Tailing pond water surface map generation method and device, electronic equipment and storage medium

Abstract

The invention provides a tailing pond water surface map generation method and device, electronic equipment and a storage medium. The method comprises the steps that point cloud data and a preset voxel map are acquired; embedding the point cloud data into a preset voxel map to generate a target voxel map; determining a target voxel in the target voxel map, and calculating a feature value of the target voxel; judging whether the target voxel is a plane based on the characteristic value; and combining all successfully judged planes to generate a planar graph of the target tailings pond, and identifying the planar graph of the target tailings pond to determine a water surface graph of the target tailings pond. By obtaining the point cloud data and the preset voxel map and combining subsequent calculation and judgment processes, automatic processing is achieved, manual participation is reduced, the water surface map generation efficiency is improved, meanwhile, the surface morphology of the target tailings pond can be recognized and described more accurately, and the water surface map generation efficiency is improved. And meanwhile, the accuracy of judging whether the target voxel is a plane or not and subsequent merging can be improved through the characteristic value of the target voxel.

Description

Technical Field

[0001] The present disclosure relates to the technical field of point cloud data processing, and in particular to a method, device, electronic device and storage medium for generating a water surface map of a tailings pond. Background Art

[0002] Tailings ponds are places for storing tailings or other industrial waste discharged after ore sorting in metal or non-metal mines. They are essential infrastructure and environmental protection projects for mining companies. The high potential energy of tailings ponds makes the mining site potentially dangerous for man-made debris flows. Once an accident occurs, it is very easy to cause a dam collapse and trigger a serious safety accident.

[0003] The semantic segmentation of tailings pond feature information (such as initial dam, accumulation dam, water surface, dry beach, etc.) is an important basis for analyzing the current status of tailings ponds. The semantic segmentation of tailings pond feature information is an important support for measuring indicators such as the length of dry beach. Traditional methods for analyzing the current status of tailings ponds usually rely on manual work, through manual on-site inspections or manual use of measurement tools to obtain geometric information of tailings ponds. However, the manual operation mode is inefficient and the data integrity is not strong. Summary of the invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, an object of the present disclosure is to propose a method for generating a water surface map of a tailings pond.

[0006] The second objective of the present disclosure is to provide a device for generating a water surface map of a tailings pond.

[0007] A third objective of the present disclosure is to provide an electronic device.

[0008] A fourth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0009] A fifth object of the present disclosure is to provide a computer program product.

[0010] To achieve the above-mentioned purpose, the first aspect of the present disclosure proposes a method for generating a water surface map of a tailings pond, comprising: obtaining point cloud data of a target tailings and a pre-configured preset voxel map; embedding the point cloud data into the preset voxel map to generate a target voxel map; determining the target voxel in the target voxel map, and calculating the eigenvalue of the target voxel; judging whether the target voxel is a plane based on the eigenvalue; merging all planes that have been successfully judged to generate a plan view of the target tailings pond, and determining the water surface map of the target tailings pond after identifying the plan view of the target tailings pond.

[0011] According to one embodiment of the present disclosure, the point cloud data is embedded into the preset voxel map to generate a target voxel map, including: for any element point in the point cloud data, dividing the coordinates of the element point by the maximum voxel size to calculate the transformation coordinates of the element point; and establishing a target element point corresponding to the element point in the target voxel map based on the transformation coordinates to generate a target voxel map.

[0012] According to one embodiment of the present disclosure, the determining the target voxel in the target voxel map includes: taking a candidate voxel containing a target element point as the target voxel.

[0013] According to one embodiment of the present disclosure, judging whether the target voxel is a plane based on the eigenvalue includes: determining the eigenvalue of the target voxel based on all target element points in the target voxel; taking the minimum value among the candidate eigenvalues ​​as the target eigenvalue, and comparing the target eigenvalue with a judgment threshold; and determining that the target voxel is a plane in response to the target eigenvalue being less than the judgment threshold.

[0014] According to one embodiment of the present disclosure, the method also includes: in response to the target feature value being greater than or equal to the judgment threshold, dividing the target voxel into a preset number of sub-voxels; for any sub-voxel, obtaining a candidate feature value of the sub-voxel; taking the minimum value among the candidate feature values ​​as the target feature value, and comparing based on the target feature value and the judgment threshold; when the target feature value is greater than or equal to the judgment threshold, repeating the above-mentioned steps of dividing the target voxel into a preset number of new target voxels and subsequent steps until the target feature value after division is less than the judgment threshold or the number of divisions reaches the set number, stopping the division, and outputting the result; or, in response to the target feature value being less than the judgment threshold, determining that the sub-voxel is a plane.

[0015] According to one embodiment of the present disclosure, all planes that are successfully judged are merged to generate a plan view of the target tailings pond, including: for any plane, all element points on the plane are combined in pairs to generate multiple edge combinations; in response to the existence of two planes with the same edge combinations, the two planes are merged.

[0016] According to one embodiment of the present disclosure, calculating the eigenvalue of the target voxel includes: calculating the first vector, the second vector and the third vector of the target voxel based on the element point distribution in the target voxel; and calculating the eigenvalue of the target voxel based on the first vector, the second vector and the third vector.

[0017] To achieve the above-mentioned purpose, the second aspect of the present disclosure proposes a device for generating a water surface map of a tailings pond, comprising: an acquisition module, used to acquire point cloud data of a target tailings and a pre-configured preset voxel map; an embedding module, used to embed the point cloud data into the preset voxel map to generate a target voxel map; a calculation module, used to determine the target voxel in the target voxel map and calculate the eigenvalue of the target voxel; a judgment module, used to judge whether the target voxel is a plane based on the eigenvalue; a generation module, used to merge all planes that have been successfully judged to generate a plan view of the target tailings pond, and determine the water surface map of the target tailings pond after identifying the plan view of the target tailings pond.

[0018] To achieve the above-mentioned purpose, the third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the method for generating a tailings pond water surface map as described in the first aspect embodiment of the present disclosure.

[0019] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the method for generating a tailings pond water surface map as described in the first aspect embodiment of the present disclosure.

[0020] To achieve the above-mentioned purpose, the fifth aspect of the present disclosure proposes a computer program product, including a computer program, which, when executed by a processor, is used to implement the method for generating a tailings pond water surface map as described in the first aspect of the present disclosure.

[0021] Therefore, by acquiring point cloud data and preset voxel maps, combined with subsequent calculation and judgment processes, automated processing is achieved, manual participation is reduced, and the efficiency of water surface map generation is improved. At the same time, it helps to more accurately identify and depict the surface morphology of the target tailings pond. At the same time, the characteristic value of the target voxel can improve the accuracy of judging whether the target voxel is a plane and the subsequent merging. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of a method for generating a water surface map of a tailings pond according to an embodiment of the present disclosure;

[0023] Figure 2 is a schematic diagram of another method for generating a water surface map of a tailings pond according to an embodiment of the present disclosure;

[0024] Figure 3 is a schematic diagram of another method for generating a water surface map of a tailings pond according to an embodiment of the present disclosure;

[0025] Figure 4 is a schematic diagram of another method for generating a water surface map of a tailings pond according to an embodiment of the present disclosure;

[0026] Figure 5 It is a schematic diagram of a tailings pond water surface map generating device according to one embodiment of the present disclosure;

[0027] Figure 6 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0029] The acquisition, storage, use, and processing of data in the technical solution disclosed in this disclosure are in compliance with the relevant provisions of relevant laws and regulations.

[0030] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0031] Figure 1 is a schematic diagram of a method for generating a tailings pond water surface map according to an embodiment of the present disclosure, such as Figure 1 As shown, the method for generating a tailings pond water surface map includes the following steps:

[0032] S101, obtaining point cloud data of a target tailings and a pre-configured preset voxel map.

[0033] The method for generating a tailings pond water surface map according to an embodiment of the present application can be applied to the scenario of collecting horizontal surface data of a tailings pond. The executor for generating a tailings pond water surface map according to an embodiment of the present application can be the tailings pond water surface map generating device according to an embodiment of the present application, and the tailings pond water surface map generating device can be set on an electronic device.

[0034] In the disclosed embodiment, a voxel map is a data structure used to represent the distribution of objects in a three-dimensional space. It divides the entire space into many small cubic units, so-called "voxels", and each voxel records the information of whether there is an object in the area.

[0035] It should be noted that the preset voxel map in the embodiment of the present disclosure is set in advance, and the preset voxel map does not include specific information, and may be an initial voxel map established according to the actual contour or area of ​​the target tailings.

[0036] In the disclosed embodiment, point cloud data of the target tailings pond can be collected by using an aerial survey multi-rotor drone, which includes an intelligent obstacle avoidance module, a high-precision three-axis gimbal laser radar, and an integrated real-time dynamic positioning system (Real-Time Kinematic, RTK) module; the RTK module can provide real-time centimeter-level positioning data for the drone, and design the flight route and camera working mode parameters of the drone. Image processing software is used to generate three-dimensional point cloud data.

[0037] S102, embedding the point cloud data into a preset voxel map to generate a target voxel map.

[0038] In the embodiment of the present disclosure, after acquiring the point cloud data, there may be multiple methods for embedding the point cloud data into the preset voxel map, which are not limited here.

[0039] In one possible implementation, based on matching the data coordinates of the elements in the point cloud data with the coordinates in a preset voxel map, the elements of the point cloud data can be replaced with the successfully matched points to generate a target voxel map.

[0040] In another possible implementation, the point cloud data can also be processed by the generative model to embed the point cloud data into a preset voxel map. It should be noted that the generative model is pre-trained and can be stored in the storage space of the electronic device for easy retrieval when needed.

[0041] S103, determining the target voxel in the target voxel map, and calculating the characteristic value of the target voxel.

[0042] It should be noted that the characteristic value in the embodiment of the present disclosure is a value for determining the distribution position or distribution trend of the target voxel in the entire target voxel map.

[0043] There are many methods for calculating the eigenvalue of the target voxel, which are not limited here. For example, the data contained in the target voxel can be calculated by an eigenvalue algorithm to generate features. The eigenvalue algorithm is designed in advance and can be changed according to actual design needs, which is not limited here.

[0044] It should be noted that the data contained in the target voxel may be of various types, which are not limited here. For example, the data may include the distribution position and number of element points in the target voxel.

[0045] S104: Determine whether the target voxel is a plane based on the eigenvalue.

[0046] It should be noted that after the eigenvalues ​​are obtained, there are many methods for determining whether the target voxel is a plane based on the eigenvalues, and no limitation is made here.

[0047] In a possible implementation, the characteristic value may be calculated by a judgment algorithm to generate a judgment result. The judgment algorithm is designed in advance and may be changed according to actual design requirements.

[0048] In another possible approach, the eigenvalues ​​may be processed by a judgment model to determine whether the target voxel is a plane. The judgment model is trained in advance and may be stored in a storage space of an electronic device for easy retrieval when needed.

[0049] S105, merging all successfully judged planes to generate a plane map of the target tailings pond, and determining a water surface map of the target tailings pond after identifying the plane map of the target tailings pond.

[0050] It should be noted that due to the low reflectivity of the water surface, the LiDAR cannot obtain point cloud data of the water surface, so the water surface area in the point cloud map is a hole. By observing the point cloud map, it can be found that the water surface area is usually a completely closed or semi-closed hole, so the boundary of the hole can be extracted to estimate the range and height of the water surface.

[0051] In the disclosed embodiment, the point cloud data of the target tailings and the pre-configured preset voxel map are first obtained, and then the point cloud data is embedded in the preset voxel map to generate the target voxel map, and then the target voxel in the target voxel map is determined, and the characteristic value of the target voxel is calculated, and then the target voxel is judged based on the characteristic value whether it is a plane, and finally all the planes that are successfully judged are merged to generate a plan view of the target tailings pond, and the water surface map of the target tailings pond is determined after the plan view of the target tailings pond is identified. Thus, by acquiring point cloud data and preset voxel maps, combined with subsequent calculation and judgment processes, automated processing is achieved, manual participation is reduced, and the efficiency of water surface map generation is improved. At the same time, it helps to more accurately identify and depict the surface morphology of the target tailings pond. At the same time, the characteristic value of the target voxel can improve the accuracy of judging whether the target voxel is a plane and the subsequent merging.

[0052] It should be noted that the candidate voxel containing the target element point is used as the target voxel.

[0053] In the above embodiment, the point cloud data is embedded into the preset voxel map to generate the target voxel map. Figure 2 Explaining further, the method includes:

[0054] S201 , for any element point in the point cloud data, dividing the coordinates of the element point by the maximum voxel size to calculate and obtain the transformed coordinates of the element point.

[0055] In the disclosed embodiment, the maximum voxel size is the conversion rate value of the coordinate conversion between the point cloud data and the preset voxel map. The maximum voxel size corresponding to different point cloud data may be different, and no limitation is made here. It can be determined according to the size of the actual point cloud data and the size of the preset voxel map.

[0056] In a possible implementation manner, the maximum voxel size may be determined according to a conversion ratio between coordinates of elements of the point cloud data and world coordinates, and according to a conversion ratio between coordinates of a preset voxel map and world coordinates.

[0057] S202: establishing a target element point corresponding to the element point in the target voxel map based on the converted coordinates to generate a target voxel map.

[0058] In the embodiment of the present disclosure, first, for any element point in the point cloud data, the coordinates of the element point are divided by the maximum voxel size to calculate the transformation coordinates of the element point, and then the target element point corresponding to the element point is established in the target voxel map based on the transformation coordinates to generate the target voxel map.

[0059] In the above embodiment, whether the target voxel is a plane is determined based on the eigenvalue, and the Figure 3 Explaining further, the method includes:

[0060] S301, determining a characteristic value of a target voxel based on all target element points in the target voxel.

[0061] In the embodiment of the present disclosure, the eigenvalue of the target voxel is calculated. The first vector, the second vector and the third vector of the target voxel can be calculated based on the element point distribution in the target voxel, and then the eigenvalue of the target voxel is calculated based on the first vector, the second vector and the third vector.

[0062] Among them, the first vector is L_max (maximum eigenvalue):

[0063] Physical meaning: L_max is the largest of the three eigenvalues, and its corresponding eigenvector indicates the main direction of the point cloud data distribution, that is, the data change (variance) in this direction is the largest. If the point cloud data is projected in the direction of this eigenvector, the distribution of the projected points will show the largest variance, which means that the data extends the farthest along this direction.

[0064] The second vector is L_middle (middle eigenvalue):

[0065] Physical meaning: L_middle is located in the middle of the three eigenvalues, and the corresponding eigenvector indicates the second main direction of the point cloud data, that is, the data changes in this direction secondarily. The second vector is similar to the case of the largest eigenvalue, but in this direction, the variance of the point cloud data is smaller than that in the direction corresponding to L_max. If projection is performed in the direction of this eigenvector, the stretch of the data will be smaller than that in the direction corresponding to L_max.

[0066] The third vector is L_min (minimum eigenvalue):

[0067] Physical meaning: L_min is the smallest of the three eigenvalues, and its corresponding eigenvector indicates the third main direction of the point cloud data, that is, the data change (variance) in this direction is the smallest. This direction is usually the "minimum extension" direction of the data distribution. If the projection is performed in the direction of this eigenvector, the variance of the projected point will be the smallest, indicating that the data has almost no extension in this direction.

[0068] Through these three eigenvectors, the original data can be projected into a new three-dimensional space, which is the main change direction of the original data. This can help reduce the dimensionality of the data while retaining most of the data information.

[0069] Data alignment and registration: When dealing with the alignment and registration problems of multiple point cloud data, the feature vector can be used as a reference direction for alignment.

[0070] Shape Analysis: By analyzing the size of the eigenvalues, we can understand the shape characteristics of the data set. For example, if L_min is close to 0, it may mean that the data is almost a line in one dimension.

[0071] S302: Taking the minimum value among the candidate feature values ​​as the target feature value, and performing a comparison based on the target feature value and the judgment threshold.

[0072] It can be seen from the above embodiment that the target eigenvalue is the value corresponding to the third eigenvector, so the value corresponding to the third eigenvector can be compared with the judgment threshold to judge whether the point cloud data is sufficiently dispersed, so that it can be considered that these points are roughly located on a plane.

[0073] It should be noted that the judgment threshold is a critical value for judging whether the target voxel is a plane. In the embodiment of the present disclosure, the judgment threshold is designed in advance and can be changed according to actual design needs, and no limitation is made here.

[0074] S303: In response to the target feature value being smaller than the judgment threshold, determining that the target voxel is a plane.

[0075] In the disclosed embodiment, the characteristic value of the target voxel is first determined based on all target element points in the target voxel, and then the minimum value among the candidate characteristic values ​​is used as the target characteristic value, and the target characteristic value is compared with the judgment threshold, and finally, in response to the target characteristic value being less than the judgment threshold, the target voxel is determined to be a plane. This method realizes the effective identification of the plane through streamlined calculation steps and clear judgment criteria, which not only ensures the accuracy of the judgment but also takes into account the efficiency, creating favorable conditions for various subsequent analyses and applications.

[0076] In another possible implementation, in response to a target eigenvalue being greater than or equal to a judgment threshold, the target voxel is divided into a preset number of sub-voxels, and for any sub-voxel, a candidate eigenvalue of the sub-voxel is obtained, and then the minimum value among the candidate eigenvalues ​​is taken as the target eigenvalue, and a comparison is made based on the target eigenvalue and the judgment threshold. Finally, when the target eigenvalue is greater than or equal to the judgment threshold, the above-mentioned steps of dividing the target voxel into a preset number of new target voxels and subsequent steps are repeated until the target eigenvalue after division is less than the judgment threshold or the number of divisions reaches the set number, the division is stopped, and the result is output, or in response to the target eigenvalue being less than the judgment threshold, the sub-voxel is determined to be a plane.

[0077] It should be noted that there are many methods for dividing the target voxel into a preset number of sub-voxels in the embodiment of the present disclosure, which are not limited here. For example, the target voxel can be divided by an octree segmentation algorithm.

[0078] The number of times is designed in advance and can be changed according to actual design needs. There is no limitation here. By designing the number of times, it is possible to ensure that the target voxel is analyzed as much as possible to determine the plane, and prevent the target voxel from being a non-plane and falling into a calculation loop.

[0079] In the above embodiment, all the planes that are successfully judged are merged to generate a plane map of the target tailings pond. Figure 4 Explaining further, the method includes:

[0080] S401, for any plane, all element points on the plane are combined in pairs to generate multiple edge combinations.

[0081] S402: In response to the existence of two planes having the same edge combination, the two planes are merged.

[0082] In the embodiment of the present disclosure, each plane represents a set, and if there is a common edge between two sets, it means that the two sets can be merged.

[0083] In the embodiment of the present disclosure, merging can be performed by the following operations:

[0084] Find: Find the representative node of the set where a certain node is located. This operation can be used to determine whether two nodes belong to the same set. Union: Merge the sets where two nodes are located into one set. In order to improve efficiency, two optimization strategies are usually adopted for union-find:

[0085] Path compression: In a lookup operation, the immediate parent of a node is set as the root node, thereby reducing the depth of subsequent lookup operations.

[0086] Merge by rank: In the merge operation, trees with smaller rank (height of the tree) are joined to trees with larger rank to avoid the tree becoming too tall.

[0087] The plane merging algorithm is mainly used to process point sets and edge sets on a plane. The following is a detailed description of the algorithm:

[0088] initialization:

[0089] Create a Union-Find instance with the number of elements equal to the number of points in the plane.

[0090] Initialize each point as a separate set, that is, the parent node of each point is itself.

[0091] Edge Scanning:

[0092] Traverse all edges on the plane.

[0093] For each edge, find the representative node of the set of two connected points.

[0094] If the two representative nodes are different, the Union method is called to merge the two collections.

[0095] Query: If you need to query whether two points are in the same set, you can call the Find method to obtain their representative nodes, and then determine whether the two representative nodes are the same.

[0096] Optimization and efficiency: The time complexity of the union-find operation depends on the efficiency of the Find operation. In the worst case, without any optimization, the time complexity of each Find operation may reach O(n), where n is the maximum number of elements in the collection. However, through path compression and rank merging, the amortized time complexity of the Find operation can be reduced to almost O(1).

[0097] In the embodiment of the present disclosure, when performing image fitting, the value corresponding to the third eigenvector of the fitted image may also be calculated. The value corresponding to the third eigenvector is then compared with the judgment threshold, and in response to the value corresponding to the third eigenvector being less than the judgment threshold, it is considered that the fitting is successful, or in response to the value corresponding to the third eigenvector being greater than or equal to the judgment threshold, it is determined that the fitting fails.

[0098] In actual operation, there may be many reasons for fitting failure. For example, the point cloud data may be too scattered to be approximated by a single plane, or the distribution pattern of the point cloud data may be too different from that of the plane.

[0099] Corresponding to the tailings pond water surface map generating methods provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides a tailings pond water surface map generating device. Since the tailings pond water surface map generating device provided in the embodiment of the present disclosure corresponds to the tailings pond water surface map generating methods provided in the above-mentioned embodiments, the implementation methods of the above-mentioned tailings pond water surface map generating methods are also applicable to the tailings pond water surface map generating device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0100] Figure 5 is a schematic diagram of a tailings pond water surface map generating device according to one embodiment of the present disclosure, such as Figure 5 As shown, the tailings pond water surface map generating device 500 includes: an acquisition module 510, an embedding module 520, a calculation module 530, a judgment module 540 and a generation module 550.

[0101] The acquisition module 510 is used to acquire the point cloud data of the target tailings and the pre-configured preset voxel map.

[0102] The embedding module 520 is used to embed the point cloud data into a preset voxel map to generate a target voxel map.

[0103] The calculation module 530 is used to determine the target voxel in the target voxel map and calculate the characteristic value of the target voxel.

[0104] The judgment module 540 is used to judge whether the target voxel is a plane based on the characteristic value.

[0105] The generation module 550 is used to merge all the planes that are successfully judged to generate a plan view of the target tailings pond, and determine the water surface map of the target tailings pond after identifying the plan view of the target tailings pond.

[0106] In one possible implementation, point cloud data is embedded into a preset voxel map to generate a target voxel map, including: for any element point in the point cloud data, dividing the coordinates of the element point by the maximum voxel size to calculate the transformation coordinates of the element point; and establishing a target element point corresponding to the element point in the target voxel map based on the transformation coordinates to generate a target voxel map.

[0107] In a possible implementation manner, determining the target voxel in the target voxel map includes: taking the candidate voxel containing the target element point as the target voxel.

[0108] In one possible implementation, determining whether a target voxel is a plane based on an eigenvalue includes: determining the eigenvalue of the target voxel based on all target element points in the target voxel; taking the minimum value among the candidate eigenvalues ​​as the target eigenvalue, and comparing the target eigenvalue with a judgment threshold; and determining that the target voxel is a plane in response to the target eigenvalue being less than the judgment threshold.

[0109] In one possible implementation, the method also includes: in response to the target eigenvalue being greater than or equal to a judgment threshold, dividing the target voxel into a preset number of sub-voxels; for any sub-voxel, updating the target voxel to a sub-voxel and obtaining a candidate eigenvalue of the sub-voxel; taking the minimum value among the candidate eigenvalues ​​as the target eigenvalue, and comparing the target eigenvalue and the judgment threshold; when the target eigenvalue is greater than or equal to the judgment threshold, subsequently dividing the target voxel into a preset number of new target voxels and subsequent steps until the target eigenvalue after division is less than the judgment threshold or the number of divisions reaches a set number, stopping the division and outputting the result; or, in response to the target eigenvalue being less than the judgment threshold, determining that the sub-voxel is a plane.

[0110] In one possible implementation, all planes that are successfully judged are merged to generate a plan view of the target tailings pond, including: for any plane, all element points on the plane are combined in pairs to generate multiple edge combinations; in response to the existence of two planes with the same edge combinations, the two planes are merged.

[0111] In one possible implementation, calculating the characteristic value of the target voxel includes: calculating the first vector, the second vector and the third vector of the target voxel based on the element point distribution in the target voxel; and calculating the characteristic value of the target voxel based on the first vector, the second vector and the third vector.

[0112] Therefore, by acquiring point cloud data and preset voxel maps, combined with subsequent calculation and judgment processes, automated processing is achieved, manual participation is reduced, and the efficiency of water surface map generation is improved. At the same time, it helps to more accurately identify and depict the surface morphology of the target tailings pond. At the same time, the characteristic value of the target voxel can improve the accuracy of judging whether the target voxel is a plane and the subsequent merging.

[0113] In order to implement the above embodiment, the present disclosure further provides an electronic device 600, Figure 6 is a schematic diagram of an electronic device according to an embodiment of the present disclosure, such as Figure 6 As shown, the electronic device 600 includes: a processor 601 and a memory 602 that is communicatively connected to the processor, the memory 602 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 601 to implement the present disclosure. Figure 1-Figure 4 A method for generating a water surface map of a tailings pond according to an embodiment.

[0114] In order to implement the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the above embodiments. Figure 1-Figure 4 A method for generating a water surface map of a tailings pond according to an embodiment.

[0115] In order to implement the above embodiments, the present disclosure also provides a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the above embodiments. Figure 1-Figure 4 A method for generating a water surface map of a tailings pond according to an embodiment.

[0116] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0117] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0118] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0119] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0120] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0121] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that contains, stores, communicates, propagates or transmits a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0122] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0123] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0124] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0125] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for generating a tailings pond water surface map, characterized in that: include: Obtain point cloud data and pre-configured preset voxel maps of target tailings; Embedding the point cloud data into the preset voxel map to generate a target voxel map; Determining a target voxel in the target voxel map, and calculating a characteristic value of the target voxel; Determining whether the target voxel is a plane based on the eigenvalue; All planes that are successfully judged are merged to generate a plan view of the target tailings pond, and the water surface map of the target tailings pond is determined after the plan view of the target tailings pond is identified.

2. The method according to claim 1, characterized in that The step of embedding the point cloud data into the preset voxel map to generate a target voxel map includes: For any element point in the point cloud data, dividing the coordinates of the element point by the maximum voxel size to calculate and obtain the transformed coordinates of the element point; A target element point corresponding to the element point is established in the target voxel map based on the converted coordinates to generate a target voxel map.

3. The method according to claim 2, characterized in that The determining of the target voxel in the target voxel map comprises: The candidate voxel containing the target element point is used as the target voxel.

4. The method according to any one of claims 1 to 3, characterized in that The determining whether the target voxel is a plane based on the characteristic value includes: Determine a characteristic value of the target voxel based on all target element points in the target voxel; Taking the minimum value among the candidate feature values ​​as the target feature value, and comparing the target feature value with the judgment threshold; In response to the target feature value being smaller than the judgment threshold, the target voxel is determined to be a plane.

5. The method according to claim 4, characterized in that The method further comprises: In response to the target feature value being greater than or equal to the judgment threshold, dividing the target voxel into a preset number of sub-voxels; For any sub-voxel, updating the target voxel to the sub-voxel, and obtaining a candidate feature value of the sub-voxel; Taking the minimum value among the candidate feature values ​​as the target feature value, and comparing the target feature value with the judgment threshold; In the case where the target feature value is greater than or equal to the judgment threshold, the target voxel is subsequently divided into a preset number of new target voxels and the subsequent steps are performed until the target feature value of the sub-voxel after division is less than the judgment threshold or the number of divisions reaches the set number, the division is stopped, and the result is output; or In response to the target feature value being smaller than the judgment threshold, the sub-voxel is determined to be a plane.

6. The method according to claim 1, characterized in that The step of merging all successfully judged planes to generate a plane diagram of the target tailings pond includes: For any plane, all element points on the plane are combined in pairs to generate multiple edge combinations; In response to the existence of the same edge combination of two planes, the two planes are merged.

7. The method according to claim 1, characterized in that The calculating the characteristic value of the target voxel comprises: Calculate a first vector, a second vector, and a third vector of the target voxel based on the element point distribution in the target voxel; A feature value of the target voxel is calculated based on the first vector, the second vector, and the third vector.

8. A tailings pond water surface map generating device, characterized in that: include: An acquisition module, used to acquire point cloud data of the target tailings and a pre-configured preset voxel map; An embedding module, used for embedding the point cloud data into the preset voxel map to generate a target voxel map; A calculation module, used for determining a target voxel in the target voxel map and calculating a characteristic value of the target voxel; A judging module, configured to judge whether the target voxel is a plane based on the characteristic value; A generation module is used to merge all planes that have been successfully judged to generate a plan view of the target tailings pond, and determine a water surface map of the target tailings pond after identifying the plan view of the target tailings pond.

9. An electronic device, characterized in that: Including memory. processor; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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