Method and device for calculating performance of unmanned aerial vehicle detection equipment based on spatial grid

Abstract

Embodiments of the present disclosure provide a kind of based on airspace grid unmanned aerial vehicle detection equipment performance calculation method and device, it is applied to unmanned aerial vehicle technical field.The method includes obtaining the construction barrier grid set in multiple source data in the region to be processed;Obtain the construction equipment envelope grid set in unmanned aerial vehicle detection equipment parameters in the region to be processed;According to barrier grid set and equipment envelope grid set, determine the visibility state of each equipment envelope grid unit, and generate the visibility grid set corresponding to each equipment;Through preset performance evaluation algorithm, calculate equipment performance according to the visibility grid set of each equipment;Based on preset merging algorithm, the visibility grid set and equipment performance of all equipment are merged, and the airspace total equipment performance grid set in the region to be processed is generated.In this way, the performance analysis and efficient fusion calculation problem in the management of low-altitude unmanned aerial vehicle detection equipment can be solved, and the coverage effect and comprehensive performance of the detection equipment widely deployed in low-altitude area can be accurately evaluated.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and more particularly to the field of unmanned aerial vehicle (UAV) technology, specifically to a method and apparatus for calculating the performance of UAV detection equipment based on airspace grids. Background Technology

[0002] With the rapid development of the low-altitude economy, low-altitude application scenarios are constantly expanding. In high-density drone scenarios, drone detection equipment has become a primary basis for low-altitude management and decision-making, and performance analysis of detection equipment has become an important basis for equipment site selection management. However, facing the ever-increasing scale of drones, the demand for detection equipment is also constantly growing, especially in large-scale low-altitude airspace management scenarios, which brings great challenges to the fusion calculation of detection equipment performance. For example, in low-altitude multi-device collaborative monitoring scenarios, in order to achieve fusion calculation of detection performance for the entire area, it is necessary to comprehensively consider multiple low-altitude factors such as regional coverage, terrain undulation, building distribution, and meteorological conditions, resulting in a significant increase in computational complexity.

[0003] Current methods for evaluating the performance of detection equipment typically employ an isolated computational approach, assessing the performance of only a single device. This means analyzing the coverage and detection capabilities of each device independently, without fully considering the spatial correlation and synergistic effects between devices. This approach faces significant challenges in large-scale network scenarios. For example, when using an isolated computational model to analyze the performance of each detection device individually, the computational load increases dramatically with the number of devices, due to the repetitive and independent calculations. This is particularly problematic in fusion computing tasks such as coverage analysis, overlap area optimization, and collaborative detection, where it becomes difficult to efficiently handle spatial correlations and data redundancy among multiple devices. This is essentially because the geographical proximity and structural characteristics of the detection range in the device deployment are not fully utilized, severely limiting the planning efficiency and real-time response capabilities of the system in large-scale regional deployments. Furthermore, this high-performance overhead hinders rapid simulation and solution iteration in dynamic environments, impacting the reliability and scalability of the low-altitude airspace management system. Furthermore, due to the lack of a mechanism for identifying and integrating overlapping areas of the equipment's detection range, the above method also suffers from a large amount of redundant computation in high-density equipment deployment scenarios. In particular, during the multi-data fusion computation, such as the overlay analysis of terrain, meteorological data and equipment coverage, the same spatial unit is processed multiple times, which greatly increases computational complexity and resource consumption. This problem severely limits the scalability and real-time analysis capabilities of large-scale low-altitude monitoring networks and makes it difficult to meet the urgent need for high-performance computing in modern low-altitude safety management.

[0004] It is evident that the current approach ignores the systemic characteristics brought about by device networking, which not only wastes computing resources but also makes it difficult for the evaluation results to truly reflect the overall performance under the joint operation of multiple devices. This leads to evaluation bias in system optimization and layout decisions. These problems severely limit the deployment efficiency and application effect of low-altitude UAV detection systems and also hinder the further expansion of low-altitude economy and airspace application scenarios. Summary of the Invention

[0005] This disclosure provides a method, apparatus, device, and storage medium for calculating the performance of UAV detection equipment based on a spatial grid.

[0006] According to a first aspect of this disclosure, a method for calculating the performance of a UAV detection device based on a spatial grid is provided. The method includes:

[0007] Acquire multi-source data within the area to be processed and construct an obstacle mesh set;

[0008] Obtain the parameters of UAV detection devices within the area to be processed, and construct a device envelope mesh set;

[0009] Based on the obstacle mesh set and the device envelope mesh set, determine the visibility state of each device envelope mesh unit and generate a visible mesh set corresponding to each device;

[0010] The device performance is calculated based on the visual grid set of each device using a preset performance evaluation algorithm.

[0011] Based on a preset merging algorithm, the visible grid sets and device performance of all devices are merged to generate a spatial summation device performance grid set within the area to be processed.

[0012] As described above and in any possible implementation, a further implementation is provided, wherein acquiring multi-source data within the region to be processed and constructing an obstacle mesh set includes:

[0013] Acquire multi-source data within the area to be processed;

[0014] Based on a preset geographic coordinate system, the multi-source data are uniformly registered;

[0015] Based on preset grid parameters, and according to the registered multi-source data, the spatial domain is discretized into regular three-dimensional grid cells in the horizontal and vertical directions. Each cell includes latitude and longitude, altitude, and shading attribute identifiers.

[0016] Based on the occlusion attribute identifiers of each unit, a grid index structure is established, and an obstacle grid set is output.

[0017] As described above and in any possible implementation, a further implementation is provided, wherein obtaining the parameters of the UAV detection device within the area to be processed and constructing the device envelope mesh set includes:

[0018] Obtain the parameters of the UAV detection equipment in the area to be processed. The equipment parameters include location, maximum detection range, azimuth angle, pitch angle, and equipment model.

[0019] The envelope range of each device is calculated based on the device parameters, and the envelope range is discretized into three-dimensional grid cells under the same spatial reference as the obstacle grid set. Each cell includes the device ID, distance to the device, and signal strength.

[0020] Based on the device ID of each unit, a grid index structure is established, and the device envelope grid set is output.

[0021] In addition to the aspects described above and any possible implementations, a further implementation is provided, wherein determining the visibility state of each device envelope mesh cell based on the obstacle mesh set and the device envelope mesh set, and generating a visible mesh set corresponding to each device, includes:

[0022] Based on the obstacle grid set, the device envelope network set is searched to determine whether the line of sight from the device to the envelope grid unit is blocked by the obstacle grid unit.

[0023] If yes, then mark the visibility state of the envelope mesh cell as invisible; otherwise, mark the visibility state of the envelope mesh cell as visible, and generate the visible mesh set corresponding to the device.

[0024] In addition to the aspects described above and any possible implementations, a further implementation is provided in which the construction of the preset performance evaluation algorithm includes:

[0025] A preset performance evaluation algorithm is constructed based on distance attenuation, equipment performance parameters, environmental parameters, and visibility coefficient; wherein, the equipment performance parameters include detection distance, sensitivity, signal-to-noise ratio, viewing angle, and distance attenuation, and the environmental parameters include meteorological conditions and electromagnetic noise.

[0026] As described above and in any possible implementation, a further implementation is provided, wherein the preset merging algorithm includes:

[0027] Weighted fusion algorithm, probabilistic merging algorithm, or maximum value synthesis algorithm.

[0028] In addition to the aspects and any possible implementations described above, an implementation is further provided in which the multi-source data includes at least two of digital surface models, digital elevation models, oblique photogrammetry models, building profile data, and land cover models.

[0029] According to a second aspect of this disclosure, a performance calculation device for unmanned aerial vehicle (UAV) detection equipment based on a spatial grid is provided. The device includes:

[0030] The module is used to acquire multi-source data within the area to be processed and construct an obstacle mesh set;

[0031] The module is also used to acquire parameters of UAV detection devices within the area to be processed and to construct a device envelope mesh set;

[0032] The generation module is used to determine the visibility status of each device envelope grid cell based on the obstacle grid set and the device envelope grid set, and to generate a visible grid set corresponding to each device.

[0033] The calculation module is used to calculate the device performance based on the visual grid set of each device using a preset performance evaluation algorithm;

[0034] The merging module is used to merge the visual grid sets and device performance of all devices based on a preset merging algorithm, and generate a spatial summation device performance grid set within the area to be processed.

[0035] 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.

[0036] 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 method described above.

[0037] This application provides a method for calculating the performance of UAV detection equipment based on a spatial grid. This method involves: acquiring multi-source data within a processing area to construct an obstacle grid set; acquiring UAV detection equipment parameters within the processing area to construct an equipment envelope grid set; determining the visibility status of each equipment envelope grid cell based on the obstacle grid set and the equipment envelope grid set, and generating a corresponding visible grid set for each equipment; calculating equipment performance based on the visible grid sets of each equipment using a preset performance evaluation algorithm; and merging the visible grid sets and equipment performance of all equipment using a preset merging algorithm to generate a processing array. The system comprises a grid set of equipment performance across the airspace within the region. Based on this, by constructing a grid of multi-source data within the low-altitude region and utilizing a grid caching mechanism to achieve equipment performance analysis and coverage assessment, the system can solve the problems of performance analysis and efficient fusion computing in the management of low-altitude UAV detection equipment. This effectively avoids redundant calculations, breaks through the performance bottleneck in airspace management, and supports accurate assessment of the coverage effect and overall performance of detection equipment widely deployed in the low-altitude region. This provides a more comprehensive and scientific basis for equipment site selection and offers an efficient computing solution for the site selection of large-scale detection equipment.

[0038] 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

[0039] 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:

[0040] Figure 1 A flowchart is shown for a method for calculating the performance of a UAV detection device based on a spatial grid according to an embodiment of the present disclosure;

[0041] Figure 2 A schematic diagram of a performance evaluation model according to an embodiment of the present disclosure is shown;

[0042] Figure 3 A schematic diagram of the output spatial domain summation device performance grid set according to an embodiment of the present disclosure is shown;

[0043] Figure 4 A block diagram of a performance calculation apparatus for a UAV detection device based on a spatial grid according to an embodiment of the present disclosure is shown;

[0044] Figure 5 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0045] 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.

[0046] 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.

[0047] This disclosure solves the problems of performance analysis and efficient fusion computing in the management of low-altitude UAV detection equipment by constructing a grid of multi-source data in low-altitude areas and using a grid caching mechanism to achieve equipment performance analysis and coverage evaluation. It effectively avoids redundant calculations, breaks through the performance bottleneck in airspace management, and supports accurate evaluation of the coverage effect and comprehensive performance of detection equipment widely deployed in low-altitude areas. This provides a more comprehensive and scientific basis for equipment site selection and an efficient computing solution for the site selection of large-scale detection equipment.

[0048] Figure 1 A flowchart of a method 100 for calculating the performance of a drone detection device based on a spatial grid according to an embodiment of the present disclosure is shown.

[0049] In box 110, acquire multi-source data within the area to be processed and construct an obstacle grid set.

[0050] In some embodiments, the area to be processed can be a geographical range specified according to the user's actual needs.

[0051] In some embodiments, the set grid is based on the Earth's ellipsoid and uses latitude, longitude and altitude information to transform geographic space into a grid cell with a hierarchical structure. Each cell corresponds to a unique code identifier, supporting fast indexing and querying of spatial location. The obstacle grid referred to in this disclosure refers to a grid cell generated based on multi-source data such as terrain and oblique photogrammetry models, and an independent grid index system can be built for it to characterize the distribution of physical obstacles in the low-altitude environment.

[0052] Furthermore, obstacle meshes are a fundamental data structure for spatial modeling and are also used to characterize physical obstacles that affect signal propagation in low-altitude environments.

[0053] In some embodiments, multi-source data can be input according to the user's actual needs, thereby integrating multi-source data.

[0054] In some embodiments, the aforementioned multi-source data includes at least two of the following: digital surface model (DSM), digital elevation model (DEM), oblique photogrammetry model, building profile data, and land cover model.

[0055] In some embodiments, during the construction of the obstacle grid set, the airspace range of a specified geographical area can be set according to the user's actual needs based on the combination of multi-source data, such as setting the airspace range to 0 to 600 meters.

[0056] In some embodiments, the above-mentioned acquisition of multi-source data within the area to be processed and construction of an obstacle mesh set specifically includes:

[0057] Acquire multi-source data within the area to be processed;

[0058] Based on a preset geographic coordinate system, multi-source data are uniformly registered;

[0059] Based on preset grid parameters, and according to the registered multi-source data, the spatial domain is discretized into regular three-dimensional grid cells in the horizontal and vertical directions. Each cell includes latitude and longitude, altitude, and shading attribute identifiers.

[0060] Based on the occlusion attribute identifiers of each unit, a grid index structure is established, and an obstacle grid set is output.

[0061] In some embodiments, a preset geographic coordinate system can be set according to the user's actual needs so as to uniformly register multi-source data to the same geographic coordinate system, thereby enabling multi-source data to have unified coordinates and aligned resolutions.

[0062] In some embodiments, preset grid parameters can be defined according to the user's actual needs, and preset grid parameters may include vertical / horizontal resolution and height range.

[0063] In some embodiments, the spatial domain can be discretized into regular three-dimensional grid cells in the horizontal and vertical directions, each cell containing latitude and longitude, altitude, and attribute identifiers. For example, for obstacle areas, such as buildings or mountains, a "shading" attribute is assigned, i.e., obstacle attributes are marked, and a grid index structure is established to build a spatial index for the obstacle grid set, so as to output an indexed obstacle grid model, thereby supporting efficient spatial queries.

[0064] In summary, this disclosure proposes a method for constructing obstacle grids by integrating multi-source geospatial data, including digital elevation models, oblique photogrammetry models, and building structure data. This grid is based on global standard grid coding systems, such as the Geographic Coordinate Subdividing Grid with One Dimension Integral Coding on 2n-Tree (GeoSOT) and the Geodesic Discrete Global Grid Systems (G-DGGS), to uniformly divide and identify the spatial environment. Each grid cell is assigned a terrain shading type and height attribute, while an efficient spatial indexing mechanism is established.

[0065] It should be noted that once the obstacle mesh is constructed, it can be reused throughout the entire calculation process to avoid multiple data loading and preprocessing. This enables the obstacle data to be constructed once and reused multiple times, effectively avoiding redundant calculations and providing basic data support for large-scale spatial domain analysis, thereby significantly improving the efficiency of subsequent analysis.

[0066] Furthermore, this disclosure enables one-time processing and multiple calls to geographic environmental data by pre-constructing a reusable standardized obstacle grid and establishing an efficient spatial index for the grid cells. This fundamentally avoids the repeated loading and calculation of geographic information for the same area found in many current methods, making it particularly suitable for large-scale spatial domain analysis scenarios. It greatly reduces computational load and time overhead, thus significantly improving computational efficiency and avoiding redundant overhead.

[0067] In box 120, obtain the parameters of the UAV detection equipment in the area to be processed, and construct the equipment envelope mesh set.

[0068] In some embodiments, the device envelope refers to a three-dimensional spatial range centered on the detection device and determined according to its detection performance parameters, such as maximum effective distance and field of view. This disclosure discretizes the device envelope into a set of grid cells, i.e., the device envelope grid set, so that it can be overlaid with the obstacle grid for analysis, thereby supporting subsequent occlusion judgment and performance calculation.

[0069] Specifically, corresponding to the obstacle mesh set mentioned above, the device envelope is also discretized into a three-dimensional mesh set. Each mesh represents a spatial unit that the device can potentially cover. To achieve efficient management, each device envelope mesh can be assigned a device ID and performance parameter attributes, and a spatial index can be established so that it can be quickly overlaid and analyzed with the obstacle mesh and other device envelopes.

[0070] In some embodiments, the above-mentioned acquisition of UAV detection device parameters within the area to be processed and construction of device envelope mesh set specifically includes:

[0071] Obtain the parameters of the UAV detection equipment in the area to be processed. The equipment parameters include location, maximum detection range, azimuth angle, pitch angle, and equipment model.

[0072] The envelope range of each device is calculated based on the device parameters, and the envelope range is discretized into three-dimensional mesh cells under the same spatial reference as the obstacle mesh set. Each cell includes mesh cell attributes such as device ID, distance to the device, and signal strength.

[0073] Based on the device ID of each unit, a grid index structure is established, and the device envelope grid set is output.

[0074] In summary, this disclosure discretizes the detection range, i.e., the envelope, of the detection device based on its performance parameters, including detection distance, elevation angle, and azimuth angle, into a refined grid representation, forming device-related performance calculation units. This envelope grid not only characterizes the theoretical coverage range of the device but also possesses attributes such as device ID and performance attenuation coefficient, supporting efficient spatial correlation analysis with obstacle grids and laying the foundation for subsequent occlusion judgment and performance fusion.

[0075] In some embodiments, combining the obstacle grid construction based on grid coding and multi-source data with the performance grid construction based on device envelopes, this disclosure performs integrated fusion calculations based on the aforementioned multi-source geographic data to construct a multi-level, detailed, and unified spatial obstacle grid model, and establishes an efficient grid index to support rapid retrieval and access. Based on this, the coverage capability of the detection device is discretized into a device envelope grid. Through efficient overlay analysis with the obstacle grid, subsequent fusion occlusion judgment and accurate performance evaluation of multi-device collaboration are achieved. The above method, through gridded data fusion and computation, significantly reduces redundant computation and resource overhead, effectively overcoming the computational bottleneck in the planning of UAV detection equipment in large-scale scenarios, and providing reliable support for efficient airspace management and optimized deployment.

[0076] It should also be noted that this disclosure, through device envelope meshing and overlay analysis with obstacle meshes, can accurately characterize the true effective coverage area of ​​a single device and its detection performance at different spatial locations. More importantly, this method inherently supports the identification and fusion calculation of overlapping coverage areas of multiple devices, thereby quantitatively evaluating the synergistic effect between devices and significantly improving the accuracy and reliability of overall performance evaluation.

[0077] In box 130, based on the obstacle mesh set and the device envelope mesh set, the visibility state of each device envelope mesh cell is determined, and the corresponding visibility mesh set for each device is generated.

[0078] In some embodiments, occlusion analysis, also known as line-of-sight analysis or field-of-sight analysis, is a spatial calculation method for determining whether there is an unobstructed line-of-sight path from the location of the detection device to a target grid cell. This process detects whether the line of sight is blocked by intermediate obstacles, such as terrain undulations or buildings, thus determining the visibility status of each device's envelope grid cell. This is a key step in this disclosure for evaluating the actual visibility range of the device.

[0079] In some embodiments, this disclosure employs an efficient algorithm for spatial overlay analysis based on device envelope mesh and obstacle mesh to achieve rapid identification of the device's visible area. Simultaneously, by performing occlusion analysis, unoccluded meshes are calculated, allowing for subsequent performance evaluation algorithms to assess their performance. This effectively avoids redundant calculations of device performance, significantly improving computational efficiency and meeting the site selection requirements for large-scale UAV detection equipment deployment.

[0080] For example, by using the obstacle grid index, the visibility of the device envelope can be quickly retrieved, the device envelope grid set can be traversed, and it can be determined whether the line of sight from the location of the detection device to the target grid is blocked by the obstacle grid. If so, the target grid is marked as "invisible", and if not, the target grid is marked as "visible".

[0081] In some embodiments, the determination of the visibility state of each device envelope grid cell based on the obstacle grid set and the device envelope grid set, and the generation of the visibility grid set corresponding to each device, specifically includes:

[0082] Based on the obstacle mesh set, the device envelope mesh set is searched to determine whether the line of sight from the device to the envelope mesh unit is blocked by the obstacle mesh unit.

[0083] If yes, then mark the visibility state of the envelope mesh cell as invisible; otherwise, mark the visibility state of the envelope mesh cell as visible, and generate the visible mesh set corresponding to the device.

[0084] In summary, based on the constructed obstacle grid spatial index, all potential obstacles between the device location and each target envelope grid cell can be quickly retrieved. Then, it can be determined whether the direct line of sight from the device to the target grid is blocked by the obstacle grid. Finally, the visibility status of each device envelope grid cell, i.e., visible or invisible, is output, forming the visible grid set corresponding to the device, providing accurate spatial visibility data for subsequent performance evaluation.

[0085] In box 140, the device performance is calculated based on the visual grid set of each device using a preset performance evaluation algorithm.

[0086] In some embodiments, device performance refers to the quantitative indicator of a detection device's ability to detect targets, such as drones, at a specific location. It typically considers the probability or capability value of various factors, including device performance such as detection distance, detection angle, resolution, distance attenuation, environmental occlusion, and weather conditions. This disclosure constructs a performance evaluation module to quantitatively calculate the detection capability of each device within its visible grid based on the results of occlusion analysis.

[0087] like Figure 2 As shown, the preset performance evaluation algorithm in the performance evaluation module can be set according to the user's actual needs, taking into account that the performance value of the device on a certain grid cell is affected by a variety of factors.

[0088] In some embodiments, the construction of the aforementioned preset performance evaluation algorithm specifically includes:

[0089] A preset performance evaluation algorithm is constructed based on distance attenuation, equipment performance parameters, environmental parameters, and visibility coefficient. Among them, equipment performance parameters include detection distance, sensitivity, signal-to-noise ratio, and viewing angle, while environmental parameters include meteorological conditions and electromagnetic noise.

[0090] Among them, distance attenuation is determined based on the attenuation of signal strength as distance increases, and visibility coefficient is determined based on the visibility status of the target grid under obstacle occlusion.

[0091] In box 150, based on a preset merging algorithm, the visible grid sets and device performance of all devices are merged to generate a spatial summation device performance grid set within the area to be processed.

[0092] In some embodiments, based on a preset merging algorithm, the grid and performance of all devices within the coverage area of ​​the processing area are calculated and merged to finally form the device performance of all spatial grids within the processing area.

[0093] like Figure 3 As shown, the method for calculating the performance of UAV detection equipment based on airspace grids can also be used as a way to output a total airspace equipment performance grid set, including an input layer, a core processing layer, and an output layer. Specifically, the input layer includes: multi-source geospatial data, detection equipment parameters, and environmental parameters; the core processing layer includes: an obstacle grid construction module, an equipment envelope construction module, an occlusion analysis module, a single equipment performance evaluation module, and a multi-equipment performance fusion module; the output layer includes: a total airspace performance grid.

[0094] In the core processing layer, the obstacle mesh construction module receives multi-dimensional geospatial data sent from the input layer; the device envelope construction module receives detection device parameters sent from the input layer and constructs a device envelope network based on the device performance processing pipeline; the occlusion analysis module receives the obstacle network constructed by the obstacle mesh construction module and the device envelope network constructed by the device envelope construction module, and constructs a visual network set; the single device performance evaluation module receives the visual network set and outputs a single device performance network; and the multi-device performance fusion module receives the single device performance network and performs fusion output.

[0095] In some embodiments, the aforementioned preset merging algorithm specifically includes:

[0096] Weighted fusion algorithm, probabilistic merging algorithm, or maximum value synthesis algorithm.

[0097] In some embodiments, the preset merging algorithm may be an optimization strategy adopted according to the user's actual needs.

[0098] In summary, this disclosure proposes an efficient method for device performance evaluation and fusion calculation based on obstacle mesh and device envelope overlay analysis. It also proposes a performance evaluation model that integrates occlusion analysis and multi-device collaborative computation. By overlaying and analyzing device envelope meshes and obstacle meshes, it achieves rapid visibility assessment and performance value calculation. Furthermore, it employs optimization strategies, such as weighted fusion, probability merging, or maximum value synthesis, for overlapping areas covered by multiple devices, ultimately outputting the comprehensive performance distribution across the entire spatial domain. This method significantly improves computational efficiency and analytical accuracy, and is suitable for performance evaluation and optimized deployment in high-density, large-scale detection device deployment scenarios.

[0099] Specifically, by constructing a unified obstacle mesh, various factors affecting detection performance within the airspace are stored and managed in an integrated manner, enabling one-time construction and multiple reuses, thereby effectively reducing redundant calculations. Simultaneously, by constructing a device envelope mesh, the spatial relationships between devices are explicitly expressed, and performance calculations are performed only for devices with optimal influence distances within the overlay mesh, avoiding redundant evaluations. Finally, through the overlay analysis and fusion calculation of the device envelope mesh and the obstacle mesh, a scientific basis is provided for the site selection and deployment of high-density detection equipment, meeting the high-performance computing needs of future low-altitude UAV intensive detection.

[0100] It should also be noted that this disclosure provides a general and efficient digital analysis framework. Its gridded model and fusion algorithm can quickly simulate and compare the overall detection performance under different equipment deployment schemes, thus providing a quantitative decision-making basis for the scientific site selection and layout optimization of high-density, large-scale detection networks. The computational complexity of this method does not increase dramatically with the number of devices, possessing good scalability and adapting to the ever-growing demand for low-altitude monitoring in the future.

[0101] According to the embodiments of this disclosure, the following technical effects are achieved:

[0102] This system can construct an obstacle grid set by acquiring multi-source data within the processing area; construct an equipment envelope grid set by acquiring parameters of UAV detection equipment within the processing area; determine the visibility status of each equipment envelope grid unit based on the obstacle grid set and equipment envelope grid set, and generate a corresponding visible grid set for each equipment; calculate equipment performance based on the visible grid set of each equipment using a preset performance evaluation algorithm; and merge the visible grid sets and equipment performance of all equipment using a preset merging algorithm to generate a total airspace equipment performance grid set within the processing area. Based on this, by constructing a grid of multi-source data within the low-altitude region and utilizing a grid caching mechanism to achieve equipment performance analysis and coverage evaluation, it can solve the problems of performance analysis and efficient fusion calculation in the management of low-altitude UAV detection equipment, effectively avoiding redundant calculations and breaking through the performance bottleneck in airspace management. This supports accurate evaluation of the coverage effect and overall performance of widely deployed detection equipment within the low-altitude region, providing a more comprehensive and scientific decision-making basis for equipment site selection and offering an efficient computational solution for large-scale detection equipment site selection.

[0103] 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.

[0104] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0105] Figure 4 A block diagram of a UAV detection device performance calculation apparatus 400 based on a spatial grid according to an embodiment of the present disclosure is shown. Figure 4 As shown, the device 400 includes:

[0106] Module 410 is used to acquire multi-source data within the area to be processed and construct an obstacle mesh set;

[0107] Module 410 is also used to acquire parameters of UAV detection devices within the area to be processed and to construct a device envelope mesh set;

[0108] The generation module 420 is used to determine the visibility status of each device envelope grid cell based on the obstacle grid set and the device envelope grid set, and to generate the corresponding visible grid set for each device.

[0109] Calculation module 430 is used to calculate device performance based on the visual grid set of each device using a preset performance evaluation algorithm;

[0110] The merging module 440 is used to merge the visual grid sets and device performance of all devices based on a preset merging algorithm, and generate a spatial summation device performance grid set within the area to be processed.

[0111] 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.

[0112] 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.

[0113] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0114] Figure 5 A block diagram of an exemplary electronic device 500 capable of implementing 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.

[0115] Electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in ROM 502 or a computer program loaded into RAM 503 from storage unit 508. RAM 503 can also store various programs and data required for the operation of electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. I / O interface 505 is also connected to bus 504.

[0116] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0117] Computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of computing unit 501 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. Computing unit 501 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 508.

[0118] In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by computing unit 501, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, computing unit 501 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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 disclosed in this disclosure can be achieved, and this is not limited herein.

[0126] 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 method for calculating the performance of UAV detection equipment based on a spatial grid, characterized in that, include: Acquire multi-source data within the area to be processed and construct an obstacle mesh set; Obtain the parameters of UAV detection devices within the area to be processed, and construct a device envelope mesh set; The step of acquiring UAV detection device parameters within the processing area and constructing a device envelope mesh set includes: acquiring UAV detection device parameters within the processing area, wherein the device parameters include location, maximum detection range, azimuth angle, pitch angle, and device model; calculating the envelope range of each device based on the device parameters, and discretizing the envelope range into three-dimensional mesh cells under the same spatial reference as the obstacle mesh set, wherein each cell includes device ID, distance to device, and signal strength; establishing a mesh index structure based on the device ID marked in each cell, and outputting the device envelope mesh set; Based on the obstacle mesh set and the device envelope mesh set, determine the visibility state of each device envelope mesh unit and generate a visible mesh set corresponding to each device; The device performance is calculated based on the visible grid set of each device using a preset performance evaluation algorithm. The construction of the preset performance evaluation algorithm includes: constructing a preset performance evaluation algorithm based on distance attenuation, device performance parameters, environmental parameters, and visibility coefficient. The device performance parameters include detection distance, sensitivity, signal-to-noise ratio, viewing angle, and distance attenuation, and the environmental parameters include meteorological conditions and electromagnetic noise. Based on a preset merging algorithm, the visible grid sets and device performance of all devices are merged to generate a spatial summation device performance grid set within the area to be processed.

2. The method according to claim 1, characterized in that, The step of acquiring multi-source data within the area to be processed and constructing an obstacle mesh set includes: Acquire multi-source data within the area to be processed; Based on a preset geographic coordinate system, the multi-source data are uniformly registered; Based on preset grid parameters, and according to the registered multi-source data, the spatial domain is discretized into regular three-dimensional grid cells in the horizontal and vertical directions. Each cell includes latitude and longitude, altitude, and shading attribute identifiers. Based on the occlusion attribute identifiers of each unit, a grid index structure is established, and an obstacle grid set is output.

3. The method according to claim 1, characterized in that, The step of determining the visibility state of each device envelope grid cell based on the obstacle grid set and the device envelope grid set, and generating the corresponding visibility grid set for each device, includes: Based on the obstacle grid set, the device envelope grid set is searched to determine whether the line of sight from the device to the envelope grid unit is blocked by the obstacle grid unit. If yes, then mark the visibility state of the envelope mesh cell as invisible; otherwise, mark the visibility state of the envelope mesh cell as visible, and generate the visible mesh set corresponding to the device.

4. The method according to any one of claims 1 to 3, characterized in that, The preset merging algorithm includes: Weighted fusion algorithm, probabilistic merging algorithm, or maximum value synthesis algorithm.

5. The method according to any one of claims 1 to 3, characterized in that, The multi-source data includes at least two of the following: digital surface model, digital elevation model, oblique photogrammetry model, building outline data, and land cover model.

6. A performance calculation device for UAV detection equipment based on a spatial grid, characterized in that, include: The module is used to acquire multi-source data within the area to be processed and construct an obstacle mesh set; The construction module is also used to acquire parameters of UAV detection devices within the area to be processed and construct a device envelope mesh set; the construction module is also specifically used to: acquire parameters of UAV detection devices within the area to be processed, the device parameters including position, maximum detection distance, azimuth angle, pitch angle and device model; calculate the envelope range of each device according to the device parameters, and discretize the envelope range into three-dimensional mesh cells under the same spatial reference as the obstacle mesh set, each cell including device ID, distance to device and signal strength; Based on the device ID of each unit, a grid index structure is established, and the device envelope grid set is output. The generation module is used to determine the visibility status of each device envelope grid cell based on the obstacle grid set and the device envelope grid set, and to generate a visible grid set corresponding to each device. The calculation module is used to calculate the device performance based on the visual grid set of each device using a preset performance evaluation algorithm; The construction of the preset performance evaluation algorithm includes: constructing a preset performance evaluation algorithm based on distance attenuation, equipment performance parameters, environmental parameters and visibility coefficient; wherein, the equipment performance parameters include detection distance, sensitivity, signal-to-noise ratio, viewing angle and distance attenuation, and the environmental parameters include meteorological conditions and electromagnetic noise; The merging module is used to merge the visual grid sets and device performance of all devices based on a preset merging algorithm, and generate a spatial summation device performance grid set within the area to be processed.

7. An electronic device, characterized in that, include: At least one processor; as well as A memory that is communicatively connected to the at least one processor; 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 1-5.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

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