Monitoring point setting method for low-altitude safe corridor

Abstract

The invention relates to a monitoring point setting method for a low-altitude safe corridor, and the method comprises the steps: constructing a three-dimensional spatial domain model of a target region, and dividing the target region into a plurality of sub-regions; monitoring equipment is installed in each sub-region to establish a monitoring point, the shielding loss of the monitoring point is determined by combining the three-dimensional spatial domain model, the receiving power of the monitoring point is determined, and the actual coverage range corresponding to the receiving power of the monitoring point is determined based on a coverage attenuation function; determining the coverage rate of all the monitoring points in the sub-region based on the actual coverage range of the monitoring points; acquiring a preset channel route, and establishing a multi-objective optimization function by combining the preset channel route with the coverage rate and the shielding loss of the monitoring points; and performing iterative solution on the multi-objective optimization function based on a genetic algorithm to obtain the number of monitoring points and the positions of the monitoring points in the target area, thereby realizing high-precision monitoring point layout, and remarkably improving the safety monitoring capability of the low-altitude corridor.

Description

Technical Field

[0001] The present application relates to the field of low-altitude technology, and in particular to a method for setting monitoring points for low-altitude safety corridors. Background Art

[0002] Low-altitude safety corridors refer to dedicated airspace channels with clear rules and safety measures designated for low-altitude aircraft (such as drones, light aircraft, and helicopters), designed to ensure the orderliness and safety of low-altitude flight activities. To effectively identify and monitor potential risks such as illegal flights, wind shear, bird flocking, and navigation interference, the industry currently generally adopts a variety of sensing methods, including Automatic Dependent Surveillance-Broadcast (ADS-B), Remote Identification (Remote ID), low-altitude detection radars, spectrum detection systems, meteorological detection instruments, and navigation interference detection equipment. These devices each have advantages in detection range, signal response, and environmental adaptability, forming the technical foundation for low-altitude safety monitoring.

[0003] However, in the actual deployment of the low-altitude monitoring system, there are still many challenges at the technical and application levels, which affect the overall monitoring efficiency and safety assurance capabilities.

[0004] For example, the layout of monitoring points generally lacks scientificity and rationality. When planning low-altitude corridor monitoring points, the functional differences of different monitoring equipment and their sensitivity to factors such as terrain, building obstructions, etc. are often not fully taken into account, resulting in frequent monitoring blind spots and seriously restricting the precise supervision capabilities of aircraft.

[0005] Another example is the lack of collaborative capabilities among multiple devices. Although a wide range of monitoring and sensing equipment has been deployed, the lack of a unified coordination mechanism prevents these devices from effectively complementing each other in terms of monitoring data, sensing range, and target identification, impacting overall monitoring accuracy and the timeliness of early warning responses. Furthermore, the overlap of monitoring ranges and interference boundaries between devices are not clearly defined, further increasing system uncertainty.

[0006] In addition, due to the lack of a centralized knowledge base system, information such as the brand, model, monitoring technology type, and coverage of current low-altitude monitoring equipment is often scattered, affecting the efficiency and reliability of subsequent equipment selection, system maintenance, equipment interconnection, and platform integration. Summary of the Invention

[0007] An embodiment of the present application provides a method for setting monitoring points for a low-altitude safety corridor, so as to at least solve the problem of frequent monitoring blind spots in related technologies.

[0008] To achieve the above objectives, the present invention provides a method for setting monitoring points for a low-altitude safety corridor, comprising:

[0009] The airspace modeling step is used to construct a three-dimensional airspace model of the target area based on geographic information system (GIS) data, building information model (BIM) data and LiDAR point cloud data, and the target area is divided into several sub-areas; based on GIS data and LiDAR point cloud data, a building information model (BIM) data containing the location, height, shape and other data of the building is constructed, and the building material details and internal structure are supplemented by building information model (BIM) data; among them, GIS data provides the vector boundary of the building (such as polygon data in Shapefile), which is accurate to the meter or sub-meter level (high-precision GIS can reach the centimeter level); LiDAR point cloud extracts the base outline of the building through point cloud classification (ground, vegetation, building), with a planar accuracy of 0.1 to 0.5 meters (depending on the point cloud density), and directly measures the absolute elevation (such as roof height) and relative height (such as floor height) of the building, with a vertical accuracy of 0.1 to 0.3 meters.

[0010] In the data preprocessing step, monitoring equipment is installed in each of the sub-areas to establish a monitoring point, the shielding loss of the monitoring point is determined in combination with the three-dimensional spatial model, and the received power of the monitoring point is determined by measurement and / or calculation, and the received power P of the monitoring point is determined based on a coverage attenuation function. r The corresponding actual coverage range is used to determine the coverage rate of all monitoring points in the sub-area based on the actual coverage range of the monitoring points;

[0011] The function model construction step is to obtain a preset airway route, and combine the preset airway route with the coverage and occlusion loss of the monitoring points to establish a multi-objective optimization function with the goals of minimizing monitoring blind spots, maximizing monitoring coverage, balancing monitoring point equipment loads, and meeting airway route planning requirements;

[0012] The function model solving step is to iteratively solve the multi-objective optimization function based on a preset genetic algorithm to obtain the number and location of monitoring points in the target area.

[0013] This implementation enables high-precision monitoring point placement, significantly improving safety monitoring capabilities in low-altitude corridors. Utilizing a three-dimensional model and occlusion calculations, it avoids blind placement and improves resource utilization. Furthermore, an optimization algorithm minimizes monitoring blind spots and balances equipment load, thereby enhancing overall system stability and reliability. Furthermore, this method allows for flexible layout adjustments based on specific waterways, demonstrating excellent adaptability and scalability.

[0014] In some embodiments, the multi-objective optimization function is configured as the following computational model:

[0015]

[0016] Among them, m is the total number of monitoring points, n is the total number of regions, ω kis the weight of region k, S jk is the coverage rate of monitoring point j to area k, L jk is the shading loss from area k to monitoring point j, the coverage rate is negatively correlated with the shading loss, λ1 and λ2 are weight coefficients, U j is the equipment load at monitoring point j, δ l is the deviation measure between the lth route and the monitoring coverage, p is the number of route plans in the target area, C(x j ) is the cost of monitoring point j, which includes construction cost and operation cost, B is the total budget cost, L max is the maximum threshold of occlusion loss, which is used to limit the maximum acceptable occlusion loss. min It is a preset coverage sensitivity threshold, which is used to limit the minimum coverage sensitivity.

[0017] In some embodiments, the coverage attenuation function is used to represent the received power P r The nonlinear relationship with the actual coverage range is obtained by pre-fitting. If the received power P r If the coverage exceeds a certain strength threshold, the coverage is considered to be within the theoretical coverage range. The input of this coverage attenuation function is the received power value (e.g., dBm) of the monitoring device at a certain location, and the output is the actual coverage ratio at that location. This strength threshold can also be dynamically adjusted based on different device models and adaptive environmental feedback to improve model generalization capabilities.

[0018] In some embodiments, the function model solving step further includes:

[0019] In the population initialization step, an initial population is randomly generated, where each individual is used to represent the sub-region and location of a set of monitoring points. The individuals are encoded using real numbers, which facilitates precise expression of the spatial location of the monitoring points and high-resolution adjustment. Each dimension in the initial population represents an individual.

[0020] The population optimization step calculates the fitness of each individual according to the multi-objective optimization function, repeats the selection operation, crossover operation and mutation operation based on the fitness, continuously updates the population until the preset termination condition is reached, and outputs the optimal number of monitoring points and the location of the monitoring points, wherein the preset termination condition includes: reaching the maximum number of iterations or the population convergence.

[0021] In some embodiments, the population optimization step further comprises:

[0022] During the selection operation, a tournament selection method is used to randomly select a number of individuals from the current population to form a sub-population, and then the individual with the best fitness value is selected from the sub-population to enter the next generation.

[0023] In some embodiments, the population optimization step further comprises:

[0024] During the crossover operation, the crossover probability is dynamically adjusted for crossover operation, and the crossover probability is set to:

[0025] P c0 is the preset initial crossover probability, gen is the current number of iterations, gen max is the maximum number of iterations, and α is the attenuation coefficient, which is used to control the decreasing speed of the crossover probability;

[0026] During the mutation operation, the mutation probability is set as:

[0027] P m0 is the preset initial mutation probability, such as 0.01, and β is the growth coefficient used to control the rising speed of the mutation probability. During the mutation process, the generated monitoring point position is detected for occlusion. If the new monitoring point position is in a severely occluded area, the mutation position is regenerated.

[0028] In some of the embodiments, in the population optimization step, the evolved population is non-dominated sorted, the population is divided into different Pareto levels according to fitness, the monitoring point layout corresponding to the non-dominated solution is preferentially retained, and an elite retention strategy is adopted to directly retain the excellent individuals in the previous generation to the next generation, among which the excellent individuals are monitoring point configurations with excellent performance in key indicators such as high monitoring coverage, low occlusion loss, and small path deviation.

[0029] In the above embodiment, during the iterative process of the genetic algorithm, the crossover probability and mutation probability satisfy the following constraints:

[0030]

[0031] Among them, p c0 is the initial crossover probability, gen is the current number of iterations, gen max is the maximum number of iterations, and β is the crossover probability attenuation coefficient.

[0032] In some embodiments, the monitoring equipment includes a low-altitude detection radar, and the shielding loss includes: free space propagation loss L fs_l , ionospheric absorption loss L iom and ground reflection loss L gr .

[0033] In some embodiments, the monitoring device further includes an ADS-B device, and the obstruction loss includes: free space propagation loss L fs_ads , atmospheric attenuation loss L a and obstacle occlusion loss L o.

[0034] In the above embodiment, L fs_ads =32.45+20lgd ads +20lgf ads , d in the free space propagation loss model ads is the distance between the target and the ADS-B device, f ads The operating frequency of ADS-B equipment.

[0035] In the above embodiment, when the signal is completely blocked by an obstacle, L o =0; when the signal is partially blocked, α i is the material attenuation coefficient of the i-th obstacle, d i is the penetration path length, that is, the path length of the signal through the obstructed path.

[0036] In some embodiments, the monitoring device further includes a Remote ID device, and the shielding loss includes: free space propagation loss L fs_rid and obstacle occlusion loss L os .

[0037] In the above embodiment, L fs_rid =32.45+20lgd rid +20lgf rid , d in the free space propagation loss model rid is the distance between the target and the Remote ID device, f rid The operating frequency of the Remote ID device.

[0038] In the above embodiment, the obstacle blocking loss L os Expressed as: Among them, ρ i is the reflection coefficient of the obstacle, μ i is the absorption coefficient of the obstacle, and n is the number of obstacles on the signal propagation path.

[0039] In addition, the ambient noise N needs to be considered, which is calculated by the noise power spectral density and the signal bandwidth.

[0040] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0042] Figure 1 is a flow chart of a monitoring point setting method according to an embodiment of the present application;

[0043] Figure 2 It is a step-by-step flow chart of the monitoring point setting method according to an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without making any creative efforts are within the scope of protection of this application.

[0045] Obviously, the drawings described below are merely examples or embodiments of the present application. Those skilled in the art can, without inventive effort, apply the present application to other similar scenarios based on these drawings. Furthermore, it is also understood that, although the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, changes in design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as an insufficiency of the content disclosed in this application.

[0046] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0047] Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by a person of ordinary skill in the technical field to which this application belongs. The words "one", "a", "the" and the like used in this application do not indicate a limit on quantity and may indicate the singular or plural. The terms "include", "comprise", "have" and any variations thereof used in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units that are inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The word "multiple" used in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0048] This embodiment provides a method for setting monitoring points for a low-altitude safety corridor. Figures 1 to 2 is a flow chart of a monitoring point setting method according to an embodiment of the present application, such as Figures 1 to 2 As shown, the process includes the following steps:

[0049] The airspace modeling step S1 is used to construct a three-dimensional airspace model of the target area, including terrain, buildings, and other information, based on geographic information system (GIS) data, building information model (BIM) data, and LiDAR point cloud data. The target area is divided into several sub-areas. Based on the GIS data and LiDAR point cloud data, data such as the location, height, and shape of buildings is constructed, supplemented by building information model (BIM) data with building material details and internal structure. GIS data provides vector boundaries of buildings (such as polygon data in a Shapefile) with meter-level or sub-meter-level accuracy (high-precision GIS can reach centimeter-level accuracy). The LiDAR point cloud extracts the building base contours through point cloud classification (ground, vegetation, buildings), with a planar accuracy of 0.1 to 0.5 meters (depending on the point cloud density), and directly measures the absolute elevation (such as roof height) and relative height (such as floor height) of the buildings, with a vertical accuracy of 0.1 to 0.3 meters. By combining GIS, BIM, and LiDAR point cloud data, high-precision three-dimensional modeling of the target area's airspace is achieved. GIS data is suitable for constructing planar structures, BIM data enhances the detailed integrity of the model, LiDAR point clouds support direct measurement and classification of building heights, and the established three-dimensional airspace model is divided into multiple sub-areas, which is conducive to detailed management and calculation.

[0050] Data preprocessing step S2, installing monitoring equipment in each of the sub-areas to establish monitoring points, determining the shielding loss of the monitoring points in combination with the three-dimensional spatial model, and determining the received power of the monitoring points by measurement and / or calculation, and determining the received power P of the monitoring points based on a coverage attenuation function. r The corresponding actual coverage range determines the coverage rate of all monitoring points in the sub-area based on the actual coverage range of the monitoring points; optionally, the received power can be monitored by a signal strength meter or a spectrum analyzer; it should be noted that when a sub-area already has a part of monitoring points, it also needs to be included in this application for corresponding calculations.

[0051] Function model construction step S3, obtains the preset airway route, combines the preset airway route with the coverage and occlusion loss of the monitoring points, and establishes a multi-objective optimization function with the goals of minimizing monitoring blind spots, maximizing monitoring coverage, balancing the equipment load of monitoring points, and meeting airway route planning requirements; wherein the airway route includes information such as the preset flight path and flight altitude.

[0052] In the function model solving step S4, the multi-objective optimization function is iteratively solved based on a preset genetic algorithm to obtain the number and location of monitoring points in the target area. The multi-objective optimization function is configured as the following calculation model:

[0053]

[0054] Among them, m is the total number of monitoring points, n is the total number of regions, ω k is the weight of region k, which can be set according to the importance of the region and the potential density of flight activities. Higher weights are given to important regions and regions with frequent flight activities to ensure the monitoring coverage of these regions. jk is the coverage rate of monitoring point j to area k, L jk is the shading loss from area k to monitoring point j, the coverage rate is negatively correlated with the shading loss, λ1 and λ2 are weight coefficients, U j is the equipment load at monitoring point j, δ l is the deviation measure between the lth route and the monitoring coverage, p is the number of route plans in the target area, C(x j ) is the cost of monitoring point j, which includes construction cost and operation cost, B is the total budget cost, L max is the maximum threshold of occlusion loss, which is used to limit the maximum acceptable occlusion loss. min It is a preset coverage sensitivity threshold, which is used to limit the minimum coverage sensitivity.

[0055] This implementation enables high-precision monitoring point placement, significantly improving safety monitoring capabilities in low-altitude corridors. Utilizing a three-dimensional model and occlusion calculations, it avoids blind placement and improves resource utilization. Furthermore, an optimization algorithm minimizes monitoring blind spots and balances equipment load, thereby enhancing overall system stability and reliability. Furthermore, this method allows for flexible layout adjustments based on specific waterways, demonstrating excellent adaptability and scalability.

[0056] In the above multi-objective optimization function, the first objective term is used to constrain the maximization optimization of the effective coverage of each sub-region. By introducing the occlusion loss, the model tends to choose the monitoring point layout scheme with the smallest occlusion loss under the condition of the same weight, avoiding the high occlusion path. Based on the regional weight ω k It is possible to control the monitoring investment in important areas and achieve optimal configuration of regional monitoring points, thereby giving priority to high-precision and high-coverage monitoring of key areas and reducing the probability of monitoring blind spots. The second goal is to avoid the problem of high load at a single point by minimizing the sum of the squares of the equipment load at each monitoring point, thereby improving stability. The third goal is to ensure that the actual coverage range is close to the route path by minimizing the deviation metric. If the route passes through the area but the monitoring points are insufficiently covered, the deviation metric will increase significantly, making the layout of monitoring points more route-adaptive. The constraints are based on the cost of monitoring points to ensure that the optimization goal takes into account economy. Limited resources automatically adjust the number and location layout of monitoring points to better fit the actual scenario and facilitate implementation.

[0057] In the above embodiment, the airway route can be represented as a discrete sequence P, P = {p1, p2, ..., p n}, where each point p i =(x i ,y i ,z i ) contains three-dimensional coordinates, and the coverage of each monitoring device is modeled as a three-dimensional space area S j , such as sphere, cylinder, polyhedron, forming a test question, for each point p in the discrete sequence i Determine whether it is in at least one three-dimensional space region S j Mark it as covered or uncovered. The proportion of the covered path length to the total length is counted to obtain the deviation measure between the airway route and the monitoring coverage. When calculating the deviation measure between the airway route and the monitoring coverage, ensure that the airway route is in an area with good monitoring coverage as much as possible to reduce the impact of monitoring blind spots on the airway route. This embodiment formalizes the channel-coverage matching problem as a geometric relationship determination problem, and evaluates the overall monitoring quality by accurately calculating the coverage path ratio. Compared with the traditional method that is only based on rough calculations of fixed points or line segments, this method provides a higher-resolution evaluation mechanism, improves the accuracy of channel path monitoring, reduces the need for manual designation of blind areas through spatial mapping, and enhances the automatic adaptability of the system. It is especially suitable for low-altitude monitoring deployments in complex urban environments, mountainous areas or other high-interference areas.

[0058] In the above embodiment, for the discrete point p i The sampling density can be dynamically adjusted according to the flight speed or path complexity to improve the calculation efficiency and adaptability. The deviation calculation method can also further assign importance weights to different flight segments.

[0059] In some embodiments, the coverage attenuation function is used to represent the received power P r The nonlinear relationship with the actual coverage range is obtained by pre-fitting the measured data. If the received power P r If the coverage exceeds a certain strength threshold, the coverage is considered to be within the theoretical coverage range. The input of this coverage attenuation function is the received power value (e.g., dBm) of the monitoring device at a certain location, and the output is the actual coverage ratio at that location. This strength threshold can also be dynamically adjusted based on different device models and adaptive environmental feedback to improve model generalization capabilities.

[0060] The above coverage attenuation function can be replaced by exponential decay, Sigmoid function or neural network regression, and different fitting accuracy and generalization ability models can be selected according to different application scenarios.

[0061] The preset genetic algorithm of this embodiment is obtained by integrating the improved non-dominated sorting genetic algorithm NSGA-II and the simulated annealing algorithm. Specifically, the basic process of NSGA-II (selection, crossover, mutation, non-dominated sorting, and crowding calculation) is retained as the core of the global search. After the mutation operation or environment selection of NSGA-II, the local search of SA is introduced, and the mutated individuals are perturbed by SA. The new solution is accepted according to probability, and the multi-objective is converted into a weighted single objective. Specifically, refer to Figure 2 As shown, the function model solving step S4 further includes:

[0062] In the population initialization step S401, an initial population is randomly generated, where each individual is used to represent the sub-region and location of a set of monitoring points. The individuals are encoded using real numbers, which facilitates precise expression of the spatial location of the monitoring points and high-resolution adjustment. Each dimension in the initial population represents an individual.

[0063] In the population optimization step S402, the fitness of each individual is calculated according to the multi-objective optimization function, and selection, crossover and mutation operations are repeated based on the fitness, and the population is continuously updated until the preset termination condition is reached, and the optimal number of monitoring points and the location of monitoring points are output. Among them, the fitness includes multiple target dimensions such as coverage improvement, minimization of occlusion loss, equipment load balancing and minimum path deviation. The preset termination conditions include: reaching the maximum number of iterations or population convergence.

[0064] Based on this, the embodiments of the present application minimize the occlusion of monitoring points while minimizing monitoring blind spots, maximizing monitoring coverage, balancing equipment loads, and meeting the needs of airway route planning, thereby achieving the goal of no occlusion or a small amount of occlusion; it effectively integrates the global balancing ability of the NSGA-II algorithm in multi-objective optimization and the local perturbation advantages of the simulated annealing algorithm, which can significantly improve the diversity, globality, and convergence of the monitoring point layout plan.

[0065] In some embodiments, the population optimization step S4 further includes:

[0066] During the selection operation, the tournament selection method is used to randomly select a number of individuals (such as 2 to 5) from the current population to form a sub-population, and then select the individual with the best fitness value from the sub-population to enter the next generation. Based on this, in the context of the fusion of NSGA-II and simulated annealing, the tournament selection further enhances the probability that individuals on the Pareto frontier are retained first, thereby promoting the continuous evolution of elite solutions in subsequent operations. In this embodiment, due to the presence of multiple objectives, the "optimal" cannot be judged by a single numerical value, but the Pareto optimal dominance relationship must be adopted, and individuals with a lower "non-dominated level" are configured first. For example, the solution located at the non-dominated frontier of the first layer (Pareto Front 1) is better than the second layer; if multiple individuals belong to the same layer, the boundary diversity is evaluated based on their crowding distance, and individuals with a larger crowding degree are selected first (retaining marginal solutions and increasing the distribution range of solutions). In another embodiment, the embodiment of the present application can use a linear weighting or a dynamic weight adjustment mechanism to combine multiple objectives into a total fitness value.

[0067] In some embodiments, the population optimization step S4 further comprises:

[0068] During the crossover operation, the crossover probability is dynamically adjusted for crossover operation, and the crossover probability is set to:

[0069] P c0 is the preset initial crossover probability, such as 0.8, gen is the current number of iterations, gen max is the maximum number of iterations, and α is the attenuation coefficient, which is used to control the rate of decrease of the crossover probability. As the number of iterations increases, the crossover probability gradually decreases, reducing the crossover operation in the later stage and protecting the excellent individuals.

[0070] During the mutation operation, the mutation probability is set as:

[0071] P m0 is the preset initial mutation probability, such as 0.01, and β is the growth coefficient, which is used to control the rising speed of the mutation probability. As the number of iterations increases, the mutation probability gradually increases. In the later stage, diversity is enhanced to avoid premature convergence into the local optimal solution. During the mutation process, the generated monitoring point positions are checked for occlusion. If the new monitoring point position is in a severely occluded area, the mutation position is regenerated to ensure that the new monitoring point distribution avoids severely occluded areas as much as possible.

[0072] This implementation method dynamically adjusts the control strategies of crossover and mutation operations in the genetic algorithm to optimize the evolutionary path and improve the global optimization capability. The introduction of the occlusion detection mechanism ensures that the mutation not only meets the requirements of mathematical perturbation but also embeds physical feasibility constraints, thereby enhancing the practicality of the understanding and the feasibility of deployment. Whether the area is in a severely occluded area can be judged based on an occlusion threshold, which can be adaptively adjusted based on different application environments or building densities to improve adaptability.

[0073] In some embodiments, in the population optimization step S4, the evolved population is non-dominated sorted, and the population is divided into different Pareto levels according to fitness. The monitoring point layout corresponding to the non-dominated solution is preferentially retained, and an elite retention strategy is adopted to directly retain the excellent individuals in the previous generation to the next generation to retain the convergence of the algorithm. Among them, the excellent individuals are monitoring point configurations with excellent performance in key indicators such as high monitoring coverage, low occlusion loss, and small path deviation, such as being in the top 5% in multiple target dimensions.

[0074] In the above embodiment, during the iterative process of the genetic algorithm, the crossover probability and mutation probability satisfy the following constraints:

[0075]

[0076] Among them, p c0 is the initial crossover probability, set to 0.8 to 0.9, gen is the current number of iterations, gen max is the maximum number of iterations, and β is the cross probability attenuation coefficient, which can be set according to actual needs.

[0077] This implementation embeds non-dominated sorting and elite retention mechanisms in the optimization algorithm, which can accelerate the algorithm convergence process while maintaining search diversity: non-dominated sorting ensures that the optimal monitoring point solution is always retained, improving the quality of the optimal solution; the elite retention mechanism avoids the loss of key layout solutions due to evolutionary operations, improving the stability and robustness of the solution; the multi-target sorting structure improves the overall balance of the monitoring point solution among multiple indicators such as coverage, occlusion avoidance and path adaptation, and is suitable for multi-target deployment tasks of monitoring systems in complex airspaces.

[0078] In some embodiments, the monitoring equipment includes a low-altitude detection radar, and the shielding loss includes: free space propagation loss L fs_l , ionospheric absorption loss L iom and ground reflection loss L gr .

[0079] In the above embodiment, L fs_l =32.45+20lgd l +20lgf l , d lis the distance between the target and the radar. The low-altitude monitoring targets targeted by the embodiment of the present application include drones, helicopters, etc. l is the radar operating frequency, the ionospheric absorption loss L iom Calculated according to the International Reference Ionosphere Model IRI, which is built based on global ground-based observations and satellite data and is continuously updated. gr Related to the ground material and roughness, the reflection coefficient P of different ground materials gr Can be pre-tested or simulated,

[0080]

[0081] In another embodiment, the signal propagation model of the low-altitude detection radar of the present application also takes into account the terrain blocking loss L caused by the signal blocking caused by the height difference of the building. ter ,The terrain occlusion loss is calculated using high-precision digital elevation model DEM data combined with ray tracing algorithm.

[0082] Considering that low-altitude detection radar operates in the shortwave band, it is based on the reflection characteristics of electromagnetic waves between the ionosphere and the ground. Electromagnetic waves are reflected between the ionosphere and the ground, enabling the radar to detect low-altitude targets below the horizon that conventional radar cannot detect. To more accurately represent its signal propagation, the embodiment of the present application also incorporates the impact of the ground reflection characteristics caused by buildings and ground materials in urban environments into its signal propagation model.

[0083] Furthermore, when the signal propagation path passes through a building whose height exceeds the signal propagation line, the signal may be diffracted or blocked. Diffraction loss can be calculated using Kohler diffraction theory based on factors such as the building's shape and size, signal wavelength, and diffraction angle. A mesh model generated from the LiDAR point cloud can be used to determine the building's outline, and thus its shape and size. The signal wavelength can be determined based on low-altitude detection radar equipment parameters, and the diffraction angle can be calculated using the ratio of the model's edge curvature radius to the wavelength based on the principles of geometric optics, or by simulating the diffraction path using a high-precision model.

[0084] In some embodiments, the monitoring device further includes an ADS-B device, and the obstruction loss includes: free space propagation loss L fs_ads , atmospheric attenuation loss L a and obstacle occlusion loss L o .

[0085] In the above embodiment, L fs_ads =32.45+20lgd ads +20lgf ads, d in the free space propagation loss model ads is the distance between the target and the ADS-B device, f ads The operating frequency of ADS-B equipment.

[0086] In the above embodiment, when the signal is completely blocked by an obstacle, L o =0; when the signal is partially blocked, α i is the material attenuation coefficient of the i-th obstacle, such as the attenuation coefficient of concrete is 2.5dB / m, d i is the penetration path length, that is, the path length of the signal through the obstructed path.

[0087] Theoretically, in low-altitude urban airspace with few buildings, the 1090ES data link coverage area of the ADS-B equipment is approximately circular and large. However, if there are a few high-rise buildings, the coverage area will be irregularly reduced due to obstruction and interference. In real-world scenarios, 1090ES link signal propagation is also affected by atmospheric attenuation, obstructions, and co-channel interference.

[0088] Therefore, the signal propagation model of ADS-B equipment further considers the atmospheric attenuation loss L a , L a Calculated based on the atmospheric composition, water vapor content and signal propagation path of the target area, L a =γ·d ads ,Atmospheric composition and water vapor content can be obtained based on meteorological ,monitoring to determine the attenuation rate γ, which ranges from 0.01-0.03dB / km.

[0089] The atmospheric attenuation rate of the above embodiment may also be determined based on an atmospheric radiation transfer model, such as the medium-resolution atmospheric transmission model MODTRAN, in combination with meteorological conditions (temperature, humidity, air pressure, etc.) at the location of the target area.

[0090] In another embodiment, for the UAT mode data link, taking into account time division multiplexing, it is also necessary to consider the impact of signal conflict and interference on the signal power received by the ADS-B device. There are local holes in the impact range, and a conflict interference factor C is defined here. The UAT model uses the TDMA mechanism, and the signal conflict probability is positively correlated with the time slot occupancy rate. The conflict interference factor C can be expressed as P collision,k The probability of overlapping time slots between the kth interference source and the target signal is obtained by monitoring the occupancy status of the UAT frame structure (1 second / frame, containing 3200 time slots). If 10% of the time slots in a certain period are occupied, then P collision,k About 0.1, I kis the equivalent power of the kth interference source, which is calculated by measuring the co-frequency signal strength using a spectrum analyzer and deducting the free space loss.

[0091] In another embodiment, for VDL Mode 4, interference is analyzed and signal strength is corrected by establishing a signal coordination matrix.

[0092] In some embodiments, the monitoring device further includes a Remote ID device, and the shielding loss includes: free space propagation loss L fs_rid and obstacle occlusion loss L os .

[0093] In the above embodiment, L fs_rid =32.45+20lgd rid +20lgf rid , d in the free space propagation loss model rid is the distance between the target and the Remote ID device, f rid The operating frequency of the Remote ID device.

[0094] In the above embodiment, the obstacle blocking loss L os Expressed as: Among them, ρ i is the reflection coefficient of the obstacle, μ i is the absorption coefficient of the obstacle, and n is the number of obstacles in the signal propagation path. If there are interference sources such as a small number of metal buildings or communication base stations, the RF signal will be affected by strong reflection and absorption. Near the interference source, the monitoring range is severely limited, and it is only effective within the close proximity of the monitoring device. The shape of the obstacle is irregular due to the distribution of obstacles.

[0095] In addition, the ambient noise N needs to be considered, which is calculated by the noise power spectral density and the signal bandwidth.

[0096] Building on the above examples, this implementation proposes a model for the obstruction loss encountered during signal transmission based on the physical principles of different types of monitoring equipment. This provides a more accurate basis for coverage modeling and received power calculation. In actual deployments, the corresponding obstruction loss calculation model can be automatically matched to the device type, enabling precise assessment of the coverage capabilities of different monitoring devices in specific environments. By mapping monitoring devices to their signal loss models, the actual monitoring capabilities of various types of equipment in different airspace structures can be more accurately assessed.

[0097] In another embodiment, the monitoring device includes a spectrum detection device. The monitoring range of the device in the open area is larger than that in the terrain undulating area, the monitoring range of the interference source area is lower than that in the terrain undulating area, and there is a small blind area where the terrain is blocked. The blocking loss of the spectrum detection device includes: atmospheric attenuation loss L a and the influence of the same frequency electromagnetic interference signal, its atmospheric attenuation loss L a The same as the above embodiment.

[0098] In another embodiment, the above-mentioned monitoring equipment also includes a Doppler laser radar device, which works on the principle that the Doppler laser radar emits laser pulses and detects meteorological parameters by receiving aerosol backscattered signals. During the signal propagation process, the signal is mainly affected by the atmospheric attenuation loss L. a , the influence of aerosol scattering characteristics and optical component loss.

[0099] The intensity of the backscattered signal is determined by the aerosol scattering properties. Different types of aerosols (such as dust, smoke, and cloud droplets) have different scattering cross sections and scattering phase functions. Let σs be the aerosol scattering cross section, and the intensity of the scattered signal is proportional to σs. Furthermore, the aerosol concentration distribution also affects signal propagation. The variation of aerosol concentration with altitude can be determined through measurements or empirical models.

[0100] Among them, optical component loss is usually related to factors such as the material, processing accuracy, and usage time of optical components (such as transmitting telescopes, receiving telescopes, filters, etc.).

[0101] Under ideal conditions, such as a clear atmosphere, moderate aerosol concentrations, and good lidar performance, the inversion accuracy of wind speed and direction within 1000 meters can meet the requirement of an error of less than 0.5 m / s. However, when there is severe weather such as heavy rainfall or dense fog, atmospheric attenuation increases sharply, and aerosol scattering characteristics also change, resulting in weakened signal strength and a reduced effective detection range. For example, in heavy rain, wind field information may only be effectively detected within 500 meters.

[0102] At the same time, the lidar's scanning method (such as vertical scanning, horizontal scanning, conical scanning, etc.) will also affect its coverage. Different scanning methods can obtain wind field information in different spatial areas. By properly setting scanning parameters, effective monitoring of specific areas can be achieved.

[0103] In another embodiment, the monitoring equipment further includes a meteorological microwave radiometer, which operates by receiving microwave radiation from various gas molecules in the atmosphere, primarily water vapor and oxygen. Signal propagation from a meteorological microwave radiometer is primarily affected by atmospheric gas absorption, cloud scattering, and ground reflection. Atmospheric absorption loss is one of the primary causes of meteorological microwave radiation signal attenuation. Water vapor and oxygen have specific absorption lines in the microwave frequency band, and absorption loss varies with gas concentration, temperature, and pressure. Clouds scatter microwave radiation, especially when the cloud layer is thick. Cloud scattering is related to cloud type, cloud droplet size, and concentration distribution. Cloud scattering loss is specifically calculated using Mie scattering theory or other related scattering models.

[0104] Furthermore, ground reflections can also affect the signals received by microwave radiometers. Ground reflectivity depends on the surface material (such as water, land, or vegetation), and can vary significantly between different materials. When microwave signals reflect from the ground, they interfere with signals directly from the atmosphere, affecting the strength and phase of the received signal.

[0105] Meteorological microwave radiometers typically detect meteorological parameters such as water vapor and temperature within the troposphere, typically reaching several thousand meters. However, when clouds are thick or the water vapor content in the atmosphere is high, the signal attenuates significantly, reducing the vertical detection range. For example, under thick cloud cover, only meteorological parameters below the cloud layer may be detected.

[0106] In the horizontal direction, the detection range of a microwave radiometer depends primarily on the antenna's beamwidth and scanning range. By adjusting the antenna's pointing direction and scanning pattern, meteorological parameters in different areas can be monitored. Furthermore, ground reflections can cause the detection range to vary depending on the surface material. In areas with high reflectivity, such as water, signal interference can be significant, potentially affecting detection accuracy and range.

[0107] In another embodiment, the monitoring equipment further includes a forward scatterometer that determines visibility by measuring the Mie scattering coefficient. Its effective detection range is 50 m to 10 km, within which the scatterometer's measurement accuracy and reliability are high. When visibility is extremely low (e.g., below 50 m), the scattered signal is too strong and may exceed the instrument's measurement range, resulting in increased measurement error or inaccurate measurement. When visibility is high (e.g., greater than 10 km), the scattered signal is weak, potentially affecting the instrument's detection sensitivity and, consequently, measurement accuracy.

[0108] In the embodiment of the present application, the received power of the monitoring point is obtained by measurement, calculation, or a combination of the two.

[0109] ADS-B equipment (Automatic Dependent Surveillance-Broadcast) can directly measure the received power (RSSI) of 1090 MHz signals through an ADS-B receiver. Alternatively, when the transmit power (typical value: 250 W) and propagation path (free space loss model, obstacle attenuation) are known, power can be estimated using theoretical formulas. However, actual measurement and calibration are required in real-world environments.

[0110] The receiving power of the Remote ID device (UAV remote identification device) is mainly tested and can be achieved through equipment such as a power meter.

[0111] The received power of the low-altitude detection radar is calculated based on the radar equation, further corrected based on the shielding loss, or measured based on a power meter. For example, but not limited to, the received power of the low-altitude detection radar is expressed as:

[0112] Among them, P t is the transmission power, G t , G r are the transmitting antenna gain and the receiving antenna gain, and λ is the radar signal wavelength.

[0113] In spectrum detection equipment, the spectrum analyzer directly measures the signal strength within the received signal band by sweeping the frequency, which is used to detect illegal emissions, locate interference sources, etc.

[0114] The received power of the meteorological detection equipment is calculated based on the meteorological radar equation and further corrected based on the shielding loss.

[0115] The received power of navigation interference detection equipment (such as GPS interference detection) directly monitors the noise floor rise or signal distortion of the navigation frequency band (such as GPS L1: 1575.42MHz) and quantifies the interference intensity in real time.

[0116] The embodiment of the present application also constructs a monitoring equipment database, which stores monitoring information of multiple monitoring equipment, including: ADS-B equipment, RemoteL D equipment, low-altitude detection radar, spectrum detection, meteorological detection equipment, and navigation interference detection equipment. The equipment information includes but is not limited to: equipment model, equipment brand, monitoring means, and monitoring range; taking ADS-B equipment as an example, its communication mode, monitoring range, and application scenarios are different under different data link technologies. Specifically, in 1090ES mode, PPM modulation in the 1090MHz frequency band is used for communication, and the monitoring range at low altitude is approximately 100-150 kilometers (depending on the terrain and base station height); in UAT mode, 978MHz frequency band and continuous phase frequency shift keying CPFSK modulation are used for communication, and the monitoring range at low altitude is approximately 50-150 kilometers; in VDL Mode 4 mode, 118-137MHz VHF band and Gaussian minimum shift keying GMSK modulation are used for communication.

[0117] In another embodiment, the monitoring equipment database is regularly updated and maintained to ensure the timeliness and accuracy of the knowledge base. A data review mechanism is also established to ensure the quality of input data. This review mechanism includes: only allowing data entry from authoritative organizations, verified partners, or official documents, such as ISO standards and equipment manufacturer technical manuals; verifying the digital certificates of the source systems for data automatically collected by APIs, such as the CA certificate required for the ADS-B data interface of the Civil Aviation Administration; and identifying conflicting data through a rule base, such as when a radar's "maximum detection range" value exceeds the limits of physical laws.

[0118] In another embodiment, the monitoring equipment database is configured with a query interface, based on which the equipment information of the monitoring equipment can be quickly obtained during the monitoring point distribution optimization and route planning process, so as to match the appropriate monitoring equipment model according to the required monitoring range, terrain conditions and budget.

[0119] In another embodiment, the monitoring device database is further configured with a data preprocessing unit for performing preprocessing such as denoising and calibration on the data collected by the monitoring devices to unify the data format and timestamp. For example, radar data may be sampled multiple times per second, while meteorological data may be sampled once per minute, which requires interpolation or aggregation processing.

[0120] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0121] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for setting monitoring points for a low-altitude safety corridor, characterized in that: include: An airspace modeling step is used to construct a three-dimensional airspace model of a target area based on geographic information system (GIS) data, building information model (BIM) data, and LiDAR point cloud data, wherein the target area is divided into a plurality of sub-areas; In the data preprocessing step, monitoring equipment is installed in each of the sub-areas to establish a monitoring point, and the shielding loss of the monitoring point is determined in combination with the three-dimensional spatial model, and the receiving power of the monitoring point is determined based on a coverage attenuation function. r The corresponding actual coverage range is used to determine the coverage rate of all monitoring points in the sub-area based on the actual coverage range of the monitoring points; The function model construction step is to obtain a preset airway route, and establish a multi-objective optimization function by combining the coverage and occlusion loss of the preset airway route with the monitoring points; The function model solving step is to iteratively solve the multi-objective optimization function based on a preset genetic algorithm to obtain the number and location of monitoring points in the target area.

2. The method for setting monitoring points for a low-altitude safety corridor according to claim 1, characterized in that: The multi-objective optimization function is configured as the following calculation model: Among them, m is the total number of monitoring points, n is the total number of regions, ω k is the weight of region k, S jk is the coverage rate of monitoring point j to area k, L jk is the occlusion loss from area k to monitoring point j, λ1 and λ2 are weight coefficients, U j is the equipment load at monitoring point j, δ l is the deviation measure between the lth route and the monitoring coverage, p is the number of route plans in the target area, C(x j ) is the cost of monitoring point j, which includes construction cost and operation cost, B is the total budget cost, L max is the maximum threshold of occlusion loss, S min The preset coverage sensitivity threshold.

3. The method for setting monitoring points for a low-altitude safety corridor according to claim 2, characterized in that: The coverage attenuation function is used to represent the received power P r The nonlinear relationship with the actual coverage range is obtained by pre-fitting. If the received power P r If the intensity exceeds a threshold, the coverage is considered to be theoretical coverage.

4. The method for setting monitoring points for a low-altitude safety corridor according to claim 3, characterized in that: The function model solving step further includes: The population initialization step randomly generates an initial population, in which each individual is used to represent the sub-region where a group of monitoring points are located and the location of the monitoring points; The population optimization step calculates the fitness of each individual according to the multi-objective optimization function, repeats the selection operation, crossover operation and mutation operation based on the fitness, continuously updates the population until the preset termination condition is reached, and outputs the optimal number and location of monitoring points.

5. The method for setting monitoring points for a low-altitude safety corridor according to claim 4, characterized in that: The population optimization step further comprises: During the selection operation, a tournament selection method is used to randomly select a number of individuals from the current population to form a sub-population, and then the individual with the best fitness value is selected from the sub-population to enter the next generation.

6. The method for setting monitoring points for a low-altitude safety corridor according to claim 5, characterized in that: The population optimization step further comprises: During the crossover operation, the crossover probability is dynamically adjusted for crossover operation, and the crossover probability is set to: P c0 is the preset initial crossover probability, gen is the current number of iterations, gen max is the maximum number of iterations, α is the attenuation coefficient; During the mutation operation, the mutation probability is set as: P m0 is the preset initial mutation probability, β is the growth coefficient, and during the mutation process, the generated monitoring point position is subjected to occlusion detection. If the new monitoring point position is in a severely occluded area, the mutation position is regenerated.

7. The method for setting monitoring points for a low-altitude safety corridor according to claim 5, characterized in that: In the population optimization step, the evolved population is sorted by non-domination, and the population is divided into different Pareto levels according to fitness. Non-dominated solutions are retained first, and an elite retention strategy is adopted to directly retain the excellent individuals in the previous generation to the next generation.

8. The method for setting monitoring points for a low-altitude safety corridor according to any one of claims 1 to 7, characterized in that: The monitoring equipment includes a low-altitude detection radar, and the shielding loss includes: free space propagation loss L fs_l , ionospheric absorption loss L iom and ground reflection loss L gr .

9. The method for setting monitoring points for a low-altitude safety corridor according to any one of claims 1 to 7, characterized in that: The monitoring equipment also includes ADS-B equipment, and the obstruction loss includes: free space propagation loss L fs_ads , atmospheric attenuation loss L a and obstacle occlusion loss L o .

10. The method for setting monitoring points for a low-altitude safety corridor according to any one of claims 1 to 7, characterized in that: The monitoring device also includes a Remote ID device, and the shielding loss includes: free space propagation loss L fs_rid and obstacle occlusion loss L os .

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