance between each point in the clustering result and a plurality of straight lines; Calculate the proportion of points whose difference between all distances between points in each clustering result and a straight line is less than a threshold, and take the maximum value of the proportion as the symmetry factor of the clustering result; According to the comparison result between the symmetry factor and the threshold, it is judged whether the corresponding clustering result is a coal pile.

3. The method according to claim 1, characterized in that: The third point cloud data corresponding to the clustering result is expanded to obtain the fourth point cloud data of the entire coal pile, which specifically includes: Obtaining the elevation of the ground according to the first point cloud data analysis; According to the comparison result of the distance between each point in the first point cloud data and a point in the third point cloud data and a threshold, the third point cloud data is extended upward to the top of the coal pile and downward to the elevation of the ground.

4. The method according to any one of claims 1 to 3, characterized in that: After obtaining the first point cloud data generated by the radar scanning of the site where the coal pile is located, it also includes: The first point cloud data is subjected to coarse-graining processing.

5. A coal panning system based on radar point cloud data, characterized in that: include: A data acquisition module, used to acquire first point cloud data generated by radar scanning the site where the coal pile is located; A clustering analysis module, used for taking a certain height above the ground of the site as a landmark layer, and processing the second point cloud data corresponding to the landmark layer according to a density clustering algorithm to obtain a clustering result; A coal pile determination module, used to determine whether each of the clustering results is a coal pile; a data expansion module, configured to expand the third point cloud data corresponding to the clustering result to obtain fourth point cloud data of the entire coal pile if the clustering result is a coal pile; A volume calculation module is used to calculate the volume of the coal pile based on the fourth point cloud data of the entire coal pile.

6. The system according to claim 5, characterized in that The coal pile judgment module is specifically used for: In a spatial rectangular coordinate system, respectively calculating the distance between each point in the clustering result and a plurality of straight lines; Calculate the proportion of points whose difference between all distances between points in each clustering result and a straight line is less than a threshold, and take the maximum value of the proportion as the symmetry factor of the clustering result; According to the comparison result between the symmetry factor and the threshold, it is judged whether the corresponding clustering result is a coal pile.

7. The system according to claim 5, characterized in that The data expansion module is specifically used for: Obtaining the elevation of the ground according to the first point cloud data analysis; According to the comparison result of the distance between each point in the first point cloud data and a point in the third point cloud data and a threshold, the third point cloud data is extended upward to the top of the coal pile and downward to the elevation of the ground.

8. The system according to claims 5-7, characterized in that: Also includes: The data processing module is used to perform coarse-graining processing on the first point cloud data after the data acquisition module acquires the first point cloud data generated by the radar scanning of the site where the coal pile is located.

9. A terminal device, characterized in that: include: processor; as well as A memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the method according to any one of claims 1 to 4.

10. A non-transitory machine-readable storage medium having executable code stored thereon, characterized in that: When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as claimed in any one of claims 1 to 4.

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