Unit division method and device based on grid model, equipment, medium and product

Abstract

The invention discloses a unit division method and device based on a grid model, equipment, a medium and a product, and the method comprises the steps: obtaining point cloud data of a target region, and carrying out the preprocessing of the point cloud data; generating a mesh grid model of the target area according to the processed point cloud data; performing plane segmentation on the mesh grid model by adopting a segmentation algorithm based on annular neighborhood flatness to obtain a slope division result of the mesh grid model; and carrying out vector conversion on a slope division result to obtain a slope unit of the target area. A mountain grid model can be constructed based on point cloud data, plane segmentation is performed on the grid model which retains geometric mountain ridge and ridge features of a mountain by adopting a segmentation algorithm based on annular neighborhood flatness, automatic slope unit division based on a landslide grid model is realized, the problem of DEM distortion is solved, manual parameter setting is not needed, and the construction efficiency is improved. And the slope unit division precision is improved.

Description

Technical Field

[0001] The present invention relates to the field of disaster identification technology, and in particular to a unit division method, device, equipment, medium and product based on a grid model. Background Art

[0002] Landslides are characterized by their concealment, wide distribution, sudden occurrence, and clustering. They have caused serious casualties and enormous economic losses worldwide, making them one of the most severe geological disasters in the world. Using existing landslide information to predict and analyze the susceptibility and potential hazards of landslides is key to developing disaster prevention measures and reducing risks. Landslide hazard analysis typically involves three steps: ① dividing evaluation units; ② extracting evaluation factors; and ③ selecting an evaluation model. The divided evaluation unit is the basic unit for extracting landslide condition factors and serves as the initial sample for model evaluation. The slope unit, based on the slope topography, divides the mountain into different slope surfaces. It can represent the local topographic characteristics of landslides and has practical significance. It is a key area for future regional landslide sensitivity research.

[0003] Currently, the most widely used slope unit division method is the hydrological analysis method. This method defines a slope unit as an area enclosed by a watershed line and a watershed confluence line. Valley lines and ridge lines can be obtained by performing a watershed transformation on the forward and reverse digital elevation models (DEMs). The slope surface can be divided by superimposing the two. This type of method has the advantages of being easy to implement and highly efficient, but it will change the local DEM elevation value, causing terrain changes, and requires manual setting of flow thresholds, which can lead to the appearance of unreasonable narrow and long surfaces and uneven slopes, thus requiring a lot of manual correction work. Summary of the Invention

[0004] The present invention provides a unit division method, device, equipment, medium and product based on a grid model. The method constructs a mountain grid model based on point cloud data, and adopts a segmentation algorithm based on the flatness of annular neighborhood to perform planar segmentation on the grid model that retains the geometric mountain ridges and ridge features of the mountain. The method realizes automatic slope unit division based on the landslide grid model, solves the DEM model distortion problem, and does not require manual parameter setting, thereby improving the accuracy of slope unit division and thus improving the accuracy of landslide hazard analysis.

[0005] To achieve the above objectives, an embodiment of the present invention provides a cell division method based on a grid model, comprising:

[0006] Acquire point cloud data of the target area and preprocess the point cloud data; generate a mesh model of the target area based on the processed point cloud data;

[0007] Performing plane segmentation on the mesh model using a segmentation algorithm based on the planarity of annular neighborhood to obtain a slope segmentation result of the mesh model;

[0008] Performing vector conversion on the slope division result to obtain slope units of the target area.

[0009] As an improvement to the above solution, the mesh model is plane-segmented using a segmentation algorithm based on the flatness of a ring neighborhood to obtain a slope segmentation result of the mesh model, including:

[0010] S21, calculating the annular flatness of each vertex according to the annular neighborhood of each vertex of the mesh model;

[0011] S22, calculating the average flatness of the vertices in each triangular facet according to all triangular faces and all annular flatnesses of the mesh model to obtain the flatness of each triangular facet;

[0012] S23, traversing all unsegmented triangular faces in the mesh model, using the triangular face with the highest planarity as the target plane of the current plane region, and determining the current reference plane of the current plane region based on the annular neighboring faces of the target plane;

[0013] S24, recording vertices whose distances from the current reference plane are less than a preset distance threshold as target vertices, and segmenting the triangles corresponding to all target vertices into the current plane region to obtain a new current plane region;

[0014] S25, determine the current reference plane of the new current plane area based on all the vertices of the new current plane area, and return to step S24 until there are no more triangular faces that need to be segmented into the new current plane area; return to step S23 until there are no unsegmented triangular faces in the mesh model, and obtain the slope segmentation result of the mesh model.

[0015] As an improvement to the above solution, the calculation of the annular flatness of each vertex according to the annular neighborhood of each vertex of the mesh model includes:

[0016] Calculating the covariance matrix of each vertex based on all points in the annular neighborhood of each vertex of the mesh model;

[0017] Calculate the eigenvalue of each vertex according to the covariance matrix;

[0018] The annular planarity of each vertex is calculated based on the eigenvalues.

[0019] As an improvement to the above solution, determining the current reference plane of the current plane area based on the annular neighborhood surface of the target plane includes:

[0020] According to the annular neighborhood surface of the target plane, obtaining all vertex coordinates of the target plane and the corresponding annular neighborhood surface;

[0021] The principal component analysis method is used to analyze the coordinates of all vertices to determine the current reference plane of the current plane area.

[0022] As an improvement to the above solution, the preset distance threshold is the average length of all edges in the mesh model.

[0023] As an improvement to the above solution, generating a mesh model of the target area based on the processed point cloud data includes:

[0024] Using a Poisson reconstruction algorithm, a surface function of the target area is constructed based on the processed point cloud data;

[0025] Constructing discrete points in the processed point cloud data into triangular surfaces based on the surface information obtained by solving the surface function;

[0026] Generate a mesh model of the target area based on all triangular faces.

[0027] To achieve the above objectives, an embodiment of the present invention provides a unit division device based on a grid model, comprising:

[0028] A mesh model generation module is used to obtain point cloud data of the target area and pre-process the point cloud data; generate a mesh model of the target area based on the processed point cloud data;

[0029] A slope result division module is used to perform plane segmentation on the mesh model using a segmentation algorithm based on the planarity of a ring neighborhood to obtain a slope division result of the mesh model;

[0030] The slope unit acquisition module is used to perform vector conversion on the slope division result to obtain the slope units of the target area.

[0031] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a unit division device based on a grid model, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the above-mentioned unit division method based on the grid model when executing the computer program.

[0032] In order to achieve the above-mentioned purpose, an embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned grid model-based unit division method.

[0033] To achieve the above objectives, an embodiment of the present invention further provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the steps of the above-mentioned unit division method based on the grid model.

[0034] Compared with the prior art, the present invention discloses a unit division method, device, equipment, medium, and product based on a grid model. The method obtains point cloud data of a target area and pre-processes the point cloud data; generates a mesh model of the target area based on the processed point cloud data; performs plane segmentation on the mesh model using a segmentation algorithm based on annular neighborhood flatness to obtain a slope division result of the mesh model; and performs vector conversion on the slope division result to obtain slope units of the target area. The method can construct a mountain grid model based on point cloud data, and perform plane segmentation on the grid model that retains the geometric mountain and ridge features of the mountain using a segmentation algorithm based on annular neighborhood flatness, thereby achieving automated slope unit division based on the landslide grid model, resolving the DEM model distortion problem without the need for manual parameter setting, improving the accuracy of slope unit division, and thus improving the accuracy of landslide hazard analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of a cell division method based on a grid model provided by an embodiment of the present invention;

[0036] Figure 2 1 is a schematic structural diagram of a unit division device based on a grid model provided by an embodiment of the present invention;

[0037] Figure 3 This is a structural block diagram of a unit division device based on a grid model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0039] It should be noted that the terms "comprises" and "specifically" and any variations thereof in the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or are inherent to these processes, methods, products or apparatuses.

[0040] See also Figure 1 , Figure 1 1 is a flow chart of a cell division method based on a grid model provided by an embodiment of the present invention. The cell division method based on a grid model includes:

[0041] S1, obtaining point cloud data of a target area and preprocessing the point cloud data; generating a mesh model of the target area based on the processed point cloud data;

[0042] S2, performing plane segmentation on the mesh model using a segmentation algorithm based on the planarity of a ring neighborhood to obtain a slope segmentation result of the mesh model;

[0043] S3, performing vector conversion on the slope division result to obtain slope units of the target area.

[0044] Exemplarily, the unit division method based on the grid model described in the embodiment of the present invention is implemented by a slope unit division server, and the slope unit division server can exchange information with the target user. The slope unit division server obtains point cloud data of the target area through a laser radar (Light Detection And Ranging, LIDAR), performs point cloud solution, point cloud data quality inspection, point cloud data filtering and other preprocessing on the point cloud data, and uses a surface reconstruction algorithm (such as a Poisson reconstruction algorithm) to generate a mesh grid model of the target area from the processed point cloud data; uses a segmentation algorithm based on the flatness of annular neighborhood to perform plane segmentation on the mesh grid model to obtain a slope division result of the mesh grid model, and performs vector conversion on the slope division result to obtain the slope unit of the target area. For example, using the Arcgis secondary development platform and configuring the Arcpy3.0 library, the Mesh model and the slope division result are converted into ShapeFile surface elements to obtain slope units. It can construct a mountain grid model based on point cloud data, and use a segmentation algorithm based on the flatness of annular neighborhood to perform plane segmentation on the grid model that retains the geometric mountain and ridge characteristics of the mountain, thereby realizing automatic slope unit division based on the landslide grid model, solving the DEM model distortion problem, and eliminating the need to manually set parameters, avoiding unreasonable narrow and long surfaces and uneven slopes, reducing manual correction work, and improving the accuracy of slope unit division, thereby improving the accuracy of landslide hazard analysis.

[0045] It's worth noting that the segmentation algorithm based on annular neighborhood planarity is used to partition complex geometric models (such as mountain meshes) into multiple regions with uniform slope and aspect properties. It is primarily used for slope unit segmentation in landslide hazard analysis. The algorithm works by iteratively growing and segmenting all triangular faces in the mesh model based on the annular planarity of each vertex until the mesh model is segmented into multiple planar regions.

[0046] Specifically, the step S2 includes:

[0047] S21, calculating the annular flatness of each vertex according to the annular neighborhood of each vertex of the mesh model;

[0048] S22, calculating the average flatness of the vertices in each triangular facet according to all triangular faces and all annular flatnesses of the mesh model to obtain the flatness of each triangular facet;

[0049] S23, traversing all unsegmented triangular faces in the mesh model, using the triangular face with the highest planarity as the target plane of the current plane region, and determining the current reference plane of the current plane region based on the annular neighboring faces of the target plane;

[0050] S24, recording vertices whose distances from the current reference plane are less than a preset distance threshold as target vertices, and segmenting the triangles corresponding to all target vertices into the current plane region to obtain a new current plane region;

[0051] S25, determine the current reference plane of the new current plane area based on all the vertices of the new current plane area, and return to step S24 until there are no more triangular faces that need to be segmented into the new current plane area; return to step S23 until there are no unsegmented triangular faces in the mesh model, and obtain the slope segmentation result of the mesh model.

[0052] For example, in step S21, assuming that there are vertices A, B, C, etc. in the mesh model, take vertex A as an example, assume that the annular neighborhood is a k-ring neighborhood, k=3 (i.e., considering its three-ring neighborhood), and determine all vertices within the three-ring neighborhood of vertex A. Fit the plane of vertex A based on all vertices. Perform principal component analysis (PCA) on all vertices, calculate the covariance matrix of vertex A, and solve the eigenvalues ​​λ1, λ2, and λ3 of the matrix. According to the annular flatness formula Calculate the three-ring flatness (annular flatness) of vertex A, where Planarity kis the k-ring flatness of the fitted plane (e.g., the three-ring flatness of vertex A), λ1, λ2, and λ3 are the eigenvalues ​​of the covariance matrix of the fitted plane, respectively, where λ3 is the smallest eigenvalue; the flatness value range is 0-1, and the closer it is to 1, the better the fitting effect, that is, the flatter the surface. Following the same method, the annular flatness of all other vertices in the mesh model is calculated in turn. It can be understood that the k-ring neighborhood of a vertex or face is the k rings around it, where each ring is composed of all faces adjacent to the vertex. The k-ring flatness is a measure of the flatness of the plane fitted by all points in the k-ring neighborhood.

[0053] In step S22, for example, there is a triangle ABC in the mesh model, and the annular flatness of vertices A, B, and C is P respectively. A 、P B 、P C , calculate the flatness P of the triangle ABC ABC , Repeat the above calculation for all triangles in the mesh model to obtain the flatness of each triangle.

[0054] In step S23, all unsegmented triangular faces in the mesh model are traversed. Assuming that triangular face DEF has the highest planarity, triangular face DEF is used as the target plane of the current planar region. Its annular neighboring faces (e.g., k-ring neighboring faces) are determined. All vertices of target plane DEF and its annular neighboring faces are collected and principal component analysis is performed on these vertices to determine the current reference plane of the current planar region. This current reference plane can be represented by the plane equation ax+by+cz+d=0.

[0055] In step S24, a distance threshold is preset, for example, obtained by calculating the average length of all edges in the mesh model, and the distance from all vertices in the mesh model to the current reference plane is calculated. The vertices whose distance from the current reference plane is less than the distance threshold are recorded as target vertices, and the triangular faces corresponding to all target vertices are found. These triangular faces are divided into the current plane area to form a new current plane area.

[0056] In step S25, principal component analysis is performed again based on all the vertices of the new current plane area to determine the current reference plane of the new current plane area, and the process returns to step S24 to repeatedly calculate the distance from the vertex to the new reference plane, and continues to segment the qualified triangles into the current plane area until there are no more triangles that need to be segmented into the new current plane area, and then returns to step S23 to find the triangle with the highest flatness among the remaining unsegmented triangles, and start a new plane area segmentation process until there are no unsegmented triangles in the mesh model, and finally obtain the slope division result of the mesh model.

[0057] The slope units divided by the embodiment of the present invention have more regular shapes and more uniform sizes, and can basically divide different slopes according to the slope lines of the actual surface, and can also separately divide the flat areas in the valleys.

[0058] More specifically, step S21 includes:

[0059] S211, calculating the covariance matrix of each vertex according to all points in the annular neighborhood of each vertex of the mesh model;

[0060] S212, calculating the eigenvalue of each vertex according to the covariance matrix;

[0061] S213: Calculate the annular planarity of each vertex according to the eigenvalue.

[0062] For example, taking vertex A as an example, all vertices in the k-ring neighborhood of vertex A are collected, and the coordinates of these points form a point set P = {p1, p2, ..., p n}, where n is the number of vertices in the k-ring neighborhood, calculate the centroid p0 of the point set P, and then calculate the covariance matrix. Solve the characteristic equation of the covariance matrix to obtain the eigenvalues ​​λ1, λ2, and λ3 of the covariance matrix. According to the eigenvalues, calculate the three-ring flatness (annular flatness) of vertex A through the annular flatness formula. Repeat the above calculation process to obtain the annular flatness of each vertex of the mesh model.

[0063] More specifically, in step S23, determining the current reference plane of the current plane area according to the annular neighborhood surface of the target plane includes:

[0064] S231, according to the annular neighborhood surface of the target plane, obtaining all vertex coordinates of the target plane and the corresponding annular neighborhood surface;

[0065] S232: Analyze all vertex coordinates using principal component analysis to determine a current reference plane of the current plane area.

[0066] For example, the triangular plane DEF is used as the target plane of the current plane area, and its annular neighboring plane (for example, a k-ring neighboring plane) is determined. All vertices of the target plane DEF and its annular neighboring plane are collected, and principal component analysis is performed on these vertices. The covariance matrix of these vertices is calculated, and the eigenvalues ​​and corresponding eigenvectors of the matrix are solved. The eigenvector v1 corresponding to the largest eigenvalue λ1 represents the direction in which the data changes the most, and the eigenvector v2 corresponding to the second largest eigenvalue λ2 represents the direction in which the data changes the most in the direction perpendicular to v1. The plane spanned by the eigenvectors v1 and v2 is the current reference plane. For example, in three-dimensional space, the plane equation can generally be expressed in the form of ax+by+cz+d=0. The coefficients a, b, and c in the plane equation can be determined through the coordinate values ​​of the eigenvectors v1 and v2. Then, combined with the average value point of all vertex coordinates, the constant term d can be determined, thereby determining the specific equation of the current reference plane and completing the determination of the current reference plane.

[0067] Specifically, the preset distance threshold is the average length of all edges in the mesh model.

[0068] Specifically, generating a mesh model of the target area based on the processed point cloud data includes:

[0069] Using a Poisson reconstruction algorithm, a surface function of the target area is constructed based on the processed point cloud data;

[0070] Constructing discrete points in the processed point cloud data into triangular surfaces based on the surface information obtained by solving the surface function;

[0071] Generate a mesh model of the target area based on all triangular faces.

[0072] Exemplarily, a laser radar device is used to scan the target area to obtain point cloud data for the area. These point cloud data contain discrete point information on the surfaces of various objects in the area, and each point has a corresponding three-dimensional coordinate (x, y, z). The raw data collected by the laser radar is solved to obtain accurate three-dimensional coordinate information, check the integrity and accuracy of the point cloud data, and eliminate some erroneous or abnormal points, such as outliers caused by abnormal laser reflection, whose coordinates may deviate too much from the surrounding points. Remove noise points and smooth data. For example, a statistical filtering method is used to calculate the distance from each point to its neighboring points, and points with a distance exceeding a certain threshold are regarded as noise points and removed. At the same time, the data is downsampled to reduce the amount of data and improve the efficiency of subsequent processing. The Poisson reconstruction algorithm regards the point cloud data as a vector field in three-dimensional space, and obtains an implicit surface function by solving the Poisson equation. The formula of the Poisson equation is Where Δ is the Laplace operator, is the divergence operator, Ω represents a bounded domain in xyz space. Here, it is assumed that the input data is normalized and mapped to the range of 0-1, so Ω=[0,1] 3 , χ represents the implicit function of the surface. The preprocessed point cloud data is input into the Poisson reconstruction algorithm, and a vector field is constructed based on the normal vector information of the point cloud data. The normal vector represents the direction of the point cloud data surface. The Poisson equation is solved numerically to obtain an implicit surface function. This function describes the terrain surface of the target area. The actual surface is extracted from the implicit surface function, that is, triangular patches are generated to approximate this surface. Based on the solved surface information, the discrete points in the point cloud data are connected into triangular faces. These triangular faces are connected to form a continuous surface, resulting in a mesh model.

[0073] The present invention discloses a unit division method based on a grid model. The method obtains point cloud data of a target area and preprocesses the point cloud data; generates a mesh model of the target area based on the processed point cloud data; performs planar segmentation on the mesh model using a segmentation algorithm based on annular neighborhood flatness to obtain slope division results for the mesh model; and performs vector conversion on the slope division results to obtain slope units of the target area. The method can construct a mountain grid model based on point cloud data, and perform planar segmentation on the grid model that retains the geometric features of the mountain's ridges and hills using a segmentation algorithm based on annular neighborhood flatness. This method implements automated slope unit division based on the landslide grid model, resolves DEM model distortion issues, and eliminates the need for manual parameter setting. The method improves the accuracy of slope unit division, thereby enhancing the accuracy of landslide hazard analysis.

[0074] See also Figure 2 , Figure 2 1 is a schematic structural diagram of a unit division device 10 based on a grid model provided by an embodiment of the present invention. The unit division device 10 based on a grid model includes:

[0075] The mesh model generation module 11 is used to obtain point cloud data of the target area and pre-process the point cloud data; generate a mesh model of the target area based on the processed point cloud data;

[0076] A slope result division module 12 is configured to perform plane segmentation on the mesh model using a segmentation algorithm based on the planarity of a ring neighborhood to obtain a slope division result of the mesh model;

[0077] The slope unit acquisition module 13 is configured to perform vector conversion on the slope division result to obtain the slope units of the target area.

[0078] Specifically, the slope result division module 12 includes:

[0079] a point flatness calculation unit, configured to calculate the annular flatness of each vertex of the mesh model based on the annular neighborhood of each vertex;

[0080] a face flatness calculation unit, configured to calculate the average flatness of the vertices in each triangular face according to the flatness of all triangular faces and all annular faces of the mesh model, thereby obtaining the flatness of each triangular face;

[0081] a reference plane determining unit, configured to traverse all unsegmented triangular faces in the mesh model, adopt the triangular face with the highest planarity as the target plane of the current plane region, and determine the current reference plane of the current plane region based on the annular neighboring faces of the target plane;

[0082] a target vertex determination unit, configured to record vertices whose distance from the current reference plane is less than a preset distance threshold as target vertices, and segment the triangles corresponding to all target vertices into the current plane region to obtain a new current plane region;

[0083] The slope result division unit is used to determine the current reference plane of the new current plane area based on all the vertices of the new current plane area, and return to the target vertex determination unit until there are no more triangular faces that need to be divided into the new current plane area; return to the reference plane determination unit until there are no undivided triangular faces in the mesh model, and obtain the slope division result of the mesh model.

[0084] A unit division device 10 based on a grid model provided in an embodiment of the present invention can implement all processes of the unit division method based on a grid model in the above-mentioned embodiment. The functions of each module in the device and the technical effects achieved are respectively the same as the functions and technical effects achieved by the unit division method based on a grid model in the above-mentioned embodiment, and will not be repeated here.

[0085] See also Figure 3 , Figure 3 2 is a schematic structural diagram of a grid-based unit division device 20 provided in an embodiment of the present invention. The grid-based unit division device 20 of this embodiment includes: a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, the steps of the aforementioned grid-based unit division method embodiment are implemented. Alternatively, when the processor 21 executes the computer program, the functions of the various modules of the aforementioned grid-based unit division device embodiment are implemented.

[0086] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the grid-based unit partitioning device 20.

[0087] The grid-based unit partitioning device 20 can be a computing device such as a desktop computer, a laptop, a PDA, or a cloud server. The grid-based unit partitioning device 20 can include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will appreciate that the schematic diagram is merely an example of the grid-based unit partitioning device 20 and does not limit the grid-based unit partitioning device 20. The grid-based unit partitioning device 20 can include more or fewer components than shown, or a combination of certain components, or different components. For example, the grid-based unit partitioning device 20 can also include input and output devices, network access devices, buses, and the like.

[0088] The processor 21 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. The processor 21 is the control center of the grid-based unit partitioning device 20, and connects various parts of the entire grid-based unit partitioning device 20 using various interfaces and lines.

[0089] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements the various functions of the grid model-based unit partitioning device 20 by running or executing the computer programs and / or modules stored in the memory 22 and accessing the data stored in the memory 22. The memory 22 can mainly include a program storage area and a data storage area. The program storage area can store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory 22 can include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0090] Wherein, if the module integrated in the grid model-based unit division device 20 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 21, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0091] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0092] An embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the unit division method based on the grid model as described in the above embodiment.

[0093] In addition, an embodiment of the present invention further provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the steps of the grid model-based unit partitioning method of the above embodiment.

[0094] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A unit division method based on a grid model, characterized in that: include: Acquire point cloud data of the target area and preprocess the point cloud data; Generate a mesh model of the target area according to the processed point cloud data; Performing plane segmentation on the mesh model using a segmentation algorithm based on the planarity of annular neighborhood to obtain a slope segmentation result of the mesh model; Performing vector conversion on the slope division result to obtain slope units of the target area.

2. The unit division method based on the grid model according to claim 1, characterized in that: The mesh model is plane segmented using a segmentation algorithm based on the planarity of annular neighborhood to obtain a slope segmentation result of the mesh model, including: S21, calculating the annular flatness of each vertex according to the annular neighborhood of each vertex of the mesh model; S22, calculating the average flatness of the vertices in each triangular facet according to all triangular faces and all annular flatnesses of the mesh model to obtain the flatness of each triangular facet; S23, traversing all unsegmented triangular faces in the mesh model, using the triangular face with the highest planarity as the target plane of the current plane region, and determining the current reference plane of the current plane region based on the annular neighboring faces of the target plane; S24, recording vertices whose distances from the current reference plane are less than a preset distance threshold as target vertices, and segmenting the triangles corresponding to all target vertices into the current plane region to obtain a new current plane region; S25, determine the current reference plane of the new current plane area based on all the vertices of the new current plane area, and return to step S24 until there are no more triangular faces that need to be segmented into the new current plane area; return to step S23 until there are no unsegmented triangular faces in the mesh model, and obtain the slope segmentation result of the mesh model.

3. The unit division method based on the grid model according to claim 2, characterized in that: The calculating the annular flatness of each vertex according to the annular neighborhood of each vertex of the mesh model includes: Calculating the covariance matrix of each vertex based on all points in the annular neighborhood of each vertex of the mesh model; Calculate the eigenvalue of each vertex according to the covariance matrix; The annular planarity of each vertex is calculated based on the eigenvalues.

4. The unit division method based on the grid model according to claim 2, characterized in that: The determining, based on the annular neighborhood surface of the target plane, the current reference plane of the current plane area includes: According to the annular neighborhood surface of the target plane, obtaining all vertex coordinates of the target plane and the corresponding annular neighborhood surface; The principal component analysis method is used to analyze the coordinates of all vertices to determine the current reference plane of the current plane area.

5. The unit division method based on the grid model according to claim 2, characterized in that: The preset distance threshold is the average length of all edges in the mesh model.

6. The unit division method based on the grid model according to claim 1, characterized in that: Generating a mesh model of the target area according to the processed point cloud data includes: Using a Poisson reconstruction algorithm, a surface function of the target area is constructed based on the processed point cloud data; Constructing discrete points in the processed point cloud data into triangular surfaces based on the surface information obtained by solving the surface function; Generate a mesh model of the target area based on all triangular faces.

7. A unit division device based on a grid model, characterized in that: include: A grid model generation module is used to obtain point cloud data of the target area and pre-process the point cloud data; Generate a mesh model of the target area according to the processed point cloud data; A slope result division module is used to perform plane segmentation on the mesh model using a segmentation algorithm based on the planarity of a ring neighborhood to obtain a slope division result of the mesh model; The slope unit acquisition module is used to perform vector conversion on the slope division result to obtain the slope units of the target area.

8. A unit division device based on a grid model, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the unit partitioning method based on the grid model as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the unit division method based on the grid model according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the cell partitioning method based on a grid model according to any one of claims 1 to 6.

Images