Gas gun operation site arrangement optimization method and system based on GIS

Abstract

The application belongs to the technical field of gas gun operation, and particularly relates to a GIS-based gas gun operation point distribution optimization method and system. The method determines the influence propagation relationship based on terrain feature data and meteorological environment data, performs grid division on an operation area, performs safety constraint evaluation and effective candidate point distribution screening according to protection target distribution data, calculates the coverage overlap area and the blank area through spatial overlay analysis, dynamically adjusts the point distribution position and the number, and finally performs reachability checking and repositioning of the point distribution that does not meet the conditions. The application improves the gas gun operation efficiency, ensures the operation safety, and optimizes the resource allocation.

Description

Technical Field

[0001] This invention relates to the field of gas cannon operation technology, and in particular to a GIS-based method and system for optimizing the deployment of gas cannon operations. Background Technology

[0002] Gas cannon operations are a commonly used weather modification technique in meteorological hail suppression and environmental protection. They utilize the shockwaves generated by gas cannons to disrupt hail clouds or disperse air pollutants. During gas cannon operations, a well-planned deployment strategy is crucial for improving operational effectiveness, expanding the protected area, and ensuring operational safety. Traditionally, gas cannon deployment relied heavily on meteorological personnel experience and simple grid-based methods. However, with the development of Geographic Information Systems (GIS) technology, combining GIS with optimized gas cannon deployment has become a technological trend.

[0003] Currently, the main methods for deploying gas cannons include grid deployment, target-oriented deployment, and multi-constraint optimization. Grid deployment divides the operational area into equally spaced grids and sets deployment points at grid intersections. Target-oriented deployment determines deployment points based on the location and morphological characteristics of underground targets. Multi-constraint optimization considers factors such as terrain, safety distance, and operational effectiveness to optimize deployment. These methods have achieved some success in practical applications, but with the increasing complexity of operational areas and stricter safety and environmental requirements, existing technologies still have many shortcomings. Existing deployment methods fail to fully consider the impact of terrain features and meteorological conditions on the propagation of operational effects, leading to significant differences between the actual coverage effect and theoretical expectations under complex terrain conditions. The safety constraint assessment process does not comprehensively consider the protected targets and lacks differentiated safety distance settings for different types of protected targets, increasing operational risks. The deployment optimization process lacks a systematic assessment of terrain accessibility, resulting in some theoretically optimal locations being inaccessible in actual operations due to terrain barriers, requiring on-site adjustments and reducing the practicality and efficiency of the deployment scheme. Summary of the Invention

[0004] This invention provides a GIS-based method and system for optimizing the deployment of gas cannon operations, which can solve the problems in the prior art.

[0005] A first aspect of this invention provides a GIS-based method for optimizing the deployment of gas cannon operations, comprising:

[0006] Based on the terrain feature data and meteorological environment data of the work area, the propagation relationship of the operation's impact is determined. The work area is then spatially discretized using this propagation relationship, dividing it into multiple grids. The impact coverage area of ​​each grid as a candidate placement location is then calculated.

[0007] Based on the distribution data of protected targets in the work area, a safety constraint assessment is performed on the influence coverage area of ​​each candidate deployment point, and a set of valid candidate deployment points that meet the safety distance constraints is selected.

[0008] The effective candidate point placement set is iteratively solved based on the task requirement parameters. In each iteration, the overlapping and blank areas of the current point placement scheme are calculated through spatial overlay analysis. The point placement positions and numbers are dynamically adjusted according to the overlapping and blank areas to obtain the initial optimized point placement scheme.

[0009] Based on the terrain feature data, the accessibility of each point in the initial optimized point layout scheme is checked to obtain the point locations with terrain barriers, and the point locations with terrain barriers are relocated to nearby locations that meet the accessibility requirements to generate the target optimized point layout scheme.

[0010] Based on terrain feature data and meteorological environment data of the work area, the propagation relationship of the work impact is determined. Using this propagation relationship, the work area is spatially discretized into multiple grids. The impact coverage area of ​​each grid as a candidate placement location is calculated, including:

[0011] The surface elevation and slope information in the terrain feature data are determined, and the three-dimensional terrain surface of the work area is determined based on the surface elevation and slope information. The initial attenuation direction of the shock wave propagation is determined along the normal direction of the three-dimensional terrain surface.

[0012] The wind direction and wind speed information in the meteorological environment data are determined. Based on the wind direction and wind speed information, the offset influence of the airflow on the propagation path of the shock wave is calculated. Based on the offset influence, the initial attenuation direction is corrected to obtain the actual propagation direction of the shock wave in three-dimensional space.

[0013] Based on the actual propagation direction and the wind speed information, the attenuation rate of the shock wave intensity with propagation distance is calculated to obtain the propagation relationship of the operation's impact;

[0014] The work area is divided into multiple grids according to a preset grid spacing. Taking the center coordinates of each grid as the starting point, the intensity distribution of the shock wave at different locations is calculated based on the propagation relationship of the work influence. The spatial range in which the shock wave intensity meets the work intensity requirements is determined as the influence coverage area when the grid is used as a candidate placement location.

[0015] Based on the distribution data of protected targets in the work area, a safety constraint assessment is performed on the influence coverage area of ​​each candidate deployment point, and a set of valid candidate deployment points that meet the safety distance constraints is selected, including:

[0016] Based on the distribution data of the protected targets, the location coordinates and protection level information of the protected targets are determined, and the minimum safe distance requirement corresponding to each protected target is determined according to the protection level information.

[0017] For each candidate deployment location, the spatial relationship between the influence coverage area of ​​the candidate deployment location and the location coordinates of the protected target is determined, and the protected targets located within the influence coverage area are identified, thus obtaining the set of protected targets affected by the candidate deployment location;

[0018] Calculate the spatial distance between the candidate placement locations and each protected target in the set of affected protected targets. Based on the spatial distance and the minimum safety distance requirement corresponding to the protected target, when all spatial distances are greater than or equal to the minimum safety distance requirement, mark the candidate placement locations as valid candidate placement locations that satisfy the safety constraints.

[0019] Traverse all candidate placement locations and determine the set of valid candidate placements based on all valid candidate placements that satisfy the safety constraints.

[0020] Based on the job requirement parameters, the effective candidate deployment point set is iteratively solved. In each iteration, the overlapping and blank coverage areas of the current deployment scheme are calculated through spatial overlay analysis. The deployment point positions and numbers are dynamically adjusted according to the overlapping and blank coverage areas to obtain an initial optimized deployment scheme, including:

[0021] Select an initial combination of points from the set of valid candidate points, and obtain the influence coverage area corresponding to each point position in the initial combination of points;

[0022] The influence coverage areas of all locations in the initial point layout combination are spatially superimposed to identify the spatial areas where multiple influence coverage areas overlap as coverage overlap areas, and the spatial areas within the work area that are not covered by any influence coverage area are identified as coverage blank areas.

[0023] Based on the coverage blank area, candidate point positions located within the coverage blank area are selected from the set of valid candidate points and added to the initial point combination; based on the coverage overlap area, point positions located within the coverage overlap area are removed from the initial point combination to form an updated point combination.

[0024] Based on the updated point combination, the spatial overlay processing and point position adjustment operations are repeated until the area of ​​the covered blank area and the area of ​​the covered overlapping area meet the convergence condition. The point combination that meets the convergence condition is determined as the initial optimized point combination.

[0025] The influence coverage areas of all locations in the initial point layout combination are spatially superimposed to identify overlapping spatial areas as coverage overlap areas, and spatial areas within the work area not covered by any influence coverage area are identified as coverage blank areas, including:

[0026] The influence coverage domain of all point positions in the initial point combination is converted into a vector space object to obtain a spatial index structure;

[0027] Based on the spatial index structure, the spatial topological relationship of the influence coverage domain of each point location in the initial point combination is determined, other influence coverage domains that have spatial intersection with the current influence coverage domain are obtained, and the spatial intersection matrix between influence coverage domains is determined;

[0028] Based on the spatial intersection matrix, a geometric intersection operation is performed on the influence coverage regions that have spatial intersection relationships to obtain the intersection regions between each influence coverage region, and the number of influence coverage regions corresponding to each intersection region is recorded;

[0029] The overlapping regions with more than a preset threshold number of affected coverage areas are spatially merged to obtain the overlapping coverage areas.

[0030] The total coverage area is obtained by performing a spatial union operation on the influence coverage areas of all locations in the initial layout combination. The spatial difference operation is then performed between the spatial range of the work area and the total coverage area to obtain the remaining uncovered spatial area as the coverage blank area.

[0031] Based on the terrain feature data, the accessibility of each point in the initial optimized point layout scheme is checked to identify points with terrain barriers. These points are then relocated to nearby locations that meet accessibility requirements, generating a target optimized point layout scheme, including:

[0032] Based on the terrain feature data, road network data and surface elevation data are determined;

[0033] Based on the road network data, the locations of points in the initial optimized point layout scheme that are blocked by roads are determined, and based on the surface elevation data, the locations of points in the initial optimized point layout scheme that are blocked by steep terrain slopes are determined.

[0034] Based on the locations of points obstructed by roads and the locations of points obstructed by steep terrain slopes, a set of locations of points obstructed by terrain is obtained;

[0035] For each location in the set of locations with terrain barriers, a candidate relocation location is searched within a preset range that satisfies path connectivity and whose terrain slope change does not exceed a preset slope threshold.

[0036] Select the position that is closest in space to the original placement position from the candidate relocation positions as the relocation target position, move the placement position to the relocation target position, update the initial optimized placement scheme based on the placement position after relocation, and generate the target optimized placement scheme.

[0037] Based on the road network data, the locations of points in the initial optimized point layout scheme that are obstructed by roads are determined, and based on the surface elevation data, the locations of points in the initial optimized point layout scheme that are obstructed by steep terrain slopes are determined, including:

[0038] Based on the road network data, determine the coordinates of road nodes and the connection relationships of road segments, and generate a road connectivity graph based on the coordinates of road nodes and the connection relationships of road segments;

[0039] Map the preset transportation starting point and each location in the initial optimized point layout scheme to the corresponding road node in the road connectivity graph;

[0040] For each deployment location, a path search is performed on the road node corresponding to the preset transportation starting point in the road connectivity map to determine whether a connecting path exists. If no connecting path exists, the corresponding deployment location is determined as a deployment location with road obstruction.

[0041] Based on the surface elevation data, the elevation values ​​of each spatial location within the work area are determined. For each location in the initial optimized layout scheme, multiple sampling points are extracted along a straight path from the preset transportation starting point to the location at a preset sampling interval. The elevation value of each sampling point is obtained, and the slope value is calculated based on the elevation difference and horizontal distance between adjacent sampling points.

[0042] When the slope value exceeds the preset slope threshold, the corresponding location of the sampling point is determined as a location where there is a steep slope barrier.

[0043] A second aspect of the present invention provides a GIS-based gas cannon operation deployment optimization system, comprising:

[0044] The first unit is used to determine the propagation relationship of operational impacts based on terrain feature data and meteorological environment data of the operational area, and to spatially discretize the operational area using the operational impact propagation relationship, dividing the operational area into multiple grids, and calculating the impact coverage area of ​​each grid as a candidate placement location;

[0045] The second unit is used to evaluate the safety constraints of the influence coverage area of ​​each candidate deployment point based on the distribution data of the protected targets in the work area, and to select a set of valid candidate deployment points that meet the safety distance constraints.

[0046] The third unit is used to iteratively solve the set of effective candidate deployment points based on the job requirement parameters. In each iteration, the overlapping area and the blank area of ​​the current deployment scheme are calculated through spatial overlay analysis. The deployment point positions and numbers are dynamically adjusted according to the overlapping area and the blank area to obtain the initial optimized deployment scheme.

[0047] The fourth unit is used to perform accessibility verification on each location in the initial optimized point layout scheme based on the terrain feature data, obtain the location of the point with terrain obstruction, and relocate the location of the point with terrain obstruction to a nearby location that meets the accessibility requirements, thereby generating the target optimized point layout scheme.

[0048] A third aspect of the present invention provides an electronic device, comprising:

[0049] processor;

[0050] Memory used to store processor-executable instructions;

[0051] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0052] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0053] This invention determines the propagation relationship of operational impacts based on terrain feature data and meteorological environmental data of the work area, and performs spatial discretization processing to achieve accurate prediction of the operational impact range in complex environments, thus improving the scientific nature of the site selection plan. This invention introduces protected target distribution data for safety constraint assessment, effectively screening candidate site locations that meet safety distance requirements, ensuring the safety of gas cannon operations and reducing potential risks to the surrounding environment. This invention uses spatial overlay analysis during the iterative solution process to dynamically calculate overlapping and blank areas of coverage, and adjusts the site locations and quantities accordingly, achieving optimized allocation of operational resources, improving operational efficiency, and reducing operational costs. This invention innovatively introduces a terrain accessibility verification step, promptly identifying and relocating site locations with terrain barriers, ensuring the practical operability of the optimized plan and avoiding site selection failures caused by neglecting terrain factors in traditional methods. This invention deeply integrates GIS technology with the characteristics of gas cannon operations, forming a complete site selection optimization technology system, which can significantly improve operational coverage and resource utilization compared to traditional experience-based site selection methods. Attached Figure Description

[0054] Figure 1This is a flowchart illustrating the GIS-based method for optimizing the deployment of gas cannons according to an embodiment of the present invention.

[0055] Figure 2 This is a flowchart illustrating the target optimization layout scheme in an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0058] Figure 1 This is a flowchart illustrating the GIS-based method for optimizing the deployment of gas cannons according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0059] Based on the terrain feature data and meteorological environment data of the work area, the propagation relationship of the operation's impact is determined. The work area is then spatially discretized using this propagation relationship, dividing it into multiple grids. The impact coverage area of ​​each grid as a candidate placement location is then calculated.

[0060] Based on the distribution data of protected targets in the work area, a safety constraint assessment is performed on the influence coverage area of ​​each candidate deployment point, and a set of valid candidate deployment points that meet the safety distance constraints is selected.

[0061] The effective candidate point placement set is iteratively solved based on the task requirement parameters. In each iteration, the overlapping and blank areas of the current point placement scheme are calculated through spatial overlay analysis. The point placement positions and numbers are dynamically adjusted according to the overlapping and blank areas to obtain the initial optimized point placement scheme.

[0062] Based on the terrain feature data, the accessibility of each point in the initial optimized point layout scheme is checked to obtain the point locations with terrain barriers, and the point locations with terrain barriers are relocated to nearby locations that meet the accessibility requirements to generate the target optimized point layout scheme.

[0063] In one optional implementation, based on terrain feature data and meteorological environment data of the work area, the propagation relationship of the work impact is determined. This propagation relationship is then used to spatially discretize the work area, dividing it into multiple grids. The impact coverage area of ​​each grid as a candidate placement location is calculated, including:

[0064] The surface elevation and slope information in the terrain feature data are determined, and the three-dimensional terrain surface of the work area is determined based on the surface elevation and slope information. The initial attenuation direction of the shock wave propagation is determined along the normal direction of the three-dimensional terrain surface.

[0065] The wind direction and wind speed information in the meteorological environment data are determined. Based on the wind direction and wind speed information, the offset influence of the airflow on the propagation path of the shock wave is calculated. Based on the offset influence, the initial attenuation direction is corrected to obtain the actual propagation direction of the shock wave in three-dimensional space.

[0066] Based on the actual propagation direction and the wind speed information, the attenuation rate of the shock wave intensity with propagation distance is calculated to obtain the propagation relationship of the operation's impact;

[0067] The work area is divided into multiple grids according to a preset grid spacing. Taking the center coordinates of each grid as the starting point, the intensity distribution of the shock wave at different locations is calculated based on the propagation relationship of the work influence. The spatial range in which the shock wave intensity meets the work intensity requirements is determined as the influence coverage area when the grid is used as a candidate placement location.

[0068] To conduct operations in a reasonable manner within the work area, it is necessary to determine the propagation relationship of the operation's impact based on terrain feature data and meteorological environment data, spatially discretize the work area, divide it into grids, and calculate the impact coverage area of ​​each grid as a candidate placement location.

[0069] First, topographic feature data of the work area is acquired, including surface elevation and slope information. Surface elevation information is usually represented in the form of a digital elevation model (DEM), which records the altitude value of each sampling point in the area; slope information represents the degree of surface inclination and can be calculated from the rate of change of height between adjacent elevation points. Based on this data, a three-dimensional topographic surface model of the work area is constructed, using triangular meshes or regular meshes to represent the surface morphology.

[0070] For each point on the three-dimensional terrain surface, calculate its normal vector direction. The normal vector can be calculated using the cross product operation of adjacent grid points: For a point P on the terrain surface, select its neighboring points P1 and P2 to form vectors V1 = P1 - P and V2 = P2 - P. Then the normal vector of this point is N = V1 × V2 (cross product). The normal vector direction is the initial attenuation direction of the shock wave propagation, indicating that, without considering other factors, the shock wave energy attenuates fastest along the direction perpendicular to the ground surface.

[0071] Next, wind direction and speed information are obtained from meteorological environmental data. Wind direction is usually expressed in angles, such as 0 degrees for due north and 90 degrees for due east; wind speed is measured in meters per second. The calculation of the impact of airflow on the deviation of the shock wave propagation path needs to consider the vector sum of wind speed and shock wave propagation speed. Let the propagation speed of the shock wave under windless conditions be Vw, and the wind speed be Va. Then the actual propagation speed V of the shock wave is the vector sum of the two: V = Vw + Va.

[0072] The initial attenuation direction vector is vector-synthesized with the wind direction and wind speed influence vectors to obtain the corrected actual propagation direction of the shock wave. Specifically, let the unit vector of the initial attenuation direction be D0, the unit vector of the wind direction be Dw, the wind speed be |Va|, and the shock wave propagation speed be |Vw|. Then the corrected propagation direction vector D is calculated as: D = D0 + Dw × (|Va| / |Vw| × k), where k is the correction coefficient, which is determined according to the actual shock wave characteristics and meteorological conditions.

[0073] Based on the actual propagation direction and wind speed information, the attenuation rate of the shock wave intensity with propagation distance is calculated. The attenuation model adopts the exponential attenuation formula: I(r) = I0 × e^(-α × r), where I(r) is the shock wave intensity at a distance r from the propagation source, I0 is the source intensity, and α is the attenuation coefficient. The attenuation coefficient α is positively correlated with the wind speed; the higher the wind speed, the faster the attenuation. Simultaneously, α is also related to the angle between the propagation direction and the wind direction. When the shock wave propagation direction is consistent with the wind direction, the attenuation is slower; when the two directions are opposite, the attenuation is faster.

[0074] The propagation direction and attenuation rate calculated above are combined to form the propagation relationship of the operation influence. This relationship describes the intensity distribution of the shock wave originating from any source point at various points in the surrounding space.

[0075] When spatially discretizing the work area, the area is divided into regular grids according to a preset grid spacing (such as 10 meters or 20 meters). The choice of grid size needs to balance computational accuracy and efficiency; the smaller the grid, the higher the accuracy, but the greater the computational load. For each grid, its center point is used as the coordinates for potential point locations.

[0076] Starting from the center point of each grid, the intensity distribution of the shock wave propagating into the surrounding space is calculated by applying the propagation relationship obtained from the previous operation. In practice, ray tracing can be performed from the starting point to the surrounding area according to the actual propagation direction, and the shock wave intensity at different distances along the ray can be calculated. To improve computational efficiency, an angle sampling method can be used to emit multiple rays at uniform angular intervals (e.g., 15 degrees) to form a fan-shaped coverage.

[0077] Finally, for each grid, the spatial range in which the shock wave intensity meets the operational intensity requirements (e.g., intensity greater than or equal to threshold T) is determined as the influence coverage area when that grid is used as a candidate placement location. The influence coverage area can be represented by polygons or a set of grids. This coverage area data will be used for subsequent placement optimization to ensure that the placement scheme can effectively cover the entire operational area.

[0078] In practical applications, such as mining blasting operations, the impact coverage area calculated by this method can reflect the range of impact of different blasting points on the surrounding environment, which helps to optimize the layout of blasting points, ensuring the blasting effect while controlling the impact on surrounding sensitive areas.

[0079] In one optional implementation, based on the distribution data of protected targets in the work area, a safety constraint assessment is performed on the influence coverage area of ​​each candidate placement location to select a set of valid candidate placement locations that meet the safety distance constraints, including:

[0080] Based on the distribution data of the protected targets, the location coordinates and protection level information of the protected targets are determined, and the minimum safe distance requirement corresponding to each protected target is determined according to the protection level information.

[0081] For each candidate deployment location, the spatial relationship between the influence coverage area of ​​the candidate deployment location and the location coordinates of the protected target is determined, and the protected targets located within the influence coverage area are identified, thus obtaining the set of protected targets affected by the candidate deployment location;

[0082] Calculate the spatial distance between the candidate placement locations and each protected target in the set of affected protected targets. Based on the spatial distance and the minimum safety distance requirement corresponding to the protected target, when all spatial distances are greater than or equal to the minimum safety distance requirement, mark the candidate placement locations as valid candidate placement locations that satisfy the safety constraints.

[0083] Traverse all candidate placement locations and determine the set of valid candidate placements based on all valid candidate placements that satisfy the safety constraints.

[0084] First, obtain the distribution data of protected targets in the work area. This data includes the location coordinates and protection level information of each protected target. Based on this information, determine the minimum safety distance requirement for each protected target. The safety distance requirement varies for targets with different protection levels; generally, the higher the protection level, the greater the required safety distance.

[0085] After obtaining the information on the protected target, the location coordinates and protection level information are determined. The location coordinates can be represented using latitude and longitude coordinates or a rectangular coordinate system. The protection level can be divided into multiple levels, such as Level I, Level II, and Level III protected areas. Corresponding minimum safety distance requirements are set according to the protection level; for example, the minimum safety distance for Level I protected areas is 500 meters, for Level II protected areas it is 300 meters, and for Level III protected areas it is 100 meters.

[0086] In practical applications, protected targets can be residential areas, schools, hospitals, cultural relics, or ecologically sensitive areas—objects requiring special protection. Each protected target is assigned a different protection level based on its importance and sensitivity, thereby determining the corresponding minimum safe distance requirement. This information can be stored using data structures, such as a data table containing location coordinates, protection level, and minimum safe distance.

[0087] After determining the protection target information, for each candidate site location, the spatial relationship between its influence coverage area and the protected target is analyzed. The influence coverage area is typically a certain radius range centered on the candidate site location, and this radius is determined based on the influence range of the site facility. Through spatial relationship determination, the protected targets located within the influence coverage area are identified, forming a set of protected targets affected by the candidate site locations.

[0088] Spatial relationship determination can be achieved by calculating the Euclidean distance between the candidate placement location and each protected target. Assuming the coordinates of the candidate placement location are (x1, y1) and the coordinates of the protected target are (x2, y2), the spatial distance between them is calculated as: distance = sqrt([(x2-x1)²+(y2-y1)²]). If the calculated distance is less than the radius of the influence coverage area, the protected target is considered to be within the influence coverage area and is added to the set of affected protected targets.

[0089] For each candidate location, calculate its spatial distance from each protected target in the affected target set, and compare it with the minimum safe distance requirement of the corresponding protected target. When the spatial distance between the candidate location and all protected targets within its influence range is greater than or equal to their respective minimum safe distance requirements, the candidate location is marked as a valid candidate location that satisfies the safety constraints.

[0090] For example, consider a candidate site A with three protected targets B, C, and D within its influence coverage area. These targets belong to secondary, primary, and tertiary protected areas, respectively, with minimum safety distance requirements of 300 meters, 500 meters, and 100 meters. The calculated spatial distances between candidate site A and these three protected targets are 350 meters, 520 meters, and 150 meters, respectively. Since all distances exceed their respective minimum safety distance requirements, candidate site A is marked as a valid candidate site that satisfies the safety constraints.

[0091] In practice, for a large number of candidate placement points and protection targets, spatial indexing techniques such as R-trees and quadtrees can be used to improve the efficiency of spatial relationship queries. These techniques can quickly filter out spatial objects within a specific range, reduce unnecessary distance calculations, and improve the overall performance of the algorithm.

[0092] After completing the safety constraint assessment of all candidate deployment locations, all candidate deployment locations that meet the safety constraints are summarized to form an effective candidate deployment set. The deployment locations in this set all meet the safety distance requirements for the protected target and can serve as the basis for subsequent deployment decisions.

[0093] In some complex scenarios, it is necessary to consider the impact of factors such as terrain and obstacles on safe distances. In such cases, a terrain correction factor can be introduced, or a more complex distance calculation model can be used, such as considering the shortest path distance to obstacles rather than a simple straight-line distance.

[0094] Furthermore, weighting factors can be introduced during the assessment process, assigning different weights to targets of different types or protection levels, making the safety constraint assessment more aligned with actual needs. For example, densely populated areas can be given higher weights, thus prioritizing the safety of personnel under the same distance conditions.

[0095] The final set of valid candidate deployment points can be used as input for subsequent deployment optimization. By further combining factors such as coverage efficiency and cost, the final deployment scheme can be determined. This candidate deployment point screening method based on security constraints ensures that the final deployment scheme can meet the security distance requirements for various protected targets and effectively reduces potential risks.

[0096] In one optional implementation, the effective candidate deployment point set is iteratively solved based on the job requirement parameters. In each iteration, the overlapping and blank coverage areas of the current deployment scheme are calculated through spatial overlay analysis. The deployment point positions and numbers are dynamically adjusted according to the overlapping and blank coverage areas to obtain an initial optimized deployment scheme, including:

[0097] Select an initial combination of points from the set of valid candidate points, and obtain the influence coverage area corresponding to each point position in the initial combination of points;

[0098] The influence coverage areas of all locations in the initial point layout combination are spatially superimposed to identify the spatial areas where multiple influence coverage areas overlap as coverage overlap areas, and the spatial areas within the work area that are not covered by any influence coverage area are identified as coverage blank areas.

[0099] Based on the coverage blank area, candidate point positions located within the coverage blank area are selected from the set of valid candidate points and added to the initial point combination; based on the coverage overlap area, point positions located within the coverage overlap area are removed from the initial point combination to form an updated point combination.

[0100] Based on the updated point combination, the spatial overlay processing and point position adjustment operations are repeated until the area of ​​the covered blank area and the area of ​​the covered overlapping area meet the convergence condition. The point combination that meets the convergence condition is determined as the initial optimized point combination.

[0101] The process of iteratively solving for the effective candidate point placement set based on job requirement parameters to obtain an initial optimized point placement scheme first requires selecting an initial point placement combination from the effective candidate point placement set. This initial point placement combination can be formed through various strategies, such as a uniform grid distribution strategy, a random selection strategy, or a strategy based on importance ranking. In a practical application scenario, the job area can be divided into a grid, and an initial point placement can be set at the center of each grid cell, thus forming the initial point placement combination.

[0102] After obtaining the initial deployment combination, it is necessary to determine the influence coverage area corresponding to each deployment location. The influence coverage area represents the spatial range that a single deployment location can effectively cover, and its shape and size depend on the specific application scenario and equipment parameters. In environmental monitoring applications, if wireless sensors are used for monitoring, the influence coverage area is a circular area centered on the deployment point, with the radius determined by the sensor's effective sensing distance. For video surveillance systems, the influence coverage area is a fan-shaped area, determined by both the camera's field of view and effective monitoring distance.

[0103] To calculate the coverage effect of the site selection scheme, it is necessary to spatially overlay the influence coverage areas of all site locations in the initial site selection combination. This process can be achieved through Geographic Information System (GIS) technology or spatial analysis algorithms. After spatial overlay processing, two types of key areas can be identified: spatial areas where multiple influence coverage areas overlap are marked as coverage overlap areas, while spatial areas within the operational area that are not covered by any influence coverage area are marked as coverage blank areas.

[0104] The process of identifying overlapping and blank areas can be achieved through rasterization. The entire working area is divided into small raster cells, and the number of influence regions covering each cell is calculated. If the number of influence regions is zero, the raster cell belongs to a blank area; if the number of influence regions is greater than one, the raster cell belongs to an overlapping area. By aggregating adjacent raster cells with the same attributes, complete blank and overlapping areas can be formed.

[0105] Based on the identified coverage gaps and overlaps, the initial deployment combination is adjusted. For coverage gaps, candidate deployment locations within the area are selected from the effective candidate deployment set and added to the initial deployment combination. The selection process can consider factors such as the distance between the candidate deployment location and the gap, and the size of the coverage area, prioritizing candidate locations that minimize gaps. For coverage overlaps, deployment locations within the area need to be removed from the initial deployment combination to reduce resource waste and redundant coverage. The removal strategy can be based on the importance indicators of the deployment, such as the unique size of the covered area and the distance from other deployments, retaining deployment locations that contribute significantly to overall coverage.

[0106] By adding and removing points as described above, an updated point combination is formed. This updated combination still contains both blank and overlapping areas, therefore, spatial overlay processing and point position adjustment operations need to be repeated based on the updated combination. This iterative process continues until a specific convergence condition is met.

[0107] The convergence criteria are related to the areas of the blank coverage area and the overlapping coverage area. For example, it can be set that when the area of ​​the blank coverage area is less than a certain percentage (e.g., 5%) of the total working area and the area of ​​the overlapping coverage area is less than a certain threshold (e.g., 20%), the point placement scheme is considered to have reached a good balance, and the iteration process can end. Alternatively, a maximum number of iterations can be set. If the convergence criteria are not met after reaching the maximum number of iterations, the point placement combination with the best coverage effect is selected as the final result.

[0108] During the iteration process, a local optimization strategy can be introduced to fine-tune the placement of data points. For each placement point, candidate locations within a small surrounding area can be explored, the impact of the adjustment on the overall coverage effect can be evaluated, and a new location that maximizes the coverage effect can be selected. This local optimization can be performed in each iteration or as a fine-tuning step after the basic convergence conditions are met.

[0109] Once the iteration process meets the convergence condition, the current combination of points is determined as the initial optimized point layout scheme. The initial optimized point layout scheme includes the spatial coordinate information of the point locations and the coverage information of each point, providing a scientific basis for subsequent point layout implementation and resource allocation.

[0110] Through the above iterative solution process, a relatively reasonable deployment scheme can be obtained while taking into account both coverage integrity and resource utilization efficiency. It is applicable to various application scenarios that require spatial deployment of points, such as environmental monitoring network deployment, communication base station site selection, and emergency resource allocation.

[0111] In one optional implementation, the influence coverage areas of all locations in the initial point layout combination are spatially superimposed to identify overlapping spatial areas of multiple influence coverage areas as coverage overlap areas, and spatial areas within the work area not covered by any influence coverage area are identified as coverage blank areas, including:

[0112] The influence coverage domain of all point positions in the initial point combination is converted into a vector space object to obtain a spatial index structure;

[0113] Based on the spatial index structure, the spatial topological relationship of the influence coverage domain of each point location in the initial point combination is determined, other influence coverage domains that have spatial intersection with the current influence coverage domain are obtained, and the spatial intersection matrix between influence coverage domains is determined;

[0114] Based on the spatial intersection matrix, a geometric intersection operation is performed on the influence coverage regions that have spatial intersection relationships to obtain the intersection areas between each influence coverage region, and the number of influence coverage regions corresponding to each intersection area is recorded;

[0115] The overlapping regions with more than a preset threshold number of affected coverage areas are spatially merged to obtain the overlapping coverage areas.

[0116] The total coverage area is obtained by performing a spatial union operation on the influence coverage areas of all locations in the initial layout combination. The spatial difference operation is then performed between the spatial range of the work area and the total coverage area to obtain the remaining uncovered spatial area as the coverage blank area.

[0117] In optimizing the layout of signal sites, it is necessary to analyze the spatial distribution of the coverage area affected by the site location, especially to identify overlapping and blank coverage areas, so as to provide a basis for subsequent site optimization.

[0118] First, the influence coverage information of all placement points in the initial placement combination is obtained. The influence coverage of each placement point can be represented as a region with a specific geometric shape, such as a circular region centered on the placement point, the radius of which can be calculated based on the signal propagation model. In practical applications, the shape of the influence coverage is also affected by factors such as terrain and obstacles, resulting in an irregular shape.

[0119] Next, the influence coverage of all point positions in the initial point combination is converted into a vector space object, resulting in a spatial index structure. Specifically, spatial indexing methods such as R-trees and quadtrees can be used to represent each influence coverage as a polygon or other geometric shape and establish a spatial index. For example, for a circular coverage, a polygon approximation can be used; the more vertices the polygon has, the higher the approximation accuracy. If an R-tree index is used, the bounding rectangle of each coverage is used as the index object to construct a tree-like hierarchical structure, accelerating subsequent spatial query operations.

[0120] Based on the constructed spatial index structure, the spatial topological relationship of the influence coverage area of ​​each point position in the initial point combination is determined. Specifically, each influence coverage area is traversed, and the spatial index is used to quickly filter out other overlapping coverage areas. Then, a precise geometric intersection judgment is performed to determine whether a spatial intersection relationship exists. This judgment can be implemented using algorithms such as line segment intersection and polygon intersection from computational geometry. The judgment result is recorded in a spatial intersection relationship matrix, which is an n×n Boolean matrix (n is the number of points). The matrix element (i,j) indicates whether the coverage areas of point i and point j have a spatial intersection.

[0121] Based on the obtained spatial intersection matrix, geometric intersection operations are performed on the influence coverage areas with spatial intersection relationships. For any two coverage areas with intersection relationships, their spatial intersection is calculated to obtain the geometric representation of the intersection region. If multiple coverage areas intersect, the intersection needs to be calculated recursively. For example, for three intersecting coverage areas A, B, and C, A∩B is first calculated to obtain an intermediate result, and then (A∩B)∩C is calculated to obtain the intersection of the three. During the intersection process, polygon clipping algorithms such as the Sutherland-Hodgman algorithm can be used for coverage areas represented by polygons; for complex shapes, Boolean operation libraries can be used to implement the intersection function. At the same time, the number of influence coverage areas corresponding to each intersection region is recorded, that is, how many coverage areas overlap in this region.

[0122] Intersecting regions with more than a preset threshold of overlapping coverage areas are spatially merged to obtain overlapping coverage areas. The preset threshold can be set according to actual needs; for example, regions with three or more overlapping coverage areas can be considered as highly overlapping regions. The spatial merging process uses a geometric union operation to merge multiple overlapping regions that meet the criteria into a single continuous geometric object. During the merging process, buffer operations can be applied to regions that are close together, connecting them into larger continuous regions to facilitate subsequent analysis and optimization.

[0123] Next, a spatial union operation is performed on the influence coverage areas of all point positions in the initial point combination to obtain the total coverage area. The spatial union operation merges all coverage areas into a single geometric object, representing the region covered by at least one coverage area. The union process can be implemented using a planar scanline algorithm or a divide-and-conquer method. For a large number of coverage areas, a batch processing strategy can be used to improve computational efficiency.

[0124] Finally, the spatial difference between the working area and the total coverage area is calculated to obtain the uncovered remaining space as the coverage blank area. The spatial difference operation subtracts the total coverage area geometry from the working area geometry object, and the result is the blank area not covered by any coverage domain. This step can be implemented using Boolean difference operations in computational geometry libraries, such as those provided by open-source libraries like GEOS and JTS.

[0125] In practical applications, the identified overlapping and blank areas can be visualized to intuitively present the coverage situation. For example, different colors can be used to mark the degree of overlap, with darker colors for areas with higher overlap, while blank areas are marked with special colors. Such visualization results can directly guide subsequent spot optimization, such as adding new spots in blank areas or adjusting the spot placement in overlapping areas to reduce redundancy.

[0126] Through the above steps, the coverage of the initial point combination was analyzed, identifying overlapping and blank areas, providing a basis for subsequent optimization. This method combines spatial indexing, topological relationship determination, and geometric operations to achieve efficient and accurate spatial coverage analysis.

[0127] In one optional implementation, the accessibility of each point location in the initial optimized point layout scheme is checked based on the terrain feature data to obtain point locations with terrain barriers, and the point locations with terrain barriers are relocated to nearby locations that meet the accessibility requirements to generate the target optimized point layout scheme, including:

[0128] Based on the terrain feature data, road network data and surface elevation data are determined;

[0129] Based on the road network data, the locations of points with road obstructions in the initial optimized point layout scheme are determined, and based on the surface elevation data, the locations of points with steep terrain obstructions in the initial optimized point layout scheme are determined.

[0130] Based on the locations where roads and steep slopes obstruct the viewpoints, a set of locations with terrain obstructions is obtained;

[0131] For each location in the set of locations with terrain barriers, a candidate relocation location is searched within a preset range that satisfies path connectivity and whose terrain slope change does not exceed a preset slope threshold.

[0132] Select the position that is closest in space to the original placement position from the candidate relocation positions as the relocation target position, move the placement position to the relocation target position, update the initial optimized placement scheme based on the placement position after relocation, and generate the target optimized placement scheme.

[0133] This implementation method mainly addresses the problem of inaccessible or difficult-to-reach initial site layout schemes under complex terrain conditions, and ensures the practical feasibility of the final site layout scheme through terrain feature analysis and location relocation technology.

[0134] Figure 2 This is a flowchart illustrating the process of generating an optimized target placement scheme according to an embodiment of the present invention. Figure 2 As shown, firstly, road network data and surface elevation data are determined based on terrain feature data. Road network data typically includes information on the spatial distribution of roads, road classification, connectivity, and other attributes. This data can be obtained by extracting linear features from the terrain feature data and combining them with road attribute information. Surface elevation data reflects the three-dimensional undulation of the land surface within the study area and is usually represented as a digital elevation model (DEM). In practical applications, a raster data structure can be used to store elevation information, with each raster cell corresponding to an elevation value.

[0135] Next, based on the road network data, the locations of points in the initial optimized point layout plan that are blocked by road barriers are determined. This step is achieved by constructing a road accessibility analysis model. Specifically, the road network is viewed as a graph structure, where nodes represent road intersections or endpoints, and edges represent road segments. For each point location in the initial optimized point layout plan, its spatial distance to the nearest road node is calculated. If this distance exceeds a preset threshold (e.g., 200 meters), the point location is considered to have a road barrier. Furthermore, the connectivity between point locations needs to be considered, and a shortest path algorithm (e.g., Dijkstra's algorithm) is used to analyze whether there are passable road paths between point locations. If two adjacent point locations cannot be connected by the road network, they are marked as having a road barrier.

[0136] Simultaneously, based on surface elevation data, the locations of points in the initial optimized point layout scheme that are obstructed by steep slopes were identified. First, the slope distribution within the study area was calculated using surface elevation data. Slope calculation can be performed using the ratio of the elevation difference between adjacent grid cells to the horizontal distance. For each point location in the initial optimized layout scheme, slope information of its location and surrounding area was extracted. If a point location is located in a steep slope area (slope exceeding a preset threshold, such as 30 degrees), it is marked as having a steep slope obstruction. Furthermore, for adjacent point locations, the elevation changes between them were analyzed. If the slope at any point on the connecting line exceeds a preset threshold, it is considered that there is a steep slope obstruction between these two point locations.

[0137] Based on the above analysis results, the locations with road obstructions and locations with steep terrain obstructions are merged to form a set of locations with terrain obstructions. This set includes all locations in the initial optimized location scheme that need to be relocated.

[0138] For each location in the set of locations with terrain barriers, a search is conducted within a preset range to find candidate relocation locations that meet the requirements. This search process can be implemented using a grid scanning method. First, a circular search range with a preset radius (e.g., 500 meters) is defined centered on the location to be relocated. Within this range, a grid of candidate points is generated at certain sampling intervals (e.g., 10 meters). For each candidate point, two checks are performed: one is a path connectivity check, ensuring that there is a passable road path between the point and other locations; the other is a terrain slope check, ensuring that the slope change on the location of the point and the connecting path between it and adjacent locations does not exceed a preset slope threshold.

[0139] Among the candidate relocation locations that meet the above conditions, the location with the closest spatial distance to the original location is selected as the relocation target location. The spatial distance can be calculated using Euclidean distance, which is the straight-line distance between the two points. The goal of minimizing the spatial distance is to reduce the disturbance to the original location scheme as much as possible while ensuring accessibility.

[0140] Finally, the locations where terrain obstructs the observation points are moved to the corresponding relocation target locations. Based on the relocation locations, the initial optimized observation point layout scheme is updated to generate the target optimized observation point layout scheme. In practical application scenarios, such as the deployment of meteorological observation station networks, this method can effectively avoid placing observation stations in hard-to-reach locations, improving the practicality and maintenance convenience of the observation network.

[0141] In practical implementation, Geographic Information System (GIS) technology can be used. For example, accessibility analysis of road networks can be achieved using network analysis modules, and the identification of steep terrain slopes can be accomplished using surface analysis tools. Candidate point generation and evaluation during the relocation process can be achieved through functions such as spatial query and buffer analysis.

[0142] It is worth noting that in practical applications, parameters such as preset range and slope threshold need to be set reasonably according to specific application scenarios and requirements. For example, for a deployment plan that is accessible by foot, the slope threshold should be set to 25 degrees; while for a deployment plan that is accessible by vehicle, the slope threshold needs to be reduced to 15 degrees. In this way, it is ensured that the final deployment plan meets both business needs and has good practical feasibility.

[0143] In one optional implementation, determining the locations of points with road obstructions in the initial optimized point layout scheme based on the road network data, and determining the locations of points with steep slope obstructions in the initial optimized point layout scheme based on the surface elevation data, includes:

[0144] Based on the road network data, determine the coordinates of road nodes and the connection relationships of road segments, and generate a road connectivity graph based on the coordinates of road nodes and the connection relationships of road segments;

[0145] Map the preset transportation starting point and each location in the initial optimized point layout scheme to the corresponding road node in the road connectivity graph;

[0146] For each deployment location, a path search is performed on the road node corresponding to the preset transportation starting point in the road connectivity map to determine whether a connecting path exists. If no connecting path exists, the corresponding deployment location is determined as a deployment location with road obstruction.

[0147] Based on the surface elevation data, the elevation values ​​of each spatial location within the work area are determined. For each location in the initial optimized layout scheme, multiple sampling points are extracted along a straight path from the preset transportation starting point to the location at a preset sampling interval. The elevation value of each sampling point is obtained, and the slope value is calculated based on the elevation difference and horizontal distance between adjacent sampling points.

[0148] When the slope value exceeds the preset slope threshold, the corresponding location of the sampling point is determined as a location where there is a steep slope barrier.

[0149] In this embodiment, the initial optimized site layout scheme is first evaluated based on road network data and surface elevation data to determine the site locations where there are road obstructions or steep terrain obstacles, providing a basis for subsequent adjustments to the site layout scheme.

[0150] Obtain electronic map data containing road network data and surface elevation data. The road network data includes information such as the location, road grade, and connection relationship of each road in the area; the surface elevation data includes the altitude values ​​of each spatial location in the area.

[0151] The coordinates of road nodes and the connectivity of road segments are determined based on road network data. Road nodes typically include characteristic points such as road intersections, turning points, and the start and end points of roads, and each road node is represented by latitude and longitude coordinates. The connectivity of road segments describes the connectivity between different road nodes, and can usually be represented in the form of an adjacency matrix or adjacency list. For example, for nodes A and B, if there is a road segment between them that allows direct passage, this information is recorded in the connectivity relationship.

[0152] A road connectivity graph is generated based on the coordinates of road nodes and the connection relationships of road segments. This connectivity graph can be viewed as an undirected or directed graph (depending on whether the road is a one-way street), where nodes represent road nodes and edges represent passable road segments. For example, in the scenario of rural power distribution network deployment, the main roads between villages and country lanes will be included in the connectivity graph, while impassable areas (such as water bodies, cliffs, etc.) will not have corresponding edges.

[0153] Each location in the preset transportation starting point and initial optimized layout plan is mapped to a corresponding road node in the road connectivity graph. The preset transportation starting point is typically a material warehouse, a centralized loading and unloading point, or a maintenance base. The mapping process uses the nearest neighbor principle, that is, each location point is mapped to the nearest road node in the road connectivity graph. For example, if the coordinates of a location are (121.5, 31.2), then the road node closest to that coordinate in the road connectivity graph is found as its mapping point.

[0154] For each location in the initial optimized point placement scheme, a path search is performed in the road connectivity graph from the road nodes corresponding to the preset transportation starting point to determine whether a connected path exists. The path search can employ Breadth-First Search (BFS), Dijkstra's algorithm, or A* algorithm, among others. For example, when using Dijkstra's algorithm, starting from the transportation starting point, the reachable nodes are gradually expanded until the node corresponding to the target point location is found or all reachable nodes have been traversed.

[0155] When the path search cannot find a connecting path from the preset transportation starting point to a certain placement location, the placement location is determined to be a placement location with road obstruction. This indicates that under the existing road network conditions, it is impossible to reach the placement location from the transportation starting point by road, and the plan needs to be adjusted.

[0156] The elevation values ​​of each spatial location within the work area are determined based on surface elevation data. Elevation data is typically stored in a regular grid format, with each grid cell corresponding to an elevation value. If a location is not on a grid node, its elevation value is calculated using methods such as bilinear interpolation. For example, in rural power distribution network deployment scenarios, elevation variations are significant in mountainous areas, requiring accurate assessment of the impact of elevation changes on the accessibility of deployment locations.

[0157] For each location in the initial optimized sampling plan, multiple sampling points are extracted along a straight path from the preset transportation starting point to that location at a preset sampling interval. The preset sampling interval can be determined based on the terrain complexity and evaluation accuracy requirements, for example, set to 50 meters or 100 meters. If the transportation starting point coordinates are (x1, y1), the location coordinates are (x2, y2), the straight-line distance between the two points is D, and the sampling interval is d, then a total of [number missing] sampling points are extracted along the straight path. There are 1 sampling point (including the start and end points).

[0158] The elevation value of each sampling point can be obtained directly from the elevation data or calculated through interpolation. The slope value is calculated based on the elevation difference and horizontal distance between adjacent sampling points. For example, for two adjacent sampling points P1 and P2, their elevation values ​​are h1 and h2 respectively, and the horizontal distance is L. Then the slope value is calculated as |h2-h1| / L.

[0159] When the calculated slope value exceeds a preset slope threshold, the corresponding location is identified as a location with a steep slope barrier. The preset slope threshold is determined based on the actual traffic capacity of construction vehicles or personnel. For example, for ordinary freight vehicles, the slope threshold can be set to 0.3 (approximately 17 degrees). This means that if there is a section of road with a slope exceeding the threshold on the path from the transportation origin to the location, the location is considered to have a steep slope barrier.

[0160] Through the above steps, the locations of the site selection points that are obstructed by roads or steep terrain slopes in the initial optimized site selection plan have been identified. This information will be used for subsequent adjustments and optimizations to the site selection plan to ensure its feasibility during actual construction and operation. For example, for site selection points identified as having obstructions, consideration can be given to moving them to nearby accessible areas, or additional road construction costs can be reserved in the plan.

[0161] The GIS-based gas cannon operation deployment optimization system of this invention includes:

[0162] The first unit is used to determine the propagation relationship of operational impacts based on terrain feature data and meteorological environment data of the operational area, and to spatially discretize the operational area using the operational impact propagation relationship, dividing the operational area into multiple grids, and calculating the impact coverage area of ​​each grid as a candidate placement location;

[0163] The second unit is used to evaluate the safety constraints of the influence coverage area of ​​each candidate deployment point based on the distribution data of the protected targets in the work area, and to select a set of valid candidate deployment points that meet the safety distance constraints.

[0164] The third unit is used to iteratively solve the set of effective candidate deployment points based on the job requirement parameters. In each iteration, the overlapping area and the blank area of ​​the current deployment scheme are calculated through spatial overlay analysis. The deployment point positions and numbers are dynamically adjusted according to the overlapping area and the blank area to obtain the initial optimized deployment scheme.

[0165] The fourth unit is used to perform accessibility verification on each location in the initial optimized point layout scheme based on the terrain feature data, obtain the location of the point with terrain obstruction, and relocate the location of the point with terrain obstruction to a nearby location that meets the accessibility requirements, thereby generating the target optimized point layout scheme.

[0166] A third aspect of the present invention provides an electronic device, comprising:

[0167] processor;

[0168] Memory used to store processor-executable instructions;

[0169] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0170] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0171] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A GIS-based method for optimizing the deployment of gas cannon operations, characterized in that, include: Based on the terrain feature data and meteorological environment data of the work area, the propagation relationship of the operation's impact is determined. The work area is then spatially discretized using this propagation relationship, dividing it into multiple grids. The impact coverage area of ​​each grid as a candidate placement location is then calculated. Based on the distribution data of protected targets in the work area, a safety constraint assessment is performed on the influence coverage area of ​​each candidate deployment point, and a set of valid candidate deployment points that meet the safety distance constraints is selected. The effective candidate point placement set is iteratively solved based on the task requirement parameters. In each iteration, the overlapping and blank areas of the current point placement scheme are calculated through spatial overlay analysis. The point placement positions and numbers are dynamically adjusted according to the overlapping and blank areas to obtain the initial optimized point placement scheme. Based on the terrain feature data, the accessibility of each point in the initial optimized point layout scheme is checked to obtain the point locations with terrain barriers, and the point locations with terrain barriers are relocated to nearby locations that meet the accessibility requirements to generate the target optimized point layout scheme. Based on the terrain feature data and meteorological environment data of the work area, the propagation relationship of the work impact is determined. This propagation relationship is then used to spatially discretize the work area, dividing it into multiple grids. The impact coverage area of ​​each grid as a candidate placement location is calculated, including: The surface elevation and slope information in the terrain feature data are determined, and the three-dimensional terrain surface of the work area is determined based on the surface elevation and slope information. The initial attenuation direction of the shock wave propagation is determined along the normal direction of the three-dimensional terrain surface. The wind direction and wind speed information in the meteorological environment data are determined. Based on the wind direction and wind speed information, the offset influence of the airflow on the propagation path of the shock wave is calculated. Based on the offset influence, the initial attenuation direction is corrected to obtain the actual propagation direction of the shock wave in three-dimensional space. Based on the actual propagation direction and the wind speed information, the attenuation rate of the shock wave intensity with the propagation distance is calculated to obtain the propagation relationship of the operation impact. The propagation relationship of the operation impact is obtained by combining the actual propagation direction and the attenuation rate, and represents the intensity distribution of the shock wave originating from any source point at various points in the surrounding space. The work area is divided into multiple grids according to a preset grid spacing. Taking the center coordinates of each grid as the starting point, the intensity distribution of the shock wave at different locations is calculated based on the propagation relationship of the work influence. The spatial range in which the shock wave intensity meets the work intensity requirements is determined as the influence coverage area when the grid is used as a candidate placement location.

2. The method according to claim 1, characterized in that, Based on the distribution data of protected targets in the work area, a safety constraint assessment is performed on the influence coverage area of ​​each candidate deployment point, and a set of valid candidate deployment points that meet the safety distance constraints is selected, including: Based on the distribution data of the protected targets, the location coordinates and protection level information of the protected targets are determined, and the minimum safe distance requirement corresponding to each protected target is determined according to the protection level information. For each candidate deployment location, the spatial relationship between the influence coverage area of ​​the candidate deployment location and the location coordinates of the protected target is determined, and the protected targets located within the influence coverage area are identified, thus obtaining the set of protected targets affected by the candidate deployment location; Calculate the spatial distance between the candidate placement locations and each protected target in the set of affected protected targets. Based on the spatial distance and the minimum safety distance requirement corresponding to the protected target, when all spatial distances are greater than or equal to the minimum safety distance requirement, mark the candidate placement locations as valid candidate placement locations that satisfy the safety constraints. Traverse all candidate placement locations and determine the set of valid candidate placements based on all valid candidate placements that satisfy the safety constraints.

3. The method according to claim 1, characterized in that, Based on the job requirement parameters, the effective candidate deployment point set is iteratively solved. In each iteration, the overlapping and blank coverage areas of the current deployment scheme are calculated through spatial overlay analysis. The deployment point positions and numbers are dynamically adjusted according to the overlapping and blank coverage areas to obtain an initial optimized deployment scheme, including: Select an initial combination of points from the set of valid candidate points, and obtain the influence coverage area corresponding to each point position in the initial combination of points; The influence coverage areas of all locations in the initial point layout combination are spatially superimposed to identify the spatial areas where multiple influence coverage areas overlap as coverage overlap areas, and the spatial areas within the work area that are not covered by any influence coverage area are identified as coverage blank areas. Based on the coverage blank area, candidate point positions located within the coverage blank area are selected from the set of valid candidate points and added to the initial point combination; based on the coverage overlap area, point positions located within the coverage overlap area are removed from the initial point combination to form an updated point combination. Based on the updated point combination, the spatial overlay processing and point position adjustment operations are repeated until the area of ​​the covered blank area and the area of ​​the covered overlapping area meet the convergence condition. The point combination that meets the convergence condition is determined as the initial optimized point combination.

4. The method according to claim 3, characterized in that, The influence coverage areas of all locations in the initial point layout combination are spatially superimposed to identify overlapping spatial areas as coverage overlap areas, and spatial areas within the work area not covered by any influence coverage area are identified as coverage blank areas, including: The influence coverage domain of all point positions in the initial point combination is converted into a vector space object to obtain a spatial index structure; Based on the spatial index structure, the spatial topological relationship of the influence coverage domain of each point location in the initial point combination is determined, other influence coverage domains that have spatial intersection with the current influence coverage domain are obtained, and the spatial intersection matrix between influence coverage domains is determined; Based on the spatial intersection matrix, a geometric intersection operation is performed on the influence coverage regions that have spatial intersection relationships to obtain the intersection areas between each influence coverage region, and the number of influence coverage regions corresponding to each intersection area is recorded; The overlapping regions with more than a preset threshold number of affected coverage areas are spatially merged to obtain the overlapping coverage areas. The total coverage area is obtained by performing a spatial union operation on the influence coverage areas of all locations in the initial layout combination. The spatial difference operation is then performed between the spatial range of the work area and the total coverage area to obtain the remaining uncovered spatial area as the coverage blank area.

5. The method according to claim 1, characterized in that, Based on the terrain feature data, the accessibility of each point in the initial optimized point layout scheme is checked to identify points with terrain barriers. These points are then relocated to nearby locations that meet accessibility requirements, generating a target optimized point layout scheme, including: Based on the terrain feature data, road network data and surface elevation data are determined; Based on the road network data, the locations of points with road obstructions in the initial optimized point layout scheme are determined, and based on the surface elevation data, the locations of points with steep terrain obstructions in the initial optimized point layout scheme are determined. Based on the locations where roads and steep slopes obstruct the viewpoints, a set of locations with terrain obstructions is obtained; For each location in the set of locations with terrain barriers, a candidate relocation location is searched within a preset range that satisfies path connectivity and whose terrain slope change does not exceed a preset slope threshold. Select the position that is closest in space to the original placement position from the candidate relocation positions as the relocation target position, move the placement position to the relocation target position, update the initial optimized placement scheme based on the placement position after relocation, and generate the target optimized placement scheme.

6. The method according to claim 5, characterized in that, Based on the road network data, the locations of points in the initial optimized point layout scheme that are obstructed by roads are determined, and based on the surface elevation data, the locations of points in the initial optimized point layout scheme that are obstructed by steep terrain slopes are determined, including: Based on the road network data, determine the coordinates of road nodes and the connection relationships of road segments, and generate a road connectivity graph based on the coordinates of road nodes and the connection relationships of road segments; Map the preset transportation starting point and each location in the initial optimized point layout scheme to the corresponding road node in the road connectivity graph; For each deployment location, a path search is performed on the road node corresponding to the preset transportation starting point in the road connectivity map to determine whether a connecting path exists. If no connecting path exists, the corresponding deployment location is determined as a deployment location with road obstruction. Based on the surface elevation data, the elevation values ​​of each spatial location within the work area are determined. For each location in the initial optimized layout scheme, multiple sampling points are extracted along a straight path from the preset transportation starting point to the location at a preset sampling interval. The elevation value of each sampling point is obtained, and the slope value is calculated based on the elevation difference and horizontal distance between adjacent sampling points. When the slope value exceeds the preset slope threshold, the corresponding location of the sampling point is determined as a location where there is a steep slope barrier.

7. A GIS-based gas cannon operation site optimization system, used to implement the method as described in any one of claims 1-6, characterized in that, include: The first unit is used to determine the propagation relationship of operational impacts based on terrain feature data and meteorological environment data of the operational area, and to spatially discretize the operational area using the operational impact propagation relationship, dividing the operational area into multiple grids, and calculating the impact coverage area of ​​each grid as a candidate placement location; The second unit is used to evaluate the safety constraints of the influence coverage area of ​​each candidate deployment point based on the distribution data of the protected targets in the work area, and to select a set of valid candidate deployment points that meet the safety distance constraints. The third unit is used to iteratively solve the set of effective candidate deployment points based on the job requirement parameters. In each iteration, the overlapping area and the blank area of ​​the current deployment scheme are calculated through spatial overlay analysis. The deployment point positions and numbers are dynamically adjusted according to the overlapping area and the blank area to obtain the initial optimized deployment scheme. The fourth unit is used to perform accessibility verification on each location in the initial optimized point layout scheme based on the terrain feature data, obtain the location of the point with terrain obstruction, and relocate the location of the point with terrain obstruction to a nearby location that meets the accessibility requirements, thereby generating the target optimized point layout scheme.

8. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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