A low-altitude intelligent connection device coverage range analysis method based on holographic grid and GPGPU

Abstract

This disclosure provides a method for analyzing the coverage range of low-altitude intelligent connected devices based on holographic meshes and GPGPUs, applicable to the field of low-altitude safety protection technology. The method includes: determining the target detection area based on the detection angle and maximum detection distance of the low-altitude intelligent connected device; dividing the target detection area into a three-dimensional mesh based on holographic mesh coding rules to generate a working space mesh system; performing mesh coding processing on multi-source ground object data within the target detection area based on this system to generate a comprehensive obstacle mesh set, and combining this with the device signal transmission parameters to generate an ideal coverage mesh; constructing a detection ray with the device location as the starting point and the ideal mesh unit as the ending point; using GPGPU to perform parallel occlusion detection to obtain the actual coverage mesh; and finally parsing the mesh code into geographic coordinates to output the actual signal coverage range of the device. In this way, high-precision and high-efficiency calculation and evaluation of the coverage range of low-altitude detection devices can be achieved.

Description

Technical Field

[0001] This disclosure relates to the field of low-altitude safety protection technology, and in particular to a method for analyzing the coverage of low-altitude intelligent connected devices based on holographic mesh and GPGPU. Background Technology

[0002] With the popularization of drone technology and the increase in low-altitude activities, low-altitude security protection has become a core requirement for ensuring public safety and maintaining social order. In scenarios such as security for major events, protection of confidential facilities, control of airport airspace, and protection of petrochemical bases, it is necessary to accurately grasp the actual coverage of low-altitude detection equipment in order to eliminate security blind spots and avoid protection dead ends.

[0003] However, existing methods for analyzing the coverage of low-altitude detection equipment have many limitations and cannot meet the needs of practical applications: Traditional two-dimensional data analysis methods simplify complex low-altitude spaces into planar models, ignoring the impact of vertical ground cover obstruction on signal propagation, failing to reflect the signal distribution characteristics of three-dimensional space, and easily leading to equipment deployment deviations; while three-dimensional modeling methods based on triangular meshes can restore the position of three-dimensional obstacles, they require processing massive amounts of triangular facets, resulting in an exponential increase in computational load, and have poor compatibility with non-triangular facet geographic data, making it difficult to balance accuracy and efficiency; the "shadow mapping" technique in computer graphics can only achieve visualization rendering of the visible range, and cannot accurately calculate the signal propagation boundary of the entire three-dimensional space, resulting in incomplete and impractical results; although traditional voxel mesh analysis methods have lower computational load, the number of voxels increases exponentially in fine-grained analysis scenarios, resulting in data redundancy, high storage pressure, and difficulty in accurately mapping the calculation results to real geographic coordinates, greatly reducing practicality.

[0004] Against this backdrop, the industry urgently needs a solution for analyzing the coverage of low-altitude detection equipment that can achieve multi-source ground object data fusion, reduce storage pressure, improve computing efficiency, and accurately match geographic coordinates. Summary of the Invention

[0005] This disclosure provides a method for analyzing the coverage of low-altitude intelligent connected devices based on holographic meshes and GPGPU, which solves the technical problem that existing low-altitude detection coverage map generation technologies struggle to balance modeling accuracy and computational efficiency.

[0006] According to a first aspect of this disclosure, a method for analyzing the coverage range of low-altitude intelligent connected devices based on holographic meshes and GPGPU is provided. The method includes: The target detection area is determined based on the detection angle and maximum detection distance of low-altitude intelligent connected devices. Based on predefined holographic grid coding rules, the target detection area is divided into three-dimensional grids to generate a workspace grid system. Based on the workspace grid system, the multi-source ground object data within the target detection area are subjected to grid coding processing to generate a comprehensive obstacle grid set; Based on the signal transmission parameters of the low-altitude intelligent connected device, an ideal coverage grid set is generated in the workspace grid system; Starting from the location of the low-altitude intelligent connected device and ending at each grid cell in the ideal signal coverage grid set, multiple detection rays are constructed in the workspace grid system; Using a GPGPU, occlusion detection is performed on each probe ray in relation to the integrated obstacle mesh set to generate an actual signal coverage mesh set; The encoding of each grid cell in the actual signal coverage grid set is parsed into geographic coordinates to generate and output the actual signal coverage range of the low-altitude intelligent connected device.

[0007] As described above and in any possible implementation, a further implementation is provided, wherein the step of dividing the target detection area into a three-dimensional mesh based on predefined holographic mesh coding rules to generate a workspace mesh system includes: Based on the business assessment accuracy requirements of the low-altitude intelligent connected equipment, the encoding level and corresponding three-dimensional grid size of the holographic grid are determined. Based on the three-dimensional grid size and the predefined holographic grid coding rules, the target detection area is divided into three-dimensional discretization, and each grid unit is assigned a globally unique three-dimensional row and column code to form an initial dedicated working area grid set. Using the geometric boundary of the target detection area as a reference, the dedicated workspace grid set is subjected to coordinate transformation, a local row and column number for local fast indexing is generated for each grid cell, and a mapping relationship is established between the local row and column number and the globally unique three-dimensional row and column code, finally generating a workspace grid system with local indexing.

[0008] As described above and in any possible implementation, a further implementation is provided, wherein the step of performing grid coding processing on multi-source ground feature data within the target detection area based on the workspace grid system to generate a comprehensive obstacle grid set includes: Acquire multi-source feature data within the target detection area, wherein the multi-source feature data includes at least one of terrain, buildings, and vegetation; For each type of land feature data, spatial intersection analysis is performed on each land feature entity and the grid cells in the workspace grid system. All grid cells that have spatial intersection with the land feature entity are marked as the gridded representation of the land feature entity, thus obtaining a single obstacle grid subset corresponding to the land feature data. For each type of terrain feature data, the spatial integrity of the single obstacle grid subset is verified to ensure that it can completely represent the spatial outline of that type of terrain feature within the target detection area. Merge all validated subsets of individual obstacle meshes and perform deduplication based on the local row and column numbers of the mesh cells to generate a unified set of comprehensive obstacle meshes containing all obstacle information.

[0009] As described above and in any possible implementation, a further implementation is provided, wherein generating an ideal coverage grid set based on the signal transmission parameters of the low-altitude intelligent connected device within the workspace grid system includes: Using the geometric center of the low-altitude intelligent connected device as the signal transmission origin, and combining the signal transmission form and core transmission parameters, a coverage area shell under an ideal unobstructed scenario is generated through spatial geometric modeling operations; wherein, the coverage area shell is constructed in the form of a triangular mesh. Based on the aforementioned workspace grid system, the coverage shell in the form of a triangular mesh is discretized, and grid cells that spatially intersect with the triangular mesh are selected. These grid cells are then integrated into a grid set to obtain the ideal coverage grid set; wherein... The row and column numbers of each grid cell in the ideal coverage grid set are represented using a compact numeric type.

[0010] As described above and in any possible implementation, a further implementation is provided in which the step of using a GPGPU to perform occlusion detection on each probe ray with respect to the integrated obstacle mesh set to generate an actual signal coverage mesh set includes: The task of detecting the occlusion of the multiple detection rays is assigned to multiple parallel computing cores of the GPGPU to form a parallel computing task queue. For each detection ray, it is controlled to move step by step from the starting grid cell to the ending grid cell in the workspace grid system; and during each step, it is determined whether the current grid cell exists in the integrated obstacle grid set. If it exists, the ray is determined to be blocked, the current grid cell is recorded as the blocking point, and further detection of the ray is terminated; If it does not exist, continue stepping towards the endpoint until the endpoint grid cell is reached, then it is determined that the ray is not blocked; The detection results of all probe rays are collected, and the endpoint grid cells corresponding to all rays that are determined to be unblocked are integrated to form the actual signal coverage grid set.

[0011] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein parsing the encoding of each grid cell in the actual signal coverage grid set into geographic coordinates, and generating and outputting the actual signal coverage range of the low-altitude intelligent connected device includes: Read the globally unique three-dimensional row and column code of each grid cell in the actual signal coverage grid set; Based on the decoding algorithm corresponding to the predefined holographic grid encoding rules, each of the globally unique three-dimensional row and column codes is converted into corresponding geographic coordinates in batches; Based on the set of all geographical coordinate points obtained by the transformation, the actual signal coverage range of the low-altitude intelligent connected device is generated and output through spatial reconstruction.

[0012] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the actual signal coverage range is represented in the following ways: At least one of the following: a two-dimensional planar thematic map of coverage, a three-dimensional spatial model of coverage, a statistical report of coverage area, and a map of blind spots.

[0013] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes: Spatial overlay analysis of the actual signal coverage of multiple low-altitude intelligent connected devices is performed to generate joint coverage range, coverage overlap area and remaining blind zone analysis results.

[0014] According to a second aspect of this disclosure, a low-altitude intelligent connected device coverage range analysis device based on holographic mesh and GPGPU is provided. The device includes: The target detection area determination module is used to determine the target detection area based on the detection angle and maximum detection distance of the low-altitude intelligent connected device. The workspace grid system generation module is used to divide the target detection area into three-dimensional grids based on predefined holographic grid coding rules, and generate a workspace grid system. The integrated obstacle mesh generation module is used to perform mesh coding processing on multi-source ground feature data within the target detection area based on the workspace mesh system, and generate an integrated obstacle mesh set; An ideal coverage grid generation module is used to generate an ideal coverage grid set in the workspace grid system based on the signal transmission parameters of the low-altitude intelligent connected device. The detection ray construction module is used to construct multiple detection rays in the workspace grid system, starting from the location of the low-altitude intelligent connected device and ending at each grid cell in the ideal signal coverage grid set. The actual signal coverage grid generation module is used to perform occlusion detection on each probe ray with the integrated obstacle grid set using the GPGPU, and generate the actual signal coverage grid set. The actual signal coverage range generation module is used to parse the encoding of each grid unit in the actual signal coverage grid set into geographic coordinates, generate and output the actual signal coverage range of the low-altitude intelligent connected device.

[0015] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0016] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the methods according to the first and / or second aspects of this disclosure.

[0017] In this disclosure, firstly, the target detection area is determined based on the detection parameters of low-altitude intelligent connected devices, ensuring that coverage analysis focuses on the core area and avoiding interference from invalid data. Simultaneously, a three-dimensional grid is created for the target area using predefined holographic grid coding rules, establishing a workspace grid system with local indexes. This provides a standardized spatial benchmark for subsequent data processing and computation, fundamentally solving the problems of difficult multi-source feature data fusion and geographic coordinate matching deviations, enabling unified coding and accurate association of feature data from different sources with real-world geographic space. Secondly, based on this workspace grid system, multi-source feature data is grid-coded and merged for deduplication, generating a comprehensive obstacle grid set. This not only fully preserves key information about feature occlusion but also eliminates redundant data, achieving localized focused data storage and effectively reducing the load on storage hardware. Furthermore, by generating an ideal coverage grid set and constructing multiple... The system utilizes the massively parallel computing capabilities of GPGPUs to detect occlusion in each detection ray, transforming the massive computational tasks that originally relied on CPU serial processing into independent parallel computing units. This significantly improves the efficiency of the interaction between the detection ray and obstacles, breaking through the efficiency bottleneck of traditional computing schemes and enabling rapid analysis and calculation of large-scale coverage areas. Finally, by parseting the encoding of the actual signal coverage grid set into geographic coordinates, the system achieves a precise mapping of coverage area from grid data to real geographic space. The output actual signal coverage area can be directly applied to practical application scenarios such as security plan formulation and equipment deployment optimization. Combined with precise occlusion judgment of multi-source objects, the system ensures high accuracy in coverage area assessment, effectively eliminating the problem of misjudgment of coverage blind spots caused by model simplification or data incompatibility in traditional methods. This provides timely, accurate, and practical technical support for low-altitude security protection.

[0018] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0019] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 A flowchart illustrating a method for analyzing the coverage of low-altitude intelligent connected devices based on holographic meshes and GPGPU, provided by an embodiment of this disclosure, is shown. Figure 2 A structural diagram of a low-altitude intelligent connected device coverage analysis device based on holographic mesh and GPGPU provided in an embodiment of this disclosure is shown. Figure 3 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0021] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0022] General-Purpose computing on Graphics Processing Units (GPGPUs) utilize the high parallel computing capabilities of GPUs to perform general-purpose computing tasks in non-graphics domains.

[0023] Holographic mesh: A three-dimensional mesh framework built on the Earth's grid subdivision system, enabling unified organization, spatiotemporal alignment, and information fusion of multi-source heterogeneous spatial data.

[0024] In this disclosure, firstly, the target detection area is determined based on the detection parameters of low-altitude intelligent connected devices, ensuring that coverage analysis focuses on the core area and avoiding interference from invalid data. Simultaneously, a three-dimensional grid is created for the target area using predefined holographic grid coding rules, establishing a workspace grid system with local indexes. This provides a standardized spatial benchmark for subsequent data processing and computation, fundamentally solving the problems of difficult multi-source feature data fusion and geographic coordinate matching deviations, enabling unified coding and accurate association of feature data from different sources with real-world geographic space. Secondly, based on this workspace grid system, multi-source feature data is grid-coded and merged for deduplication, generating a comprehensive obstacle grid set. This not only fully preserves key information about feature occlusion but also eliminates redundant data, achieving localized focused data storage and effectively reducing the load on storage hardware. Furthermore, by generating an ideal coverage grid set and constructing multiple... The system utilizes the massively parallel computing capabilities of GPGPUs to detect occlusion in each detection ray, transforming the massive computational tasks that originally relied on CPU serial processing into independent parallel computing units. This significantly improves the efficiency of the interaction between the detection ray and obstacles, breaking through the efficiency bottleneck of traditional computing schemes and enabling rapid analysis and calculation of large-scale coverage areas. Finally, by parseting the encoding of the actual signal coverage grid set into geographic coordinates, the system achieves a precise mapping of coverage area from grid data to real geographic space. The output actual signal coverage area can be directly applied to practical application scenarios such as security plan formulation and equipment deployment optimization. Combined with precise occlusion judgment of multi-source objects, the system ensures high accuracy in coverage area assessment, effectively eliminating the problem of misjudgment of coverage blind spots caused by model simplification or data incompatibility in traditional methods. This provides timely, accurate, and practical technical support for low-altitude security protection.

[0025] Figure 1 The flowchart illustrates a method for analyzing the coverage of low-altitude intelligent connected devices based on holographic meshes and GPGPU, as provided in an embodiment of this disclosure. Figure 1 As shown, a method 100 for analyzing the coverage range of low-altitude intelligent connected devices based on holographic grids and GPGPU may include the following steps: S110 determines the target detection area based on the detection angle and maximum detection distance of low-altitude intelligent connected devices.

[0026] Specifically, the actual deployment center coordinates (including latitude, longitude, and altitude) of the low-altitude intelligent connected equipment must first be clearly defined. This serves as the spatial reference origin for the target detection area, avoiding deviations in area definition due to ambiguous references. Combining the equipment's detection angle parameters (covering both horizontal and vertical detection angles), a three-dimensional spatial detection cone model is constructed to accurately characterize the divergence direction of the equipment's signals, compensating for the shortcomings of traditional two-dimensional analysis that neglects the vertical dimension. Using the maximum detection distance as the radial extension threshold and the three-dimensional detection cone model as the spatial boundary constraint, an initial three-dimensional detection space is formed by expanding outwards. This ensures that the maximum effective detection distance of the equipment is fully contained within this initial space in all possible signal propagation directions, avoiding the truncation of the equipment's actual coverage area due to boundary limitations and guaranteeing the integrity of subsequent coverage analysis.

[0027] Meanwhile, considering the actual application needs of low-altitude scenarios (such as the distribution range of ground features in the target area and the core boundaries of security protection), the initial three-dimensional detection space is reasonably narrowed to eliminate irrelevant geographical areas that exceed the actual protection requirements, thereby reducing the redundancy of subsequent data processing. This ensures that the target detection area can accurately match the equipment's detection capabilities and the actual application scenario, and lays a precise and efficient spatial foundation for subsequent workspace grid division and multi-source ground feature data processing. This avoids the problems of excessively large area definitions leading to a surge in computation or excessively small areas leading to coverage omissions in traditional methods.

[0028] S120, based on predefined holographic grid coding rules, divides the target detection area into three-dimensional grids to generate a workspace grid system.

[0029] In some embodiments, based on predefined holographic grid coding rules, the target detection area is divided into three-dimensional grids to generate a workspace grid system, including: Based on the business assessment accuracy requirements of low-altitude intelligent connected equipment, the encoding level and corresponding three-dimensional grid size of the holographic grid are determined. Based on the three-dimensional grid size and predefined holographic grid coding rules, the target detection area is discretized into three dimensions, and each grid cell is assigned a globally unique three-dimensional row and column code to form an initial dedicated working area grid set. Using the geometric boundary of the target detection area as a reference, the coordinate transformation of the dedicated work area grid set is performed to generate a local row and column number for each grid cell for local fast indexing, and a mapping relationship between the local row and column number and the globally unique three-dimensional row and column code is established, finally generating a workspace grid system with local indexing.

[0030] Specifically, the predefined holographic grid coding rules take the geographic coordinate system as the core and the Earth ellipsoid as the reference. By constructing a multi-level and hierarchical spatial coding system through latitude, longitude and altitude information, the grid division is naturally associated with the real geographic space, realizing the unified organization, spatiotemporal alignment and information fusion of multi-source heterogeneous spatial data.

[0031] Specifically, when determining the coding level and corresponding 3D grid size of the holographic grid, it is necessary to closely integrate the operational assessment accuracy requirements of low-altitude intelligent connected devices, fully consider the ground feature density, coverage area, and actual calculation accuracy requirements of the target detection area, and follow the principle that "coding level and grid size are inversely related"—the higher the coding level, the smaller the grid size, which is suitable for scenarios with dense ground features and high accuracy requirements, such as urban core areas; the lower the coding level, the larger the grid size, which is suitable for scenarios with sparse ground features and lower accuracy requirements, such as suburban low-altitude areas. This achieves precise matching between grid size and scenario requirements, balances computational efficiency and analysis accuracy, and makes up for the shortcomings of traditional single grid models that are difficult to adapt to multiple scenarios.

[0032] Specifically, in the 3D discretization stage, the target detection area is uniformly and regularly divided into 3D spaces according to a preset 3D grid size and latitude, longitude, and altitude information. This transforms the continuous target detection area into a set of discretized grid units. During the division process, the consistency of the grid division intervals in both latitude, longitude, and altitude directions is strictly ensured to guarantee a regular and uniform grid structure. Simultaneously, each resulting grid unit is assigned a globally unique 3D row and column code. This code directly links the grid unit's absolute position information in 3D space, including the specific latitude and longitude coordinate range and altitude interval, achieving precise binding between the grid unit and the real geographic space. This fundamentally solves the problem of traditional voxel grids being difficult to match with geographic coordinates.

[0033] Subsequently, using the geometric boundary of the target detection area as a reference, the offset of this boundary relative to the global grid system is first calculated. Then, based on this offset, the global 3D row and column codes of each grid cell in the initial dedicated work area grid set are offset corrected and clipped to generate local row and column numbers applicable only to the target detection area. These local row and column numbers are stored using a compact numerical type, significantly reducing the data storage overhead of identical grid information and reducing memory usage and indexing time for subsequent computationally intensive operations such as ray propagation calculations and occlusion interaction analysis. During this process, a bidirectional mapping relationship needs to be established between the local row and column numbers and the globally unique 3D row and column codes. This allows for rapid indexing of the corresponding grid cells within the target detection area, improving data access and computation efficiency. Furthermore, this mapping relationship allows for the reverse calculation of the globally unique 3D row and column codes of the grid cells, enabling precise location of their absolute position in global geospatial space. This provides support for the accurate mapping of subsequent calculation results to real-world geographic coordinates, ultimately forming a workspace grid system with local indexing that combines efficient indexing capabilities with geographic binding characteristics.

[0034] S130, based on the workspace grid system, performs grid coding on multi-source ground feature data within the target detection area to generate a comprehensive obstacle grid set.

[0035] In some embodiments, based on a workspace grid system, multi-source terrain data within the target detection area are subjected to grid coding processing to generate a comprehensive obstacle grid set, including: Acquire multi-source ground feature data within the target detection area. The multi-source ground feature data includes at least one of terrain, buildings, and vegetation. For each type of land cover data, spatial intersection analysis is performed on each land cover entity and the grid cells in the workspace grid system. All grid cells that have spatial intersection with the land cover entity are marked as the gridded representation of the land cover entity, thus obtaining a single obstacle grid subset corresponding to the land cover data. Spatial integrity verification is performed on a single obstacle grid subset corresponding to each type of terrain feature data to ensure that it can fully represent the spatial outline of that type of terrain feature within the target detection area. Merge all validated subsets of individual obstacle meshes and perform deduplication based on the local row and column numbers of the mesh cells to generate a unified set of comprehensive obstacle meshes containing all obstacle information.

[0036] Specifically, the multi-source ground feature data within the target detection area covers not only core ground feature types such as terrain, buildings, and vegetation, but also geographic data from different sources such as oblique photogrammetry data, digital elevation models (DEM), and point cloud data. After acquisition, the data needs to be preprocessed to standardize the format, unify the coordinate reference and spatial resolution of the data, and ensure that all types of data can be accurately aligned with the geographic coordinate system of the workspace grid system, laying the data foundation for subsequent grid coding.

[0037] Specifically, for each type of preprocessed ground feature data, the three-dimensional spatial contour information of each ground feature entity needs to be extracted one by one. The spatial geometric intersection algorithm is used to compare and analyze each ground feature entity with all grid cells in the working space grid system to determine whether there is spatial overlap between the ground feature entity and the grid cell. As long as the ground feature entity occupies part or all of the spatial range of a certain grid cell, it is determined that there is an intersection relationship between the two. Then, the grid cell is marked as the gridded representation of the corresponding ground feature entity, and the local row and column number and global three-dimensional row and column code of the grid cell in the working space grid system are recorded. In this way, the grid coding of a single ground feature entity is completed. After all ground feature entities in a type of ground feature data have been coded, they are integrated to form a single obstacle grid subset corresponding to that type of ground feature data.

[0038] Specifically, to avoid incomplete feature representation due to missing data or algorithmic bias, spatial integrity verification is required for a single obstacle grid subset corresponding to each type of feature data. This is done by overlaying and comparing the gridded feature outline with the original three-dimensional spatial extent of the feature entity to check for any feature areas not covered by the grid or any redundant grid markers exceeding the actual extent of the feature. If any missing features are found, the corresponding grid cell codes are added; if there is redundancy, invalid grids are removed. This ensures that a single obstacle grid subset can completely and accurately represent the spatial distribution and outline features of this type of feature within the target detection area, preventing omissions in feature representation from affecting the accuracy of subsequent occlusion detection.

[0039] Finally, all subsets of single obstacle grids that have passed integrity verification are aggregated, and comparison rules are established using the local row and column numbers of the grid cells as the unique benchmark. Through coding, duplicate grid cells describing obstacles at the same location from different data sources are quickly compared and removed, thus completely solving the redundancy problem in multi-source ground feature data fusion. Ultimately, a unified, non-repeating, comprehensive obstacle grid set covering all obstacles within the target detection area is generated. This set links multi-source ground feature information through standardized grid coding, preserving the key occlusion features of ground features on signal propagation while achieving efficient fusion of multi-source ground feature data. This provides accurate and unified obstacle reference for subsequent detection of occlusion of detection rays.

[0040] S140 generates an ideal coverage grid set within the workspace grid system based on the signal transmission parameters of low-altitude intelligent connected devices.

[0041] In some embodiments, generating an ideal coverage grid set based on the signal transmission parameters of low-altitude intelligent connected devices within a workspace grid system includes: Using the geometric center of the low-altitude intelligent connected device as the signal transmission origin, and combining the signal transmission form and core transmission parameters, a coverage shell under an ideal unobstructed scenario is generated through spatial geometric modeling operations; the coverage shell is constructed in the form of a triangular mesh. Based on the workspace grid system, the coverage shell in the form of a triangular mesh is discretized, and grid cells that spatially intersect with the triangular mesh are selected. These grid cells are then integrated into a grid set to obtain the ideal coverage grid set; among which... In an ideal cover grid set, the row and column numbers of each grid cell are represented using a compact numeric type.

[0042] Specifically, the signal transmission parameters of the equipment must first be fully defined, including the signal transmission form determined by the equipment type (such as a sphere for omnidirectional coverage equipment, a hemisphere for directional upward coverage equipment, and a spindle for directional long-distance coverage equipment), as well as the core transmission parameters (including the signal working radius, i.e., the maximum coverage distance, and the beam angle, i.e., the signal divergence angle of directional equipment), to ensure that the parameters are complete and consistent with the actual performance of the equipment, thus providing a foundation for accurate modeling of the ideal coverage range.

[0043] Subsequently, the geometric center of the low-altitude intelligent connected device is used as the signal transmission origin. This origin must accurately correspond to its specific three-dimensional coordinates in the workspace grid system to ensure that the transmission origin and the coordinate reference of the workspace grid system are completely aligned. Combining the clearly defined signal transmission form and core transmission parameters, the coverage shell under the ideal unobstructed scenario is constructed through spatial geometric modeling technology. During the modeling process, it is necessary to accurately fit according to the spatial characteristics of different transmission forms. For example, the spherical transmission form needs to construct a complete sphere with the transmission origin as the center and the maximum coverage distance as the radius. The directional transmission form needs to form the corresponding three-dimensional spatial boundary according to the beam angle constraint. The final generated coverage shell is constructed in the form of a triangular mesh. By discretizing the continuous spatial surface into several regular triangular patches, the spatial contour of the coverage area is ensured to be accurate and easy to process later.

[0044] Based on this, and using the workspace grid system defined above, the outer shell of the triangular mesh coverage area is discretized: a spatial geometric intersection detection algorithm is used to compare the spatial position of each triangular facet in the triangular mesh with all grid cells in the workspace grid system one by one, to determine whether there is a spatial intersection between the grid cell and the triangular facet (including cases where the grid cell partially overlaps or is completely contained within the coverage area of ​​the triangular mesh), and to filter out all grid cells that meet the intersection condition; at the same time, since these grid cells are all within the workspace grid system, their row and column numbers are stored using a compact numerical type (such as short integer), which greatly reduces data storage overhead compared to the traditional global encoding method and improves the efficiency of subsequent data processing and calculation.

[0045] Finally, all the selected grid units are systematically integrated according to their local row and column numbers in the workspace grid system to form a unified grid set, which is the ideal coverage grid set. This set fully represents the theoretical coverage range of the low-altitude intelligent connected equipment under the condition of no ground cover, and establishes a precise correlation with the workspace grid system and the actual geographic space, providing standardized and efficient basic data support for the subsequent construction of detection rays and the detection of obstruction.

[0046] S150 constructs multiple detection rays in the workspace grid system, starting from the location of the low-altitude intelligent connected device and ending at each grid cell in the ideal signal coverage grid set.

[0047] Specifically, when constructing multiple detection rays, the physical installation location of the low-altitude intelligent connected equipment must first be accurately mapped to the workspace grid system. Through the previously established mapping relationship between geographic coordinates and grid codes, the specific grid cell (i.e., the starting grid cell) where the geometric center of the equipment is located is determined. The local row and column number, global three-dimensional row and column code, and geometric center coordinates (including latitude, longitude, and altitude) of this cell are clarified to ensure that the spatial reference of the ray starting point is completely aligned with the workspace grid system, and to avoid the ray direction deviation caused by the starting point positioning deviation.

[0048] Subsequently, for each grid cell in the ideal coverage grid set, its geometric center is used as the precise endpoint of the ray. Choosing the geometric center ensures that the ray can completely represent the signal propagation direction corresponding to that grid cell, while avoiding omissions or repetitions in the coverage area due to improper selection of the endpoint position. When constructing each ray, based on the discretization characteristics of the workspace grid and the spatial positional relationship between the starting and ending grid cells, the straight-line propagation trajectory of the ray is determined through spatial geometric calculations. This clarifies the path sequence of the ray as it travels through each grid cell in the workspace grid system, i.e., the sequence of intermediate grid cells that the ray passes through after starting from the starting grid. This provides a clear path basis for the subsequent grid-by-grid judgment during parallel execution of occlusion detection by the GPGPU.

[0049] Simultaneously, it is necessary to ensure the integrity and full coverage of the detection rays. Each grid cell in the ideal coverage grid set corresponds to an independent detection ray, with no omissions or repetitions, fully covering all spatial directions within the ideal coverage area of ​​the device. This ensures that subsequent analysis of the actual coverage area can fully reflect the theoretical signal propagation area of ​​the device under unobstructed conditions. In addition, each detection ray needs to be associated with and recorded with the local row and column numbers, global three-dimensional row and column codes, and spatial coordinate information of the starting and ending grids, forming a structured ray data set. This facilitates rapid indexing and task allocation during subsequent GPGPU calculations and ensures accurate spatial association between the rays and the integrated obstacle grid and workspace grid system. This lays the data foundation for efficient and accurate occlusion detection and aligns with the core design idea of ​​transforming the continuous signal coverage problem into a discrete grid problem, ensuring the continuity and logical consistency of the technical process.

[0050] S160 uses a GPGPU to perform occlusion detection on each probe ray and the integrated obstacle mesh set to generate an actual signal coverage mesh set.

[0051] In some embodiments, using a GPGPU to perform occlusion detection on each probe ray and the integrated obstacle mesh set to generate an actual signal coverage mesh set includes: The task of detecting the occlusion of multiple probe rays is distributed to multiple parallel computing cores of the GPGPU, forming a parallel computing task queue. For each detection ray, control its movement in the workspace grid system from the starting grid cell to the ending grid cell, cell by cell; and during each step, determine whether the current grid cell exists in the integrated obstacle grid set; If it exists, the ray is determined to be blocked, the current grid cell is recorded as the blocking point, and further detection of the ray is terminated; If it does not exist, continue stepping towards the endpoint until the endpoint grid cell is reached, then it is determined that the ray is not blocked; The detection results of all probe rays are collected, and the endpoint grid cells corresponding to all rays that are determined to be unblocked are integrated to form the actual signal coverage grid set.

[0052] Specifically, based on the hardware architecture characteristics of GPGPU and combined with the number and data characteristics of the detection rays, a GPU-friendly data structure is used to organize all relevant information of the detection rays (including the local row and column numbers of the start / end grid, global 3D row and column encoding, and grid-by-grid path sequence, etc.) in a structured manner. This ensures that the data can be quickly read and accessed by the GPGPU parallel cores, laying the foundation for efficient task allocation. Subsequently, the occlusion detection tasks of multiple detection rays are evenly distributed to multiple parallel computing cores of the GPGPU through a task scheduling algorithm, forming a conflict-free parallel computing task queue. This fully utilizes the multi-core parallel advantages of GPGPU, avoiding excessive load on a single core or idle resources. At the same time, relying on the inherent characteristics that each ray's movement and occlusion determination are independent and have no data dependency, the efficient advancement of parallel computing is guaranteed, breaking through the efficiency bottleneck of traditional CPU serial processing.

[0053] Specifically, for each probe ray, the GPGPU controls it to move from the starting grid cell in the workspace grid system, gradually advancing towards the ending grid cell according to a preset grid-by-grid movement rule (ordered stepping based on grid space coordinates to ensure that the ray does not skip any intermediate grid cells along the straight-line propagation trajectory). During each step, a pre-built fast indexing mechanism (such as a hash index based on local row and column numbers) is used to query in real time whether the code of the current grid cell exists in the integrated obstacle grid set. This integrated obstacle grid set has been pre-loaded into the GPGPU memory, and the high-speed read and write capabilities of the memory greatly shorten the query response time, avoiding the latency caused by frequent data transfer between the CPU and GPU. If the query result is positive, it is determined that the ray propagation has encountered an obstruction. The local row and column number of the current grid cell, the global three-dimensional row and column code, and the specific coordinates of the obstruction location are immediately recorded. At the same time, further detection of the ray is terminated to ensure that no key obstruction information is missed. If the query result is negative, the ray continues to move along the preset path towards the endpoint grid cell until it is fully reached. At this point, it is determined that the ray is not obstructed, and the endpoint grid cell is marked as "signal reachable".

[0054] Specifically, after all parallel computing cores complete their assigned occlusion detection tasks, an efficient parallel data integration strategy (such as atomic operations and reduction algorithms) is used to synchronously aggregate the detection results of all probe rays, avoiding data conflicts and redundant storage in parallel computing. During this process, the endpoint grid cells corresponding to all rays determined to be unoccluded need to be screened out, while invalid grid cells that did not reach the endpoint due to occlusion or were repeatedly marked are removed to ensure the accuracy and uniqueness of the results. Finally, the screened valid endpoint grid cells are systematically integrated according to their local row and column numbers in the workspace grid system to form a unified set of actual signal coverage grids. This set fully represents the entire spatial area that the low-altitude intelligent connected device can effectively reach after considering complex ground cover occlusion. It achieves efficient detection by relying on GPGPU parallel computing and ensures the accuracy of occlusion detection through precise grid-by-grid query and judgment, perfectly balancing the core requirements of "efficiency" and "accuracy" that are difficult to achieve with traditional technologies.

[0055] S170 parses the encoding of each grid unit in the actual signal coverage grid set into geographic coordinates, and generates and outputs the actual signal coverage range of the low-altitude intelligent connected device.

[0056] In some embodiments, parsing the encoding of each grid cell in the actual signal coverage grid set into geographic coordinates to generate and output the actual signal coverage range of the low-altitude intelligent connected device includes: Read the globally unique three-dimensional row and column code of each grid cell in the actual signal coverage grid set; Based on the decoding algorithm corresponding to the predefined holographic grid encoding rules, each globally unique three-dimensional row and column code is converted into its corresponding geographic coordinates in batches; Based on the set of all geographic coordinate points obtained from the transformation, the actual signal coverage range of the low-altitude intelligent connected device is generated and output through spatial reconstruction.

[0057] In some embodiments, the actual signal coverage range is represented in the following ways: At least one of the following: a two-dimensional planar thematic map of coverage, a three-dimensional spatial model of coverage, a statistical report of coverage area, and a map of blind spots.

[0058] Specifically, firstly, relying on the previously established bidirectional mapping relationship between local row and column numbers and globally unique three-dimensional row and column codes, the globally unique three-dimensional row and column code corresponding to each grid unit in the actual signal coverage grid set is quickly read, ensuring the accuracy and efficiency of code reading and avoiding geographic coordinate resolution deviations due to coding association errors. Subsequently, based on the decoding algorithm corresponding to the predefined holographic grid coding rules—this algorithm takes the geographic coordinate system as its core and the Earth's ellipsoid as its reference, and incorporates backward calculation logic for latitude, longitude, and altitude—all globally unique three-dimensional row and column codes are synchronously decoded through a batch processing mechanism. The three-dimensional spatial location information contained in the code is accurately converted into the corresponding standard geographic coordinates (including specific latitude, longitude, and altitude data). During the decoding process, the rationality of the coordinates needs to be verified, and abnormal coordinates that exceed the geographic boundaries of the target detection area are eliminated, ensuring that the converted geographic coordinate point set can truly reflect the spatial range that the signal can reach, thus solving the pain point that traditional voxel grid calculation results are difficult to match with geographic coordinates.

[0059] Specifically, based on the complete set of geographic coordinate points obtained through transformation, a spatial reconstruction algorithm is used to generate the coverage area. By connecting the boundary coordinates of grid cells in the coordinate point set, the three-dimensional spatial outline of the signal-reachable area is restored, ensuring both the continuity and integrity of the coverage area and accurately presenting the signal propagation boundaries at different heights and directions. This overcomes the limitation of traditional "shadow mapping" technology, which can only present the visible range from a single perspective. Furthermore, based on the needs of actual application scenarios, diverse representations of actual signal coverage are generated: Two-dimensional planar coverage thematic maps must clearly mark latitude and longitude scales, coverage boundary lines, and the locations of key features, intuitively displaying the coverage distribution in the planar dimension; three-dimensional coverage spatial models must completely restore the three-dimensional coverage morphology, supporting multi-angle viewing and spatial measurement, adapting to the refined evaluation needs of complex low-altitude scenarios; coverage area statistical reports must include core indicators such as coverage area, coverage efficiency, and the number of signal-reachable grid cells, providing data support for equipment performance evaluation; blind zone distribution maps must clearly mark the locations and ranges of blind zones not covered by signals by comparing the ideal coverage area with the actual coverage area, providing clear guidance for subsequent blind zone replenishment deployments.

[0060] In some embodiments, the method further includes: Spatial overlay analysis of the actual signal coverage of multiple low-altitude intelligent connected devices is performed to generate joint coverage range, coverage overlap area and remaining blind zone analysis results.

[0061] Specifically, for scenarios with multiple low-altitude intelligent connected devices, further spatial overlay analysis of the actual signal coverage of each device is required. Based on the set of geographic coordinates of the actual coverage of each device, spatial intersection operations are used to accurately identify overlapping areas of different device coverage, calculate the overlapping area and overlap ratio, and simultaneously use spatial difference operations to filter out remaining blind spots not covered by any device. This generates a comprehensive analysis result including the joint coverage area, details of overlapping areas, and the distribution of remaining blind spots. During the overlay analysis, the differences in detection parameters of different devices (such as detection angle and maximum detection distance) must be taken into account to ensure the accuracy of the overlay results. All final outputs should seamlessly integrate with the real-world geographic scenario, providing a basis for optimizing the deployment of single devices and data support for developing blind spot compensation schemes for multi-device collaborative protection. This fully meets the core requirements of practical applications such as low-altitude security and device deployment for the practicality and accuracy of coverage analysis results.

[0062] According to the embodiments of this disclosure, the following technical effects are achieved: (1) In terms of multi-source data integration and geographic positioning. This disclosure relies on the holographic grid coding rules based on the geographic coordinate system and the Earth ellipsoid to transform multi-source ground feature data such as oblique photography data, digital elevation models, and point cloud data into standardized three-dimensional grid codes. This completely solves the pain points of inconsistent formats and compatibility conflicts of multi-source ground feature data in traditional technologies, and realizes the efficient integration of ground feature information from different sources. At the same time, by establishing a precise mapping relationship between grid codes and the global geographic coordinate system, as well as a bidirectional association between local row and column numbers and global three-dimensional row and column codes, the analysis results can directly correspond to geographic information such as latitude, longitude, and altitude in the real scene. This completely gets rid of the limitation that traditional voxel grid schemes are difficult to match with geographic coordinates, and ensures that the coverage analysis results can be seamlessly connected with the real ground feature location and control boundary, providing accurate geographic reference for equipment deployment and security plan formulation.

[0063] (2) In terms of data storage optimization, this disclosure reduces the global grid matrix to a dedicated workspace defined by device detection parameters, eliminating irrelevant data outside the scene and achieving localized focused storage of obstacle data. At the same time, the row and column numbers of grid cells in the workspace are represented by a compact numerical type, which significantly reduces data storage overhead without losing key geographic information. This effectively solves the storage pressure problem caused by the exponential growth of the number of voxels in fine-grained analysis scenarios in traditional voxel schemes, allowing limited hardware resources to support larger-scale and more refined coverage analysis tasks, laying a storage foundation for subsequent efficient computing.

[0064] (3) In terms of improving computational efficiency. This disclosure innovatively transforms the problem of low-altitude intelligent connected device coverage analysis into a ray occlusion detection problem under a discrete grid. By utilizing the independent characteristics of the movement and occlusion determination of each detection ray, it fully leverages the advantages of the GPGPU ultra-large-scale parallel computing core, distributing the occlusion detection task of massive detection rays to multiple parallel cores for synchronous processing, thus completely breaking through the efficiency bottleneck of traditional CPU serial or inefficient parallel computing. Compared with the problem of excessive computation time caused by the need to process billions of triangular faces in the three-dimensional analysis scheme based on triangular mesh, and the defect of low computational efficiency of the traditional voxel scheme, the parallel computing mode of this disclosure significantly shortens the processing time of coverage analysis, and can quickly output accurate results, meeting the urgent needs of analysis timeliness in scenarios such as low-altitude security and major event support in complex urban environments.

[0065] (4) In terms of the accuracy and practicality of coverage analysis. This disclosure fully represents the spatial outline of ground features through three-dimensional mesh modeling. Combined with the occlusion detection mechanism of step-by-step meshing, it accurately simulates the propagation path of signals in three-dimensional space and the interaction between ground feature occlusion and the situation. It effectively avoids the defects of traditional two-dimensional analysis that ignores vertical dimension occlusion and the "shadow mapping" technology that can only achieve visualization but cannot accurately calculate. It ensures that the output actual coverage can truly reflect the effective boundary of the equipment in complex ground feature environment. At the same time, it supports the generation of two-dimensional planar coverage thematic maps, three-dimensional coverage spatial models, coverage statistical reports, blind spot distribution maps and other diverse forms of expression. It can also perform spatial overlay analysis on the actual coverage of multiple devices, accurately identify joint coverage, coverage overlap area and remaining blind spot, and provide comprehensive and practical data support for single device deployment optimization and multi-device collaborative blind spot filling. It significantly improves the scientificity and reliability of the construction of low-altitude safety protection system.

[0066] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0067] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0068] Figure 2 The diagram illustrates a structural representation of a low-altitude intelligent connected device coverage analysis apparatus based on a holographic grid and GPGPU, as provided in an embodiment of this disclosure. Figure 2As shown, a low-altitude intelligent connected device coverage range analysis device 200 based on holographic grids and GPGPU may include: The target detection area determination module 210 is used to determine the target detection area based on the detection angle and maximum detection distance of the low-altitude intelligent connected device.

[0069] The workspace grid system generation module 220 is used to divide the target detection area into three-dimensional grids based on predefined holographic grid coding rules to generate a workspace grid system.

[0070] The integrated obstacle mesh generation module 230 is used to perform mesh coding processing on multi-source ground feature data within the target detection area based on the workspace mesh system, and generate an integrated obstacle mesh set.

[0071] The ideal coverage grid generation module 240 is used to generate an ideal coverage grid set in the workspace grid system based on the signal transmission parameters of the low-altitude intelligent connected device.

[0072] The detection ray construction module 250 is used to construct multiple detection rays in the workspace grid system, starting from the location of the low-altitude intelligent connected device and ending at each grid cell in the ideal signal coverage grid set.

[0073] The actual signal coverage grid generation module 260 is used to perform occlusion detection on each probe ray with the integrated obstacle grid set using the GPGPU to generate the actual signal coverage grid set.

[0074] The actual signal coverage range generation module 270 is used to parse the encoding of each grid unit in the actual signal coverage grid set into geographic coordinates, generate and output the actual signal coverage range of the low-altitude intelligent connected device.

[0075] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0076] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0077] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0078] Figure 3A schematic block diagram of an electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0079] Electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in ROM 302 or a computer program loaded into RAM 303 from storage unit 308. RAM 303 can also store various programs and data required for the operation of electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O interface 305 is also connected to bus 304.

[0080] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0081] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).

[0082] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0083] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0084] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0085] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0086] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0087] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0088] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.

[0089] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A low-altitude intelligent connected device coverage analysis method based on holographic grid and GPGPU, characterized in that, include: The target detection area is determined based on the detection angle and maximum detection distance of low-altitude intelligent connected devices. Based on predefined holographic grid coding rules, the target detection area is divided into three-dimensional grids to generate a workspace grid system. Based on the workspace grid system, the multi-source ground object data within the target detection area are subjected to grid coding processing to generate a comprehensive obstacle grid set; Based on the signal transmission parameters of the low-altitude intelligent connected device, an ideal coverage grid set is generated in the workspace grid system; Starting from the location of the low-altitude intelligent connected device and ending at each grid cell in the ideal signal coverage grid set, multiple detection rays are constructed in the workspace grid system; Using a GPGPU, occlusion detection is performed on each probe ray in relation to the integrated obstacle mesh set to generate an actual signal coverage mesh set; The encoding of each grid cell in the actual signal coverage grid set is parsed into geographic coordinates to generate and output the actual signal coverage range of the low-altitude intelligent connected device.

2. The method according to claim 1, characterized in that, The process of dividing the target detection area into a three-dimensional grid based on predefined holographic grid coding rules to generate a workspace grid system includes: Based on the business assessment accuracy requirements of the low-altitude intelligent connected equipment, the encoding level and corresponding three-dimensional grid size of the holographic grid are determined. Based on the three-dimensional grid size and the predefined holographic grid coding rules, the target detection area is divided into three-dimensional discretization, and each grid unit is assigned a globally unique three-dimensional row and column code to form an initial dedicated working area grid set. Using the geometric boundary of the target detection area as a reference, the dedicated workspace grid set is subjected to coordinate transformation, a local row and column number for local fast indexing is generated for each grid cell, and a mapping relationship is established between the local row and column number and the globally unique three-dimensional row and column code, finally generating a workspace grid system with local indexing.

3. The method according to claim 2, characterized in that, The step of performing grid coding processing on multi-source ground feature data within the target detection area based on the workspace grid system to generate a comprehensive obstacle grid set includes: Acquire multi-source feature data within the target detection area, wherein the multi-source feature data includes at least one of terrain, buildings, and vegetation; For each type of land feature data, spatial intersection analysis is performed on each land feature entity and the grid cells in the workspace grid system. All grid cells that have spatial intersection with the land feature entity are marked as the gridded representation of the land feature entity, thus obtaining a single obstacle grid subset corresponding to the land feature data. For each type of terrain feature data, the spatial integrity of the single obstacle grid subset is verified to ensure that it can completely represent the spatial outline of that type of terrain feature within the target detection area. Merge all validated subsets of individual obstacle meshes and perform deduplication based on the local row and column numbers of the mesh cells to generate a unified set of comprehensive obstacle meshes containing all obstacle information.

4. The method according to claim 2, characterized in that, The generation of an ideal coverage grid set based on the signal transmission parameters of the low-altitude intelligent connected device in the workspace grid system includes: Using the geometric center of the low-altitude intelligent connected device as the signal transmission origin, and combining the signal transmission form and core transmission parameters, a coverage area shell under an ideal unobstructed scenario is generated through spatial geometric modeling operations; wherein, the coverage area shell is constructed in the form of a triangular mesh. Based on the aforementioned workspace grid system, the coverage shell in the form of a triangular mesh is discretized, and grid cells that spatially intersect with the triangular mesh are selected. These grid cells are then integrated into a grid set to obtain the ideal coverage grid set; wherein... The row and column numbers of each grid cell in the ideal coverage grid set are represented using a compact numeric type.

5. The method according to claim 1, characterized in that, The step of using a GPGPU to perform occlusion detection on each probe ray and the integrated obstacle mesh set to generate an actual signal coverage mesh set includes: The task of detecting the occlusion of the multiple detection rays is assigned to multiple parallel computing cores of the GPGPU to form a parallel computing task queue. For each detection ray, it is controlled to move step by step from the starting grid cell to the ending grid cell in the workspace grid system; and during each step, it is determined whether the current grid cell exists in the integrated obstacle grid set. If it exists, the ray is determined to be blocked, the current grid cell is recorded as the blocking point, and further detection of the ray is terminated; If it does not exist, continue stepping towards the endpoint until the endpoint grid cell is reached, then it is determined that the ray is not blocked; The detection results of all probe rays are collected, and the endpoint grid cells corresponding to all rays that are determined to be unblocked are integrated to form an actual signal coverage grid set.

6. The method according to claim 5, characterized in that, The step of parsing the encoding of each grid cell in the actual signal coverage grid set into geographic coordinates, and generating and outputting the actual signal coverage range of the low-altitude intelligent connected device includes: Read the globally unique three-dimensional row and column code of each grid cell in the actual signal coverage grid set; Based on the decoding algorithm corresponding to the predefined holographic grid encoding rules, each of the globally unique three-dimensional row and column codes is batch converted into corresponding geographic coordinates; Based on the set of all geographical coordinate points obtained by the transformation, the actual signal coverage range of the low-altitude intelligent connected device is generated and output through spatial reconstruction.

7. The method according to claim 6, characterized in that, The actual signal coverage range is represented in the following ways: At least one of the following: a two-dimensional planar thematic map of coverage, a three-dimensional spatial model of coverage, a statistical report of coverage area, and a map of blind spots.

8. The method according to claim 1, characterized in that, The method further includes: Spatial overlay analysis of the actual signal coverage of multiple low-altitude intelligent connected devices is performed to generate joint coverage range, coverage overlap area and remaining blind zone analysis results.

9. A low-altitude intelligent connected device coverage range analysis device based on holographic grid and GPGPU, characterized in that, include: The target detection area determination module is used to determine the target detection area based on the detection angle and maximum detection distance of the low-altitude intelligent connected device. The workspace grid system generation module is used to perform three-dimensional grid division on the target detection area based on predefined holographic grid coding rules to generate a workspace grid system. The integrated obstacle mesh generation module is used to perform mesh coding processing on multi-source ground feature data within the target detection area based on the workspace mesh system, and generate an integrated obstacle mesh set; An ideal coverage grid generation module is used to generate an ideal coverage grid set in the workspace grid system based on the signal transmission parameters of the low-altitude intelligent connected device. The detection ray construction module is used to construct multiple detection rays in the workspace grid system, starting from the location of the low-altitude intelligent connected device and ending at each grid cell in the ideal signal coverage grid set. The actual signal coverage grid generation module is used to perform occlusion detection on each probe ray with the integrated obstacle grid set using the GPGPU, and generate the actual signal coverage grid set. The actual signal coverage range generation module is used to parse the encoding of each grid unit in the actual signal coverage grid set into geographic coordinates, generate and output the actual signal coverage range of the low-altitude intelligent connected device.

10. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1-8.

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