Electromagnetic environment analysis method and system combining situational awareness and space modeling

Abstract

The present invention provides an electromagnetic environment analysis method and system combining situational awareness and spatial modeling, the method comprising: obtaining regional map information of a target area; constructing a regional three-dimensional model of the target area; generating device deployment points of a mobile detection device around the target area, configuring the drone detection task of an electromagnetic detection drone and the equipment detection task of an electromagnetic signal acquisition and analysis device; controlling the electromagnetic detection drone to perform the drone detection task in a silent communication mode, and controlling all electromagnetic signal acquisition and analysis devices to perform the equipment detection task; obtaining the first area detection result through the mobile detection device, and simultaneously obtaining the second area detection result of all electromagnetic signal acquisition and analysis devices; analyzing the radiation source position and radiation source type of the radiation source in the target area in combination with the first area detection result and the second area detection result. The present invention has the effect of efficiently completing electromagnetic environment analysis in a large area with complex terrain.

Description

Technical Field

[0001] The present invention belongs to the technical field of electromagnetic environment monitoring, and specifically relates to an electromagnetic environment analysis method and system combining situation awareness and space modeling. Background Art

[0002] With the widespread application of wireless communication technology, the electromagnetic environment has become increasingly complex. In order to ensure that various wireless communication systems can work properly and prevent electromagnetic interference from potentially affecting equipment safety, it is necessary to comprehensively analyze and manage the radiation sources in the electromagnetic environment of a specific area.

[0003] Traditional electromagnetic environment analysis methods have revealed many shortcomings in the face of these new challenges. These methods usually rely on fixed monitoring equipment, such as signal receivers and analyzers fixed at specific locations. Although these devices can provide continuous monitoring data, their coverage is limited and it is difficult to comprehensively monitor radiation sources in large areas. Especially in areas with complex terrain or remote areas, it is often costly and difficult to operate a sufficient number of fixed monitoring devices. Another common method is to use manually operated mobile devices for measurement and analysis. Although this method increases flexibility, it still has problems such as low efficiency, high labor costs, and high safety risks when facing large areas with complex terrain. Summary of the invention

[0004] The present invention provides an electromagnetic environment analysis method and system combining situational awareness and space modeling to solve the problem of low efficiency of existing electromagnetic environment analysis methods when facing large-scale areas with complex terrain.

[0005] In a first aspect, the present invention provides an electromagnetic environment analysis method combining situational awareness and space modeling, which is applied to an electromagnetic environment detection system, wherein the electromagnetic environment detection system includes three mobile detection devices with the same configuration, each of which includes a mobile drone base station and an electromagnetic signal acquisition and analysis device, and an electromagnetic detection drone is deployed in each of the mobile drone base stations. Any two of the mobile detection devices maintain a communication connection, the electromagnetic detection drone is in communication connection with all of the mobile drone base stations, and the electromagnetic detection drone can stay at any of the mobile drone base stations;

[0006] The method comprises the following steps:

[0007] Obtain regional map information of the target area where electromagnetic environment analysis is required;

[0008] Based on the regional map information and through the GIS system, a regional three-dimensional model of the target area is constructed;

[0009] Generate device deployment points of all the mobile detection devices located around the target area in the regional map information in combination with the regional three-dimensional model, wherein all the device deployment points are located at the edge of the regional boundary of the target area and outside the target area, and the straight-line distance between any one of the device deployment points and the other two of the device deployment points is equal;

[0010] Deploy all the mobile detection devices according to the device deployment points;

[0011] After all the mobile detection devices are deployed to the device deployment point, the drone detection task of the electromagnetic detection drone and the equipment detection task of the electromagnetic signal acquisition and analysis device are configured in combination with the device deployment point and the area range of the target area in the area map information;

[0012] Using the mobile drone base station to send the drone detection task to the electromagnetic detection drone currently deployed by the mobile drone base station;

[0013] Control all the electromagnetic detection drones to perform the drone detection task in silent communication mode, so that the electromagnetic detection drones detect the electromagnetic environment situation in the target area, and control all the electromagnetic signal acquisition and analysis equipment to perform the equipment detection task, so that the electromagnetic signal acquisition and analysis equipment detects the electromagnetic environment situation in the target area;

[0014] The mobile detection device obtains the first area detection results of all the electromagnetic detection drones when they complete the drone detection task, and simultaneously obtains the second area detection results of all the electromagnetic signal acquisition and analysis devices when they complete the device detection task;

[0015] The radiation source positions and radiation source types of all radiation sources in the target area are obtained by combining the first area detection result and the second area detection result for analysis.

[0016] Optionally, the generating of the device deployment points of all the mobile detection devices located around the target area in the regional map information by combining the regional map information and the regional three-dimensional model comprises the following steps:

[0017] Identifying the area range of the target area according to the area boundary of the target area in the area map information;

[0018] Taking the center of the largest inscribed circle in the target area as the range expansion base point, and generating a virtual area after the target area is proportionally enlarged according to a preset enlargement ratio;

[0019] taking an area between a virtual area boundary of a virtual area and the area boundary in the area map information as a preselected deployment area;

[0020] Using the regional three-dimensional model, all areas in the pre-selected deployment area that do not meet the preset detection requirements are screened out, and all the retained pre-selected deployment areas are used as target deployment areas;

[0021] Taking all the position coordinate points in the target deployment area as an initial deployment point set;

[0022] Randomly selecting three position coordinate points from the initial deployment point set as initial deployment points based on a preset distance constraint condition, wherein the distance constraint condition is that the straight-line distance between any one of the initial deployment points and the other two of the initial deployment points is equal;

[0023] The deployment point coverage area in the target area is calculated according to the deployment point coordinates of all the initial deployment points, the deployment point coverage area is the overlapping area between the complete coverage area formed by all the initial deployment points and the target area, and the complete coverage area is the area enclosed by the shortest straight line segment between any two of the initial deployment points;

[0024] Taking maximizing the coverage area of ​​the deployment points as the optimization goal, all the initial deployment points are adjusted to the optimal deployment points based on the distance constraint condition and using an optimization algorithm;

[0025] The optimal deployment point is used as the device deployment point of the mobile detection device.

[0026] Optionally, taking maximizing the coverage area of ​​the deployment points as the optimization goal, adjusting all the initial deployment points to the optimal deployment points based on the distance constraint and using an optimization algorithm comprises the following steps:

[0027] Constructing an objective function with maximizing the coverage area of ​​the deployment points as the optimization goal;

[0028] Constructing a first initial population of an optimization algorithm based on all of the initial deployment points;

[0029] Based on the distance constraint condition and taking approximation of the objective function as the optimization direction, the first initial population is iteratively updated using the optimization algorithm to obtain a first optimal population under the distance constraint condition;

[0030] If the first optimal population satisfies the objective function, an optimal deployment point is determined according to the first optimal population.

[0031] Optionally, the method further comprises the following steps:

[0032] If the first optimal population cannot satisfy the objective function, the distance constraint condition is adjusted so that the straight-line distance difference between any one of the initial deployment points and the other two initial deployment points is less than a preset difference threshold;

[0033] Using the first optimal population as a second initial population of the optimization algorithm;

[0034] Based on the adjusted distance constraint condition and taking approximation of the objective function as the optimization direction, the second initial population is iteratively updated using the optimization algorithm to obtain a second optimal population under the adjusted distance constraint condition;

[0035] The optimal deployment point is determined according to the second optimal population.

[0036] Optionally, the step of using the regional three-dimensional model to filter out all areas in the pre-selected deployment area that do not meet preset detection requirements, and taking all the retained pre-selected deployment areas as target deployment areas comprises the following steps:

[0037] Acquire the model heights of all boundary models located at the boundary of the target area in the regional three-dimensional model;

[0038] Mark the boundary model whose model height exceeds the obstacle height threshold in the preset detection requirement as an ultra-high boundary model, and obtain the model longitude and latitude coordinates of both ends of the ultra-high boundary model in the three-dimensional model of the region;

[0039] Generating a first prohibited deployment area that does not meet the preset detection requirements in the preselected deployment area of ​​the regional map information based on the model longitude and latitude coordinates;

[0040] Screening out all the first prohibited deployment areas in the pre-selected deployment areas, and taking all the retained pre-selected deployment areas as preferred deployment areas;

[0041] Based on the geological requirements for device deployment in the preset detection requirements and using the GIS system, a regional screening step is performed on the preferred deployment areas, and all the retained preferred deployment areas are used as target deployment areas.

[0042] Optionally, the step of performing a regional screening step on the preferred deployment area based on the device deployment geological requirements in the preset detection requirements and using a GIS system, and taking all the retained preferred deployment areas as target deployment areas comprises the following steps:

[0043] Acquiring regional geological information of the preferred deployment area through the GIS system;

[0044] Combining the regional geological information and the device deployment geological requirements in the preset detection requirements, generating a second prohibited deployment area in the preferred deployment area that does not meet the preset detection requirements;

[0045] All the second prohibited deployment areas in the preferred deployment area are screened out, and all the retained preferred deployment areas are used as target deployment areas.

[0046] Optionally, configuring the drone detection task of the electromagnetic detection drone and the equipment detection task of the electromagnetic signal acquisition and analysis device in combination with the device deployment point and the area range of the target area in the area map information includes the following steps:

[0047] Based on the device deployment point and according to the device preset parameters of the electromagnetic signal acquisition and analysis device, generating the device perception range of the electromagnetic signal acquisition and analysis device in the regional map information;

[0048] Identifying the area range of the target area according to the area boundary of the target area in the area map information;

[0049] Eliminate the overlapping portion of the area range with the sensing range of the device, and use the remaining area range as the detection range of the electromagnetic detection drone;

[0050] Based on the detection range and the device deployment point, respectively configure the drone detection task of the electromagnetic detection drone in each of the mobile drone base stations;

[0051] The device detection task of the electromagnetic signal acquisition and analysis device is configured in combination with the device sensing range and the drone detection time in the drone detection task.

[0052] Optionally, the drone detection task of configuring the electromagnetic detection drone in each of the mobile drone base stations based on the detection range and the device deployment point includes the following steps:

[0053] Determine an initial range division point in the detection range based on the device deployment point, wherein the straight-line distances between the initial range division point and all the device deployment points are equal;

[0054] generating a segmentation boundary in the detection range by the shortest straight line between the initial range segmentation point and the device deployment point;

[0055] Assigning a base station detection range to each of the mobile drone base stations in combination with the segmentation boundary and the range boundary of the detection range;

[0056] For each of the mobile drone base stations, based on the detection range of the base station and with another mobile drone base station as the path endpoint, an optimal path algorithm is used to generate the drone optimal path of the electromagnetic detection drone, and all the mobile drone base stations can only serve as the path endpoint of one drone optimal path at most in the same drone detection mission;

[0057] Calculating the flight time of the electromagnetic detection drone in the optimal path of the drone based on the drone flight parameters preset by the mobile drone base station;

[0058] If the flight time is the maximum flight time among the flight times calculated by all the mobile drone base stations, configuring the drone detection task of the electromagnetic detection drone in combination with the drone optimal path and the drone flight parameters;

[0059] If the flight time is not the maximum flight time among the flight times calculated by all the mobile UAV base stations, the UAV flight parameters are adjusted until the flight time is the same as the maximum flight time, and the UAV detection mission of the electromagnetic detection UAV is configured in combination with the optimal UAV path and the adjusted UAV flight parameters.

[0060] Optionally, the analyzing and obtaining the radiation source positions and radiation source types of all radiation sources in the target area by combining the first area detection result and the second area detection result comprises the following steps:

[0061] Combining the first area detection result and the second area detection result to analyze and obtain the electromagnetic environment situation awareness result in the target area;

[0062] Identify the radiation source longitude and latitude data of all radiation sources in the target area based on the electromagnetic environment situation awareness result;

[0063] Based on the latitude and longitude data of the radiation sources and using a clustering classification algorithm, a clustering analysis is performed on all the radiation sources to obtain the radiation source types of all the radiation sources.

[0064] In a second aspect, the present invention also provides an electromagnetic environment analysis system combining situational awareness and spatial modeling, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electromagnetic environment analysis method combining situational awareness and spatial modeling as described in the first aspect is implemented.

[0065] The beneficial effects of the present invention are:

[0066] The present invention can more accurately describe and analyze the geographical features and electromagnetic environment of the target area by combining the GIS system and three-dimensional modeling technology, providing a solid foundation for subsequent detection tasks. Secondly, the uniformly distributed device deployment points generated by the intelligent algorithm ensure comprehensive coverage of the periphery of the target area, greatly improving the comprehensiveness and accuracy of the detection. Furthermore, the present invention combines mobile detection devices, electromagnetic detection drones and electromagnetic signal acquisition and analysis equipment to form a multi-level and multi-angle detection network, which can simultaneously perform a full range of electromagnetic environment analysis of the target area from the ground and the air. In particular, the use of electromagnetic detection drones not only expands the detection range, but also can flexibly respond to complex terrain and environmental conditions, overcoming the limitations of traditional fixed equipment and manually operated mobile devices. In addition, the silent communication mode is used to perform the detection task, which minimizes the impact of the detection process on the electromagnetic environment of the target area and ensures the authenticity and reliability of the detection results. At the same time, the mobile drone base station controls multiple drones to work together, greatly improving the detection efficiency and coverage. Finally, the present invention can more comprehensively and accurately determine the location and type of each radiation source in the target area by comprehensively analyzing the detection results from different sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a schematic diagram of the system structure of an electromagnetic environment detection system in one embodiment of the present application.

[0068] Figure 2 This is a flow chart of an electromagnetic environment analysis method combining situational awareness and space modeling in one embodiment of the present application.

[0069] Figure 3 This is a schematic diagram of the target area and device deployment points in one embodiment of the present application.

[0070] Figure 4 This is a schematic diagram of a virtual area, a pre-selected deployment area, a complete coverage area, and a deployment point coverage area in one embodiment of the present application.

[0071] Figure 5 This is a schematic diagram of a prohibited deployment area in one embodiment of the present application.

[0072] Figure 6 A schematic diagram of generating the optimal path for a drone within the device perception range and the target area in one embodiment of the present application. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0074] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0075] The present invention discloses an electromagnetic environment analysis method combining situational awareness and space modeling, referring to Figure 1 The electromagnetic environment analysis method combining situational awareness and space modeling is applied to the electromagnetic environment detection system. The electromagnetic environment detection system includes three mobile detection devices with the same configuration. Each mobile detection device includes a carrier, a mobile drone base station and electromagnetic signal acquisition and analysis equipment mounted on the carrier. An electromagnetic detection drone is deployed in each mobile drone base station. Any two mobile detection devices maintain communication connection. The electromagnetic detection drone is connected to all mobile drone base stations. The electromagnetic detection drone can stay at any mobile drone base station. The electromagnetic detection drone is equipped with a mobile electromagnetic monitoring lateral system. The electromagnetic detection drone usually has the following performance parameters:

[0076] Monitoring / direction finding frequency range: 20~8000MHz.

[0077] Real-time IF bandwidth: 240MHZ.

[0078] Scan speed: >150GHz / s (25kHz step).

[0079] IF phase noise: ≤-100dBc / Hz@10kHz(f=1GHz).

[0080] Modulation measurement and analysis: AM / FM / CW / ASK / PSK / DPSK / QAM / FSK / MSK / QPSK, etc.

[0081] Number of DDC channels:>32.

[0082] Burst signal interception capability: 20us (100% probability).

[0083] Channel scanning speed: >500ch / s (25kHz channel spacing)

[0084] System weight: ≤9kg.

[0085] Working temperature: -20℃~+55.

[0086] Electromagnetic signal acquisition and analysis equipment consists of electromagnetic signal acquisition module, electromagnetic signal analysis module, detection antenna, display and control terminal, electromagnetic environment situation awareness module and analysis software. Electromagnetic signal acquisition and analysis equipment usually has the following performance parameters:

[0087] Frequency range: 100kHz-9GHz.

[0088] Instantaneous bandwidth: 80Mhz.

[0089] Sweep rate: up to 200GHz / S (RBW>100kHz).

[0090] DANL is as low as -161dBm / Hz (>30MHz and above frequency band).

[0091] Phase noise is as low as -105dBc / Hz@10kHz1GHz carrier.

[0092] Analog demodulation: AM, FM, WFM, NFM, USB, LSB, CW.

[0093] Detection mode: normal, sampling, positive peak, negative peak, RMS.

[0094] It has the functions of intermediate frequency analysis, panoramic spectrum scanning, real-time scanning, discrete scanning, differential spectrum analysis, analog signal analysis, digital signal demodulation, signal recognition, etc.

[0095] Equipment interface: IF output, RF input, power switch, reference input, data communication port, etc.

[0096] Electromagnetic signal acquisition and analysis equipment can use efficient modeling and real-time rendering technology to unify the modeling of terrain and electromagnetic data in the space electromagnetic environment, combine the influence of antenna and feeder systems, and terrain parameters to calculate the electromagnetic propagation and coverage of various emission sources, analyze the interference effect and electromagnetic spectrum occupancy situation. Visualize the electromagnetic environment from multiple dimensions, intuitively display the distribution of the electromagnetic environment based on electronic maps, and conduct electromagnetic compatibility analysis and electromagnetic radiation assessment.

[0097] Figure 2 FIG. 1 is a flow chart of an electromagnetic environment analysis method combining situational awareness and space modeling in one embodiment. It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps in the above method may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps. Figure 2 As shown, the electromagnetic environment analysis method combining situational awareness and space modeling disclosed in the present invention specifically includes the following steps:

[0098] S101. Obtain regional map information of a target area requiring electromagnetic environment analysis.

[0099] First, the geographic boundaries of the target area need to be clearly defined, which can be administrative divisions, natural geographic units, or specific project areas. Then, collect terrain data for the area, including elevation, slope, land cover type, etc. These data usually come from open source geographic data platforms. In actual operation, professional geographic information system software, such as ArcGIS or QGIS, can be used to process and integrate these data. For example, you can first import the basic terrain layer and then overlay satellite images to obtain the latest surface information. After that, add vector data layers such as roads, buildings, obstacles, etc., and ensure that all data use a unified coordinate system and projection method.

[0100] S102. Construct a regional three-dimensional model of the target area based on the regional map information and through the GIS system.

[0101] Among them, the basic three-dimensional model of the terrain is created using digital elevation model (DEM) data. DEM data is usually stored in raster format, and each pixel represents the altitude of a geographical location. Using the three-dimensional modeling function of GIS software, these elevation data can be converted into continuous three-dimensional surfaces. For example, in ArcGIS, you can use the "Create TIN" tool to generate a triangulated irregular network (TIN) from DEM, and then use the "TIN to Raster" tool to create a smoother surface. Next, add surface cover information. This includes vegetation, water bodies, buildings, etc. For vegetation, the Normalized Difference Vegetation Index (NDVI) in satellite remote sensing data can be used to distinguish different types and densities of vegetation, and represented in the three-dimensional model with three-dimensional objects of different heights and textures. Water bodies can be simulated by setting specific reflectivity and smoothness.

[0102] 3D modeling of buildings is the most complex part. First, start with 2D building outline data, which is usually stored in vector format. Then assign height information to each building. This can be obtained through field measurements, LiDAR data, or estimation. Using the 3D modeling functions of GIS software, such as ArcGIS's "Extrude" tool, the 2D outlines can be converted into 3D building models. For complex buildings, it may be necessary to create a more detailed model using specialized 3D modeling software (such as SketchUp or CityEngine) and then import it into the GIS system.

[0103] S103. Generate device deployment points of all mobile detection devices located around the target area in the regional map information by combining the regional map information and the regional three-dimensional model.

[0104] Among them, refer to Figure 3 First, the precise boundary of the target area needs to be determined in the GIS system. Then a buffer is created outside the boundary of the target area. The width of this buffer depends on the effective detection range of the mobile detection device and the size of the target area. Then, the area in the buffer that is not suitable for deploying mobile detection devices is screened out through the regional three-dimensional model. Next, evenly distributed candidate deployment points are generated in this buffer, and then a spatial clustering algorithm (such as K-means) is applied to select the optimal point set (such as Figure 3 The key is to ensure that any deployment point is equidistant from the two closest deployment points. This can be achieved by following these steps:

[0105] 1. Initialization: Randomly select N points in the buffer as the initial deployment points.

[0106] 2. Calculate the distance: For each point, calculate its distance to all other points.

[0107] 3. Adjust the position: Move each point so that it forms an equilateral triangle with the two nearest points. You can use an iterative method to adjust the position of the point slightly each time until the desired accuracy is achieved.

[0108] 4. Repeat steps 2 and 3 until the positions of all points are stable.

[0109] Specifically, the adjustment process of the point can be expressed by the following formula: P'i=Pi+α*(((Pj+Pk) / 2)-Pi)

[0110] Among them, Pi is the position of the current point, Pj and Pk are the positions of the two nearest points, α is the adjustment coefficient (0<α<1), and P'i is the new position after adjustment.

[0111] The main advantage of this deployment method is that it can achieve uniform coverage of the target area, minimize detection blind spots, and provide an ideal geometric configuration for subsequent triangulation. By ensuring that the deployment points are equidistant, the signal strength analysis and source location algorithms can be simplified, improving positioning accuracy.

[0112] S104. Deploy all mobile detection devices according to the device deployment points.

[0113] S105. After all mobile detection devices are deployed to the device deployment point, the drone detection task of the electromagnetic detection drone and the equipment detection task of the electromagnetic signal collection and analysis equipment are configured in combination with the device deployment point and the area range of the target area in the area map information.

[0114] The detection task is configured based on the specific location of the device deployment point and the scope of the target area in the regional map information. The detection task should consider the following key factors:

[0115] Coverage: Ensure that the entire target area is within the detection range, leaving no blind spots.

[0116] Detection frequency: The detection frequency and interval are determined based on the complexity and change rate of the electromagnetic environment in the target area.

[0117] Resolution: Determines the spatial and temporal resolution of the detection to meet the analysis requirements.

[0118] Energy efficiency: Optimize detection paths and times to maximize battery life and detection efficiency.

[0119] Detection parameters: Set appropriate frequency range, signal strength threshold and other parameters according to the characteristics of the target area and the purpose of detection.

[0120] For electromagnetic detection drones, the following specific tasks need to be configured:

[0121] Flight Path: Design a flight path that covers the entire target area. Usually a grid or spiral path is used to ensure uniform coverage. For example, the following algorithm can be used to generate a grid path:

[0122] a. Divide the target area into grids of equal size.

[0123] b. Calculate the coordinates of the center point of each grid.

[0124] c. Generate the shortest path connecting all center points (such as using the traveling salesman problem algorithm).

[0125] Flight altitude: The optimal flight altitude is determined based on the terrain, building height, and performance characteristics of the detection equipment. Typically, the flight altitude requires a balance between signal coverage and resolution.

[0126] Flight speed: Set an appropriate flight speed to ensure the quality and continuity of detection data. Too fast a speed may result in insufficient data sampling, while too slow a speed will affect detection efficiency.

[0127] Detection frequency range: Set an appropriate frequency scanning range based on the type of electromagnetic signals that may exist in the target area. For example, if you are interested in mobile communication signals, you may need to cover the frequency band from 700MHz to 2.6GHz.

[0128] Signal strength threshold: Set the minimum strength threshold for signal detection to filter out background noise.

[0129] Data Sampling Rate: Set the data sampling frequency according to the desired time resolution.

[0130] Battery management strategy: Set battery level warning thresholds and automatic return points.

[0131] For ground-based electromagnetic signal acquisition and analysis equipment, the following tasks need to be configured:

[0132] Detection Schedule: Set the time interval for regular detection, and you may need to adjust the frequency according to the level of electromagnetic activity at different times.

[0133] Frequency scanning range: Similar to drones, but may cover a wider frequency range because ground equipment usually has more processing power.

[0134] Signal analysis parameters: Set algorithm parameters for signal identification and classification, such as the threshold for signal modulation type identification.

[0135] Data storage and transfer strategy: Determine the local storage capacity of data and the frequency of transfer to the central system.

[0136] Interference Detection Threshold: Set the signal strength and frequency characteristic thresholds used to identify potential interference sources.

[0137] During the configuration process, you also need to consider the coordination between devices. For example:

[0138] Time synchronization: Ensuring that the clocks of all devices are synchronized is critical for subsequent data fusion and analysis.

[0139] Frequency allocation: Whenever possible, assign different frequency bands to different devices to improve overall detection efficiency.

[0140] Data cross-validation: Set some overlapping detection areas for cross-validation of data between different devices.

[0141] Dynamic task adjustment: Allows the system to dynamically adjust task parameters based on real-time detection results. For example, if an abnormal signal is detected in a certain area, the sampling frequency of that area can be increased.

[0142] After configuration is complete, a comprehensive system test is required, including:

[0143] Communication test: Ensure that all devices can communicate stably with the control center.

[0144] Data consistency test: Verify the data consistency of different devices in the overlapping area.

[0145] Task execution test: simulates the actual detection process to ensure that all equipment can work normally according to the scheduled tasks.

[0146] Abnormal situation handling test: simulate various possible abnormal situations (such as equipment failure, communication interruption, etc.) to test the system's response capabilities.

[0147] Through this comprehensive and detailed task configuration process, it can ensure that the entire electromagnetic environment detection system can operate efficiently and reliably, providing high-quality raw data for subsequent data analysis. This method not only improves the efficiency and accuracy of detection, but also enhances the flexibility and adaptability of the system, and can cope with various complex electromagnetic environment monitoring needs.

[0148] S106. Use the mobile drone base station to send the drone detection mission to the electromagnetic detection drone currently deployed by the mobile drone base station.

[0149] Among them, the mobile drone base station, as the hub of the entire system, needs to have strong computing and communication capabilities. It is usually equipped with a high-performance processor, a large-capacity storage device, and a multi-mode communication module. The main functions of the base station include:

[0150] Mission planning and scheduling: Generate specific UAV flight paths and detection parameters based on the detection mission configured in the previous steps.

[0151] Real-time communication: Maintain continuous two-way communication with electromagnetic detection drones.

[0152] Data processing and storage: Receive, process and store detection data sent back by drones.

[0153] Status monitoring: real-time monitoring of the drone’s flight status, battery charge, detection equipment working status, etc.

[0154] Before sending a mission to the drone, the mobile drone base station needs to perform the following steps:

[0155] Path Planning: Generate an optimized flight path for each drone, taking into account factors such as terrain, obstacles, no-fly zones, etc.

[0156] Task parameter setting: According to the detection requirements, set the specific parameters of each task, such as detection frequency range, signal strength threshold, data sampling rate, etc.

[0157] The task sending process usually takes the following steps:

[0158] Establishing a communication link: The base station first establishes a secure communication link with the target drone. This usually uses an encrypted wireless communication protocol such as AES encrypted WiFi or a dedicated drone control channel.

[0159] Authentication: The base station and drone verify each other’s identity to ensure secure communications. This may involve the exchange of digital certificates or encryption keys.

[0160] Mission data packaging: The base station packages the mission information into a standard format. A typical mission data packet may contain the following information: mission ID, flight path coordinate sequence, detection parameters (frequency range, sampling rate, etc.), time window (start time, end time), and emergency handling instructions.

[0161] Data transmission: Use a reliable transmission protocol (such as TCP / IP) to send mission data packets to the drone.

[0162] Mission activation: After receiving the confirmation, the base station sends a mission activation command and the drone starts to execute the mission.

[0163] The mobile drone base station can effectively control and coordinate the work of multiple electromagnetic detection drones. This not only ensures the accurate execution of the detection mission, but also improves the flexibility and efficiency of the entire system. The base station can dynamically adjust the mission according to the real-time situation. For example, when an abnormal signal is found, it can immediately send additional detection instructions to nearby drones. This method greatly enhances the adaptability and response speed of the electromagnetic environment detection system, enabling it to better cope with complex and changing electromagnetic environments.

[0164] S107. Control all electromagnetic detection drones to perform drone detection tasks in silent communication mode, so that the electromagnetic detection drones detect the electromagnetic environment situation in the target area. At the same time, control all electromagnetic signal acquisition and analysis equipment to perform equipment detection tasks, so that the electromagnetic signal acquisition and analysis equipment detects the electromagnetic environment situation in the target area.

[0165] Among them, the silent communication mode means that the drone reduces its own electromagnetic radiation as much as possible when performing detection tasks, so as not to interfere with the electromagnetic environment of the target area. The following technologies are usually used to achieve the silent communication mode:

[0166] Low-power communication: Use ultra-low-power communication modules to perform short-distance, low-bandwidth data transmission only when necessary.

[0167] Optical communications: When possible, use lasers or infrared for short-range communications, avoiding RF radiation altogether.

[0168] Passive reception: The drone is mainly in a passive reception state and only transmits back when it receives specific instructions from the base station.

[0169] Time division multiplexing: Multiple drones take turns communicating with a base station according to a predetermined schedule, reducing the need for simultaneous communications.

[0170] Directional Antennas: Use highly directional antennas to communicate only in the necessary direction, reducing radiation spread.

[0171] When performing a drone detection mission, each drone flies along the path generated by the base station while detecting electromagnetic signals. The detection process usually includes the following steps:

[0172] Signal scanning: The electromagnetic detection equipment onboard the drone continuously scans a preset frequency range. For example, software defined radio (SDR) technology can be used to achieve flexible frequency scanning.

[0173] Signal detection: When a signal exceeding a preset threshold is detected, the signal characteristics such as frequency, strength, direction, etc. are recorded. This is usually achieved using digital signal processing (DSP) technology to achieve real-time analysis.

[0174] Data logging: Detected signal characteristics are associated with current timestamps and GPS coordinates and stored in local memory.

[0175] Preliminary analysis: The drone may perform some preliminary signal analysis, such as modulation type identification, signal classification, etc., to reduce the amount of data that needs to be transmitted.

[0176] Anomaly reporting: If a particularly strong or unusual signal is detected, the drone may immediately report it to the base station, even if this may temporarily break the silence.

[0177] At the same time, electromagnetic signal collection and analysis equipment on the ground is also performing detection tasks. These devices usually have stronger processing capabilities and wider frequency coverage. Their working process includes:

[0178] High-precision analysis: Use high-performance signal processors to perform real-time signal analysis, including signal demodulation, feature extraction, interference identification, etc.

[0179] Direction finding: Use a multi-antenna array to estimate the direction of arrival (DOA) of the signal to help locate the source of the radiation.

[0180] Data storage: Store the collected raw data and analysis results in large-capacity storage devices to prepare for subsequent in-depth analysis.

[0181] Real-time alarm: When a preset abnormal signal is detected, an alarm is immediately sent to the control center.

[0182] S108. Obtain the first area detection results of all electromagnetic detection drones when they complete the drone detection task through the mobile detection device, and simultaneously obtain the second area detection results of all electromagnetic signal acquisition and analysis equipment when they complete the equipment detection task.

[0183] Among them, the drone may use a variety of communication methods to transmit data back to the mobile detection device, including high-bandwidth wireless links: such as 4G / 5G cellular networks or dedicated long-distance wireless communication systems; satellite communications: suitable for long distances or complex terrain situations; optical communications: such as laser communications, suitable for close distances, high bandwidth, and low interference requirements. Strong encryption algorithms (such as AES-256) are used during data transmission to ensure security. Use efficient data compression algorithms (such as LZMA2) to reduce the amount of transmitted data. The first area detection result data usually uses a standardized data format, such as JSON or ProtocolBuffers, to facilitate data exchange between different systems. The information contained in the first area detection result data may include timestamps, GPS coordinates, detected signal characteristics (frequency, strength, direction, etc.), and drone status information (battery power, altitude, speed, etc.).

[0184] The second area detection result data collected by the electromagnetic signal acquisition and analysis equipment may include original signal data, spectrum analysis results, signal modulation type identification results, direction finding results, etc. Since the electromagnetic signal acquisition and analysis equipment is usually fixed, a wired network (such as optical fiber) can be used for high-speed and reliable data transmission. For remote locations or locations where it is difficult to access a wired network, a wireless link with a high-gain directional antenna can be used. Use a precise time synchronization mechanism (such as NTP or PTP) to ensure that the timestamps of all devices are consistent. Implement a data synchronization protocol to ensure that data from different devices can be properly aligned.

[0185] Align the data from the drone and ground equipment in time and space. Use data fusion algorithms (such as Kalman filtering) to merge data from different sources to improve overall accuracy. Perform spatial interpolation of discrete measurement points to generate a continuous electromagnetic field intensity distribution map. Geostatistical methods such as Kriging or Inverse Distance Weighted (IDW) can be used. Perform time series analysis on continuously collected data to identify time-varying patterns in the electromagnetic environment. Methods such as Fourier transform or wavelet analysis can be used. Combine data from multiple measurement points and use triangulation or multi-point positioning algorithms to estimate the location of the radiation source. Considering factors such as terrain and buildings, it may be necessary to use advanced propagation models such as ray tracing.

[0186] S109. Analyze and obtain the radiation source positions and radiation source types of all radiation sources in the target area by combining the first area detection result and the second area detection result.

[0187] In one embodiment, combining the regional map information and the regional three-dimensional model to generate the device deployment points of all mobile detection devices located around the target area in the regional map information includes the following steps:

[0188] Identify the area scope of the target area according to the area boundary of the target area in the area map information;

[0189] Taking the center of the largest inscribed circle in the target area as the range expansion base point, and generating a virtual area after the target area is proportionally enlarged according to a preset enlargement ratio;

[0190] taking an area between a virtual area boundary and an area boundary of the virtual area in the area map information as a pre-selected deployment area;

[0191] Using the regional three-dimensional model, all areas in the pre-selected deployment area that do not meet the preset detection requirements are screened out, and all the retained pre-selected deployment areas are used as target deployment areas;

[0192] All the location coordinate points in the target deployment area are used as the initial deployment point set;

[0193] Based on the preset distance constraint, three position coordinate points are randomly selected from the initial deployment point set as the initial deployment points. The distance constraint condition is that the straight-line distance between any initial deployment point and the other two initial deployment points is equal;

[0194] The deployment point coverage area in the target area is calculated based on the deployment point coordinates of all initial deployment points. The deployment point coverage area is the overlapping area between the complete coverage area formed by all initial deployment points and the target area. The complete coverage area is the area enclosed by the shortest straight line segment between any two initial deployment points.

[0195] Taking maximizing the coverage area of ​​deployment points as the optimization goal, all initial deployment points are adjusted to the optimal deployment points based on distance constraints and using optimization algorithms;

[0196] The optimal deployment point is used as the device deployment point of the mobile detection device.

[0197] In this embodiment, refer to Figure 4, identifying the regional scope of the target area according to the regional boundaries of the target area in the regional map information is a key initial step, which lays the foundation for subsequent analysis and planning. This process usually involves a variety of geographic information system (GIS) technologies and image processing methods. First, it is necessary to load and process regional map information, including vector data (such as shapefiles) and raster data (such as satellite images). For vector data, boundary information can be directly extracted; for raster data, image segmentation and edge detection algorithms need to be used. Commonly used edge detection algorithms include Canny algorithm, Sobel algorithm, etc. For example, the steps of using the Canny algorithm include: Gaussian filtering denoising, calculating image gradient, non-maximum suppression, dual threshold detection and edge tracking.

[0198] The identified boundaries may require further processing, such as smoothing (using the Douglas-Peucker algorithm) and topological error correction. The results of boundary identification are usually stored in the form of polygons, with each vertex having precise geographic coordinates. This polygon represents the precise extent of the target area and provides a spatial reference for subsequent analysis. During implementation, challenges that may be encountered include dealing with complex terrain features (such as coastlines), dealing with changes in administrative boundaries, and dealing with errors and inconsistencies in data sources. To address these challenges, techniques such as multi-temporal data comparison and geocoding correction can be used. In addition, it is necessary to consider the conversion between different coordinate systems to ensure that all data is processed in the same coordinate system.

[0199] Next, it is necessary to find the largest inscribed circle in the identified target area. This can be achieved by the following steps: 1) discretize the target area into a grid; 2) for each grid point, calculate the shortest distance to the boundary; 3) select the point with the largest distance as the center of the circle, and its distance to the boundary is the radius. This process can be optimized using a distance transformation algorithm (such as the Euclidean distance transformation). In practical applications, computational efficiency may need to be considered, especially for large or complex-shaped areas. One optimization method is to use a quadtree structure to recursively divide the space and quickly exclude areas that cannot contain the largest inscribed circle.

[0200] After finding the largest inscribed circle, its center coordinates (x_c, y_c) will be used as the base point for range expansion (such as Figure 4 Assuming that the preset enlargement ratio is k (k>1), the enlargement process can be described as follows: for each point (x, y) on the boundary of the target area, the corresponding point (x', y') on the boundary of the new virtual area can be calculated by the following formula:

[0201] x'=x_c+k(x-x_c)

[0202] y'=y_c+k(y-y_c)

[0203] This transformation maintains the shape of the target area and only increases its size proportionally. In actual operation, a large number of boundary points may need to be processed, so vectorized operations can be considered to improve efficiency. In addition, special cases should be handled carefully, such as the possibility of self-intersection of the sunken area after expansion, in which case additional geometric operations are required to solve the topological problem. The boundary of the enlarged virtual area may require further processing, such as smoothing or simplification, to reduce computational complexity. The Douglas-Peucker algorithm can be used to simplify the boundary while maintaining the main shape features. In addition, it is necessary to ensure that the enlarged virtual area does not conflict with other important geographical features (such as protected areas, water bodies, etc.), and spatial analysis and adjustment need to be combined with GIS data. The virtual area provides a larger operating space for subsequent deployment point selection while maintaining geometric similarity with the original target area. By adjusting the expansion ratio k, the size of the virtual area can be flexibly controlled to balance coverage and resource utilization.

[0204] The area between the virtual area boundary and the area boundary of the virtual area in the area map information is used as the pre-selected deployment area. This process is actually performing a spatial difference operation, that is, "subtracting" the original target area from the enlarged virtual area to obtain a ring area. The specific implementation process is as follows: First, both the original target area and the virtual area need to be represented as polygon objects. This usually involves the processing of vector data, and each polygon is defined by a series of vertex coordinates. Then, a polygon difference operation is performed using a spatial analysis library (such as GDAL, Shapely, or ArcGIS's geometry processing tools). Mathematically, this operation can be expressed as: pre-selected deployment area = virtual area - target area, where "-" represents a spatial difference operation.

[0205] In actual operation, if the shape of the target area is very complex or contains multiple discontinuous parts, the difference operation may produce multiple separated polygons. In this case, it is necessary to decide whether to use all these separated areas as pre-selected deployment areas or to select only the largest continuous area. In order to improve computational efficiency, especially for large-scale or high-resolution map data, spatial indexing technology (such as R-tree) can be used to accelerate geometric operations. In addition, if the shape of the pre-selected deployment area is too complex, it may be necessary to simplify it, such as using the Douglas-Peucker algorithm to reduce the number of vertices while maintaining the main shape features.

[0206] The regional 3D model is used to screen out all areas in the pre-selected deployment area that do not meet the preset detection requirements, and all retained pre-selected deployment areas are used as target deployment areas. The purpose of this step is to ensure that the final selected deployment area is not only geographically suitable, but also meets the requirements in terms of electromagnetic detection effect. Specifically, the regional 3D model needs to be loaded and processed first. This model usually contains terrain elevation data (such as digital elevation model DEM) and surface feature data (such as buildings, vegetation, etc.). The accuracy and detail of the model directly affect the accuracy of the screening.

[0207] The preset detection requirements usually include the following aspects:

[0208] 1. Line of sight visibility: The deployment point needs to have good line of sight to the target area.

[0209] 2. Signal coverage: Consider the propagation characteristics of electromagnetic waves to ensure that the detection signal can effectively cover the target area.

[0210] 3. Terrain suitability: Deployment points need to be located in locations with suitable terrain, avoiding steep or unstable areas.

[0211] 4. Accessibility: Consider the accessibility of infrastructure such as roads.

[0212] 5. Environmental factors: such as avoiding sources of electromagnetic interference and considering climatic conditions.

[0213] To assess these requirements, a series of spatial analyses are required:

[0214] 1. Line of sight analysis: Use line of sight analysis algorithms (such as ray tracing) to evaluate the visible range of each potential deployment point. This needs to take into account the occlusion effects of terrain and buildings.

[0215] 2. Signal coverage analysis: Use electromagnetic wave propagation models (such as the Longley-Rice model or the COST-231 model) to simulate the propagation of signals in three-dimensional space. This requires taking into account factors such as terrain, building reflection and diffraction.

[0216] 3. Terrain analysis: Calculate terrain parameters such as slope and aspect to identify flat areas suitable for equipment installation.

[0217] 4. Accessibility analysis: Use network analysis algorithms to evaluate the distance and ease of access to the nearest road or infrastructure for each potential deployment point.

[0218] 5. Analysis of environmental factors: Combined with other geographic data (such as land use maps, meteorological data, etc.) to assess environmental suitability.

[0219] These analyses can be implemented through raster operations or vector analysis. For example, the pre-selected deployment area can be converted into a high-resolution raster, and then the above analysis is performed on each raster cell, and finally the raster cells that meet all conditions are reconverted into vector polygons. In actual operation, weights and thresholds may need to be set to balance different requirements. For example, multi-criteria decision analysis (MCDA) methods such as weighted linear combination (WLC) or analytic hierarchy process (AHP) can be used to comprehensively evaluate the suitability of each location. The screening process can be expressed as a series of Boolean operations or fuzzy logic operations:

[0220] Target deployment area = pre-selected deployment area ∩ (visible area ∩ signal coverage area ∩ terrain suitable area ∩ access convenient area ∩ environmental suitable area)

[0221] Among them, "∩" represents the spatial intersection operation.

[0222] The above process can obtain a strictly screened target deployment area, which not only meets the geographical location requirements, but also ensures that the equipment deployed here can effectively perform the detection task. The advantage of this method is that it comprehensively considers multiple influencing factors, which can greatly improve the success rate and effect of subsequent deployment. In addition, the amount of calculation in this step may be large, especially for large or complex areas. In order to improve efficiency, you can consider using parallel computing technologies such as GPU acceleration or distributed computing. At the same time, you can also adopt a multi-resolution analysis strategy, first use low-resolution data for coarse screening, and then use high-resolution data for fine analysis in key areas.

[0223] In one implementation, maximizing the coverage area of ​​the deployment points is taken as the optimization goal, and adjusting all initial deployment points to the optimal deployment points based on the distance constraint and using the optimization algorithm includes the following steps:

[0224] Construct an objective function with the optimization goal of maximizing the coverage area of ​​the deployment points;

[0225] Constructing a first initial population of the optimization algorithm based on all initial deployment points;

[0226] Based on the distance constraint condition and taking the approximation of the objective function as the optimization direction, the optimization algorithm is used to iteratively update the first initial population to obtain the first optimal population under the distance constraint condition;

[0227] If the first optimal population satisfies the objective function, the optimal deployment point is determined according to the first optimal population.

[0228] In this embodiment, if the first optimal population cannot satisfy the objective function, the distance constraint condition is adjusted so that the straight-line distance difference between any initial deployment point and the other two initial deployment points is less than a preset difference threshold;

[0229] Using the first optimal population as the second initial population of the optimization algorithm;

[0230] Based on the adjusted distance constraint condition and taking the approximation objective function as the optimization direction, the second initial population is iteratively updated by using the optimization algorithm to obtain the second optimal population under the adjusted distance constraint condition;

[0231] The optimal deployment point is determined according to the second optimal population.

[0232] In this embodiment, constructing an objective function with maximizing the coverage area of ​​the deployment points as the optimization goal can transform the problem into a form that can be solved by mathematical methods. The design of the objective function directly affects the effect and efficiency of the optimization. By adjusting the form and parameters of the objective function, it can be flexibly adapted to different actual needs and constraints. Then, the first initial population of the optimization algorithm is constructed based on all the initial deployment points. The quality and diversity of the initial population are directly related to whether the optimization algorithm can find the global optimal solution. In this embodiment, each individual represents a set of possible deployment point locations.

[0233] A common method for constructing the initial population is to perform random perturbations based on the initial deployment points. The specific steps are as follows:

[0234] Represent each initial deployment point as a vector (x,y), where x and y are geographic coordinates.

[0235] For each initial deployment point, generate multiple variants:

[0236] (x',y')=(x+δx,y+δy)

[0237] where δx and δy are small perturbations randomly generated from some range, for example sampled from a normal distribution N(0,σ^2).

[0238] Make sure the new point you generate is still within the target deployment area. If not, you can regenerate it or project it to the nearest feasible location.

[0239] The above process is repeated until a sufficient number of individuals are generated. A typical population size may be between 50 and 200, depending on the complexity of the problem and the computing resources.

[0240] Another approach is to use a heuristic algorithm to generate the initial population. For example, you can use the k-means clustering algorithm to generate evenly distributed points in the target deployment area, and then perform random perturbations around these points. This method ensures that the initial population has a good spatial distribution. In order to increase the diversity of the population, you can combine multiple generation methods. For example, 70% of the individuals are generated using random perturbations, 20% are generated using clustering methods, and the remaining 10% are completely randomly generated. This can achieve a balance between local search and global exploration.

[0241] Next, based on the distance constraint and with the approximation of the objective function as the optimization direction, the optimization algorithm is used to iteratively update the first initial population to obtain the first optimal population under the distance constraint. This process involves multiple iterations, each of which includes operations such as selection, crossover, mutation, and evaluation. The choice of optimization algorithm has a significant impact on the results. Commonly used algorithms include genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE). Taking the genetic algorithm as an example, a typical iteration process is as follows:

[0242] Selection: Select excellent individuals based on fitness (objective function value). A tournament selection method can be used.

[0243] Crossover: Select two parent individuals and generate new offspring. For example, for two parent deployment points (x1, y1) and (x2, y2), offspring can be generated:

[0244] (x',y')=(αx1+(1-α)x2,αy1+(1-α)y2)

[0245] Where α is a random number between [0,1].

[0246] Mutation: Randomly mutate individuals with a small probability to increase population diversity. For example, randomly move selected points by a small amount.

[0247] Evaluation: Calculate the fitness value of the newly generated individuals.

[0248] Replacement: Replace the old population with a new generation of population, retaining elite individuals.

[0249] In this process, distance constraints need special treatment. A penalty function method can be used: if an individual violates the distance constraint, its fitness value is reduced. During the iteration process, the algorithm parameters need to be adjusted dynamically. For example, the mutation rate can be gradually reduced as the number of iterations increases to achieve the transition from global exploration to local refinement. The stopping condition can be set to reach the maximum number of iterations, the fitness value is no longer significantly improved, or the preset target value is reached. Through multiple iterations, the individuals in the population will gradually converge to the optimal solution. The optimization process not only improves the coverage effect, but also ensures that the distance between deployment points meets the requirements, providing a feasible solution for actual deployment.

[0250] Calculate the objective function value of the first optimal population. If the objective function value satisfies the objective function, determine the optimal deployment point based on the first optimal population. In other words, directly select the best individual in the first optimal population as the final solution, thereby obtaining a set of optimal deployment points that meet all requirements. These points not only maximize the coverage area in theory, but also meet various constraints of actual deployment.

[0251] If the objective function value calculated by the first optimal population cannot satisfy the objective function, the distance constraint is adjusted to the point where the difference in straight-line distance between any initial deployment point and the other two initial deployment points is less than the preset difference threshold. This process is intended to relax the constraints and provide a larger search space for the optimization algorithm. The specific implementation method for adjusting the distance constraint is as follows:

[0252] Define the difference threshold ε. This value should be set according to the specific situation of the problem, for example, it can be set to 10% of the original distance requirement.

[0253] The new distance constraint can be expressed as:

[0254] |d_ij-d_ik|<ε

[0255] Where d_ij and d_ik are the distances from point i to point j and point k respectively.

[0256] Updated the way constraints are checked in optimization algorithms.

[0257] The effect of implementing the above steps is to create a larger search space for the optimization algorithm, increasing the probability of finding a satisfactory solution. By allowing small variations in the distance between deployment points, it is possible to discover excellent layouts that would have been overlooked under strict isometric conditions.

[0258] Next, the first optimal population is used as the second initial population of the optimization algorithm, which can achieve the convergence speed of the secondary optimization algorithm process and improve the possibility of finding a better solution. The specific implementation is as follows:

[0259] Evaluate the first optimal population: First, the fitness value of each individual in the first optimal population needs to be recalculated. This is because changes in constraints may cause the previous fitness evaluation to be no longer accurate.

[0260] Sorting and screening: Sort the individuals in the first optimal population according to the new fitness value. You can choose to retain a certain proportion of the top ranked individuals (such as the top 50%) as the basis of the second initial population.

[0261] Population expansion: In order to maintain the diversity of the population, it is necessary to expand the population based on the retained excellent individuals. The following methods can be used:

[0262] a) Perform small random mutations on the retained individuals to generate new individuals.

[0263] b) Use the crossover operation to pair the retained individuals together to generate new offspring.

[0264] c) Introduce a certain proportion (e.g. 10-20%) of completely randomly generated new individuals to increase population diversity.

[0265] Population balance: Ensure that the size of the expanded population is the same as the first initial population to maintain the consistency of the algorithm.

[0266] Initialization parameters: Depending on the new constraints and optimization goals, you may need to adjust some algorithm parameters, such as mutation rate, crossover rate, etc. Usually, these parameters can be slightly increased to increase the exploration ability of the population.

[0267] Next, based on the adjusted distance constraint and taking the approximation objective function as the optimization direction, the optimization algorithm is used to iteratively update the second initial population to obtain the second optimal population under the adjusted distance constraint. The specific implementation process is as follows:

[0268] Initialization: Use the second initial population generated in the previous step as the starting point. This population already contains some high-quality individuals, which helps to quickly find a good solution.

[0269] Fitness evaluation: Calculate the fitness value for each individual in the population. The fitness function needs to consider the new distance constraint and can be expressed as:

[0270] F=O(x)-λ*P(x)

[0271] Where O(x) is the original objective function, P(x) is the penalty function based on the new distance constraint, and λ is the weight coefficient.

[0272] Selection operation: Use appropriate selection methods (such as tournament selection) to select excellent individuals for reproduction. You can consider using an elite retention strategy to ensure that the best individuals are not lost during the evolution process.

[0273] Crossover operation: Select parent individuals for crossover to generate new offspring. There are many crossover methods that can be used, such as one-point crossover, two-point crossover, or uniform crossover.

[0274] Mutation operation: Randomly mutate some individuals to maintain population diversity. Mutation can be a small random adjustment of the deployment point coordinates. The mutation rate can be reduced as the number of iterations increases to achieve a transition from global exploration to local refinement.

[0275] Constraint handling: Ensure that individuals after crossover and mutation still meet the adjusted distance constraints. Individuals that do not meet the constraints can be handled using a repair strategy or a regeneration strategy.

[0276] Iterative update: Repeat steps 2-6 until the termination condition is met. The termination condition can be reaching the maximum number of iterations, the fitness value no longer significantly improves, or reaching the preset target value.

[0277] Parameter Adaptation: Consider using adaptive parameter adjustment strategies, such as dynamically adjusting the crossover rate and mutation rate based on population diversity to balance exploration and exploitation.

[0278] Local search: After a certain number of generations, perform local search on the current optimal individual, such as using simulated annealing or hill climbing algorithms, to further improve the quality of the solution.

[0279] Diversity maintenance: Monitor population diversity, and if the diversity drops below a certain threshold, introduce some randomly generated new individuals or increase the mutation rate.

[0280] The effect of implementing the above steps is to obtain a high-quality solution set under the new constraints. Due to the better starting point (the second initial population), the optimization process may converge faster than the first optimization. At the same time, due to the relaxation of the constraints, better solutions that could not be achieved under the original constraints may be found.

[0281] In one embodiment, using the regional three-dimensional model to screen out all areas in the pre-selected deployment area that do not meet the preset detection requirements, and using all the retained pre-selected deployment areas as target deployment areas includes the following steps:

[0282] Obtaining the model heights of all boundary models located at the boundary of the target area in the regional three-dimensional model;

[0283] Mark the boundary model whose model height exceeds the obstacle height threshold in the preset detection requirement as an ultra-high boundary model, and obtain the model longitude and latitude coordinates of both ends of the ultra-high boundary model in the regional three-dimensional model;

[0284] Generate a first prohibited deployment area that does not meet preset detection requirements in the pre-selected deployment area of ​​the regional map information based on the model latitude and longitude coordinates;

[0285] Screening out all first prohibited deployment areas in the pre-selected deployment areas, and taking all retained pre-selected deployment areas as preferred deployment areas;

[0286] Based on the geological requirements for device deployment in the preset detection requirements and using the GIS system, a regional screening step is performed on the preferred deployment areas, and all retained preferred deployment areas are used as target deployment areas.

[0287] In this embodiment, based on the device deployment geological requirements in the preset detection requirements and using the GIS system to perform a regional screening step on the preferred deployment area, and taking all the retained preferred deployment areas as the target deployment areas includes the following steps:

[0288] Obtain regional geological information of the preferred deployment area through the GIS system;

[0289] Combining regional geological information and device deployment geological requirements in preset detection requirements, generating a second prohibited deployment area that does not meet the preset detection requirements in the preferred deployment area;

[0290] All second prohibited deployment areas in the preferred deployment area are screened out, and all retained preferred deployment areas are used as target deployment areas.

[0291] In this embodiment, refer to Figure 5 , obtaining the model height of all boundary models located at the boundary of the target area in the regional three-dimensional model involves the processing and analysis of three-dimensional spatial data. Specifically, it is necessary to accurately locate the boundary of the target area in the three-dimensional model first. In practice, this may involve sampling of digital elevation model (DEM) or triangulated irregular network (TIN) data. For example, a height value can be extracted at a fixed distance (such as 1 meter or 5 meters, depending on the required accuracy) along the boundary line. For complex terrain or buildings, more advanced interpolation techniques such as bilinear interpolation or kriging may be required to obtain more accurate height estimates. When processing large-scale data, spatial indexes (such as R-trees or quadtrees) may be required to optimize query efficiency. In addition, the consistency of coordinate systems between different data sources needs to be considered, and coordinate transformation may be required. Ultimately, this step will generate a dataset containing boundary locations and corresponding heights, providing a basis for subsequent analysis. The effect of this step is to obtain an accurate boundary height model that reflects the height changes of terrain and buildings around the target area, providing key information for evaluating potential line of sight obstruction and signal propagation.

[0292] Next, the boundary model whose model height exceeds the obstacle height threshold in the preset detection requirements is marked as an ultra-high boundary model, and the model latitude and longitude coordinates of the two ends of the ultra-high boundary model in the regional three-dimensional model are obtained. This step first requires setting an appropriate height threshold, which is usually determined based on the performance characteristics of the detection equipment and the expected detection effect. For example, if the effective detection height of the detection equipment is 50 meters, the height threshold may be set to 45 meters, leaving a certain safety margin. Next, it is necessary to traverse all the boundary height data obtained in the previous step and compare the height of each point with the threshold. Points that exceed the threshold need to be marked. This can be achieved by adding a Boolean flag bit to the data structure.

[0293] It is important to pay attention to continuous superelevation points, as they may represent a complete obstacle (such as a tall building or a mountain). In order to obtain the coordinates of both ends of the superelevation boundary model, it is necessary to detect the start and end points in the marker sequence (such as Figure 5Point C and point D). This can be done by checking the marking status of adjacent points. Once the scope of the superelevation boundary model is determined, the precise latitude and longitude coordinates of its two end points need to be extracted. This usually involves the conversion of the coordinate system, because the 3D model may use a local coordinate system, which needs to be converted to a standard geographic coordinate system (such as WGS84). The effect of this step is to obtain the precise location and scope of a series of potential line-of-sight obstructions or signal propagation obstacles, which provides important constraints for subsequent deployment area planning.

[0294] Next, the first prohibited deployment area that does not meet the preset detection requirements is generated in the pre-selected deployment area of ​​the regional map information based on the model latitude and longitude coordinates. The purpose of this step is to identify those areas that are not suitable for deploying detection equipment due to tall obstacles. First, the latitude and longitude coordinates of the super-elevation boundary model obtained in the previous step need to be imported into the GIS system. These coordinate points represent potential line-of-sight obstructions or signal propagation obstacles. Next, an appropriate buffer distance needs to be determined based on the performance characteristics of the detection equipment and the height of the obstacle. This distance can be estimated by a simple geometric model, for example: buffer distance = (obstacle height-detection equipment height) / tan(minimum elevation angle). Among them, the minimum elevation angle is the minimum vertical angle at which the detection equipment can work effectively. Using this formula, a specific buffer distance can be calculated for each super-elevation boundary model. Then, with each super-elevation boundary model as the center, a corresponding buffer zone is created. These buffer zones constitute the first prohibited deployment area. When creating buffer zones, it is necessary to pay attention to handling overlapping situations. The spatial merge (Dissolve) operation can be used to merge intersecting buffers to obtain a continuous prohibited area. In addition, these prohibited areas need to be spatially intersected with the pre-selected deployment areas to ensure that only the prohibited deployment areas within the pre-selected area are retained.

[0295] Next, all the first prohibited deployment areas in the pre-selected deployment area are screened out, and all the retained pre-selected deployment areas are used as preferred deployment areas. This step actually performs a spatial difference operation, the purpose of which is to remove those areas determined to be unsuitable for deployment from the original pre-selected deployment area, so as to obtain a more optimized deployment area. First, both the pre-selected deployment area and the first prohibited deployment area need to be represented as polygonal geometric objects. This usually involves the processing of vector data, which can be done using geospatial data processing libraries such as GDAL / OGR. Next, a spatial difference operation is performed, which can be expressed as: preferred deployment area = pre-selected deployment area - first prohibited deployment area.

[0296] In practice, this process may produce complex geometries, including possible islands (small patches of allowed area completely surrounded by prohibited areas) or elongated connected areas. Therefore, post-processing may be required, such as removing areas with too small an area or simplifying complex boundaries. This can be achieved by setting a minimum area threshold and using the Douglas-Peucker algorithm. In addition, the connectivity of the preferred deployment area needs to be checked to ensure that it is not split into multiple discontinuous parts. If this happens, subsequent analysis may need to be performed separately for each part.

[0297] Next, the regional geological information of the preferred deployment area is obtained through the GIS system. The purpose of this step is to provide more detailed geological background information for deployment decisions, ensuring that the selected deployment location not only meets the requirements of line of sight and signal propagation, but also meets the geological conditions for equipment installation and long-term stable operation. First, it is necessary to determine the type of geological information required, which may include rock and soil types, stratigraphic structure, groundwater level, geological disaster risks (such as landslides, subsidence), etc. Then, it is necessary to obtain this data from relevant geological databases or geological survey institutions. These data may exist in different formats, such as vector layers, raster images, or attribute tables. After importing these data into the GIS system, the coordinate system needs to be unified and the data format converted to ensure that they can be spatially overlaid with the data of the preferred deployment area. Using the spatial query and attribute extraction functions of GIS, the geological data is spatially intersected with the preferred deployment area. This process may involve resampling of raster data or clipping of vector data. For continuous geological features (such as terrain slope), interpolation operations may be required to obtain higher resolution data. For discrete geological features (such as rock and soil types), spatial join operations are required to associate attribute information to different parts of the preferred deployment area. In addition, some statistical analysis may be required, such as calculating the area proportion of each geological type and identifying areas with high incidence of geological risks.

[0298] Next, the second prohibited deployment area that does not meet the preset detection requirements is generated in the preferred deployment area in combination with the regional geological information and the device deployment geological requirements in the preset detection requirements. The purpose of this step is to further refine the areas available for deployment and exclude those areas that meet the line of sight and signal propagation requirements but are not suitable due to geological conditions. First, the geological requirements for device deployment need to be clearly defined, which may include multiple indicators such as ground bearing capacity, terrain slope, groundwater level depth, geological stability, etc. For each indicator, a threshold or acceptable range needs to be set. For example, it may be required that the ground slope does not exceed 15 degrees and the groundwater level is at least 5 meters deep. Then, these requirements need to be converted into actionable spatial query conditions in GIS. For continuous variables (such as slope), raster calculations or surface analysis tools can be used to directly generate areas that do not meet the conditions. For categorical variables (such as geotechnical types), it is necessary to first determine which categories do not meet the requirements and then extract the areas covered by these categories. For indicators with different importance, a weight system can be introduced to use weighted overlay analysis to comprehensively evaluate the suitability of each location. The generated area that does not meet the conditions is the second prohibited deployment area, which comprehensively considers the impact of geological conditions on equipment installation and operation. This can not only improve the success rate of deployment and the long-term stability of the equipment, but also reduce potential geological risks and provide a more reliable spatial range for the subsequent selection of specific deployment locations. Then all second prohibited deployment areas in the preferred deployment area are screened out, and all retained preferred deployment areas are used as target deployment areas.

[0299] In one embodiment, in combination with the device deployment point and the area range of the target area in the area map information, configuring the drone detection task of the electromagnetic detection drone and the device detection task of the electromagnetic signal acquisition and analysis device includes the following steps:

[0300] Based on the device deployment point and according to the device preset parameters of the electromagnetic signal acquisition and analysis device, a device sensing range of the electromagnetic signal acquisition and analysis device is generated in the regional map information;

[0301] Identify the area scope of the target area according to the area boundary of the target area in the area map information;

[0302] Eliminate the overlapping part of the area range with the device sensing range, and use the remaining area range as the detection range of the electromagnetic detection drone;

[0303] The drone detection mission of configuring electromagnetic detection drones in each mobile drone base station based on the detection range and device deployment point;

[0304] The device detection task of the electromagnetic signal acquisition and analysis device is configured in combination with the device perception range and the drone detection time in the drone detection task.

[0305] In this embodiment, refer to Figure 6 First, we need to determine the precise geographic coordinates of each device deployment point, and then calculate the theoretical maximum sensing distance of the device based on the preset parameters of the electromagnetic signal acquisition and analysis equipment, such as signal receiving sensitivity, antenna gain, operating frequency, etc. For example, the maximum sensing distance = , where Pt is the transmit power, Gt and Gr are the transmit and receive antenna gains, λ is the signal wavelength, and Pr_min is the receive sensitivity. Next, the buffer analysis tool of the GIS software is used to create a circular buffer with each device deployment point as the center and the calculated maximum perception distance as the radius, representing the overall perception range of the electromagnetic signal acquisition and analysis equipment.

[0306] The regional scope of the target area is identified according to the regional boundary of the target area in the regional map information. Specifically, the regional map information needs to be loaded and parsed first, which is usually stored in a vector data format (such as shapefile or GeoJSON). The boundary of the target area may be a complex polygon, and may even contain multiple discontinuous parts or internal holes. Using the geometry processing tools of the GIS software, the vertex coordinate sequence of this boundary polygon can be extracted. If the target area contains multiple discontinuous parts, it may be necessary to perform these calculations separately for each part and mark their relative positional relationships. Finally, a regional scope object or data structure containing all this information is generated, which includes not only boundary coordinates, but also derived information such as area, perimeter, and bounding box. The effect of this step is to obtain a clearly defined and quantitatively described target area scope, which provides a basic reference framework for subsequent spatial analysis and mission planning.

[0307] The overlapping part of the area range with the device perception range is removed, and the remaining area range is used as the detection range of the electromagnetic detection drone. The purpose of this step is to determine the area that the electromagnetic detection drone needs to focus on to avoid duplication with the perception range of ground equipment. First, the target area range and the device perception range need to be converted into polygon objects under the same coordinate system. Then, the spatial analysis tool of the GIS software is used to perform polygon difference operations, which can be expressed as: detection range = target area range - device perception range. The detection range provides a clear spatial boundary for drone mission planning, which helps to develop a more accurate and efficient flight path. Then, based on the detection range and device deployment point, the drone detection mission of the electromagnetic detection drone is configured in each mobile drone base station.

[0308] Next, the timing of the UAV detection mission needs to be analyzed, including the expected detection start and end time for each area. This information can be calculated from the flight path and speed of the UAV. Then, this time information is combined with the perception range of the electromagnetic signal acquisition and analysis equipment to create a schedule for each ground device. This schedule should include: 1) the detection start time, which is usually set slightly earlier than the start time of the UAV detection in the corresponding area; 2) the detection end time, which is usually set slightly later than the end time of the UAV detection; 3) the detection frequency and duration, which may need to be dynamically adjusted according to the location of the UAV. For example, when the UAV is close to the edge of the device's perception range, the detection frequency may need to be increased to improve the accuracy of the data. In addition, the duty cycle and energy consumption of the equipment need to be considered. Optimization algorithms (such as genetic algorithms or simulated annealing) can be used to balance detection effect and energy efficiency. For each time period, specific detection parameters need to be configured, such as frequency range, signal strength threshold, sampling rate, etc. These parameters may need to be dynamically adjusted according to environmental conditions and detection targets. Data storage and transmission strategies also need to be set, including local storage capacity, data compression method, transmission frequency, etc. The implementation effect of this step is to obtain an accurate, dynamic and coordinated ground equipment detection mission configuration. This configuration not only ensures the time synchronization between ground equipment and drone detection activities, but also optimizes resource utilization and improves the efficiency and reliability of the overall detection system. Through this coordinated configuration, seamless cooperation between ground equipment and drone systems can be achieved to maximize detection coverage and data quality.

[0309] In one embodiment, the drone detection task of configuring an electromagnetic detection drone in each mobile drone base station based on the detection range and the device deployment point includes the following steps:

[0310] Determine an initial range segmentation point in the detection range based on the device deployment point, wherein the straight-line distances between the initial range segmentation point and all device deployment points are equal;

[0311] Generate a segmentation boundary in the detection range using the shortest straight line between the initial range segmentation point and the device deployment point;

[0312] Combining the segmentation boundary and the range boundary of the detection range, a base station detection range is allocated to each mobile UAV base station;

[0313] For each mobile drone base station, based on the base station detection range and with another mobile drone base station as the path endpoint, the optimal path algorithm is used to generate the optimal path of the electromagnetic detection drone. All mobile drone base stations can only serve as the path endpoint of one drone optimal path in the same drone detection mission.

[0314] The flight time of the electromagnetic detection drone in the optimal drone path is calculated based on the drone flight parameters preset by the mobile drone base station;

[0315] If the flight time is the maximum flight time among the flight times calculated by all mobile drone base stations, the drone detection mission of the electromagnetic detection drone is configured in combination with the drone optimal path and the drone flight parameters;

[0316] If the flight time is not the maximum flight time among the flight times calculated by all mobile UAV base stations, the UAV flight parameters are adjusted until the flight time is the same as the maximum flight time, and the UAV detection mission of the electromagnetic detection UAV is configured in combination with the optimal UAV path and the adjusted UAV flight parameters.

[0317] In this embodiment, if Figure 6 As shown, the initial range segmentation point is determined in the detection range based on the device deployment point, and the straight-line distance between the initial range segmentation point and all device deployment points is equal. The purpose of this step is to find a balance point (such as Figure 6 The detection range is evenly distributed to each mobile drone base station. One way to achieve this step is to use a weighted centroid algorithm. First, each device deployment point is regarded as a mass point, and its mass can be assigned according to the importance or expected workload of the point. Then, the coordinates of the initial range segmentation point are calculated using the following formula:

[0318] x=Σ(wi*xi) / Σwi

[0319] y=Σ(wi*yi) / Σwi

[0320] Among them, (xi,yi) is the coordinate of the i-th device deployment point, and wi is its weight.

[0321] However, this initial point may not completely meet the requirement of being equidistant from all deployment points. Therefore, iterative optimization is required. The gradient descent method can be used to define the objective function as the variance of the distance from the segmentation point to all deployment points, and then minimize this variance through multiple iterations. With each iteration, the position of the segmentation point is slightly adjusted until the preset accuracy requirement or the upper limit of the number of iterations is met. In practical applications, the shape and terrain characteristics of the detection range also need to be considered. If the detection range is non-convex or contains inaccessible areas, more complex algorithms may need to be used, such as methods based on Voronoi diagrams, to determine the segmentation points.

[0322] The segmentation boundary is generated by the shortest straight line between the initial range segmentation point and the device deployment point in the detection range. This process is actually constructing a Voronoi diagram, in which each device deployment point is the center of a Voronoi unit. The generation of the segmentation boundary involves the following steps:

[0323] 1. For each pair of adjacent device deployment points, calculate their perpendicular bisector. This bisector can be determined by the following method:

[0324] a) Calculate the midpoint of two points (xm,ym) = ((x1+x2) / 2, (y1+y2) / 2)

[0325] b) Calculate the direction vector (-Δy, Δx) perpendicular to the connecting line, where Δx=x2-x1, Δy=y2-y1

[0326] 2. Extend these bisectors until they intersect the boundaries of the detection range.

[0327] 3. At each intersection, it is necessary to determine whether the point actually belongs to the final Voronoi boundary. This can be determined by checking the distance from the point to all device deployment points. Only when the distance from the point to the two nearest device deployment points is equal and less than the distance to all other points, it is a valid boundary point.

[0328] Next, the segmentation boundary and the range boundary of the detection range are combined to assign a base station detection range to each mobile drone base station. The purpose of this step is to determine the specific area that each mobile drone base station is responsible for, ensuring that the entire detection range is fully and efficiently covered. Implementing this step involves the following key operations:

[0329] 1. Boundary intersection calculation: Perform spatial intersection calculation on the Voronoi segmentation boundary generated in the previous step and the outer boundary of the detection range. This can be achieved using the geometric operation function of GIS software, such as the "Intersection" tool of QGIS or the "Intersect" tool of ArcGIS.

[0330] 2. Sub-area generation: The result of the intersection operation is a series of polygons, each of which represents the preliminary detection range of a base station.

[0331] 3. Attribute assignment: Assign a unique identifier and related attributes to each sub-area, such as the responsible base station ID, area, perimeter, etc.

[0332] 4. Connectivity check: Make sure the detection range of each base station is continuous. If there is discontinuity, the boundaries need to be reallocated or adjusted.

[0333] 5. Load balancing: Calculate the area allocated to each base station. If the difference is too large, you may need to adjust the segmentation boundaries to achieve a more balanced distribution. This can be achieved by moving the generation point of the Voronoi diagram or adjusting the weights.

[0334] 6. Obstacle handling: Consider obstacles within the detection range (such as buildings, water bodies, etc.). It may be necessary to exclude these areas from the detection range or assign special detection strategies to them.

[0335] For each mobile drone base station, based on the base station detection range and with another mobile drone base station as the path end point, the optimal path algorithm is used to generate the optimal path for the electromagnetic detection drone. This step involves multiple optimization considerations, including path length, coverage efficiency, energy consumption, etc. The following methods can be used to implement this step:

[0336] 1. Path planning algorithm selection: You can use A* algorithm, rapid random exploration tree (RRT*) or ant colony algorithm (ACO), etc. Take A* algorithm as an example, it combines the heuristic of best first search and the completeness of Dijkstra algorithm.

[0337] 2. Map discretization: Convert the base station detection range into a grid map, where each grid represents a possible drone location. The selection of grid size needs to balance computational complexity and path accuracy.

[0338] 3. Cost function definition: Design a cost function that takes multiple factors into consideration, such as:

[0339] f(n)=g(n)+h(n)+c(n)

[0340] Among them, g(n) is the actual cost from the starting point to the current node, h(n) is the estimated cost from the current node to the target, and c(n) is an additional constraint (such as obstacle avoidance, coverage requirements, etc.).

[0341] 4. Destination selection: Select a suitable destination base station for each base station. This can be based on distance, direction, or a predetermined rotation strategy. It is necessary to ensure that each base station is the destination of at most one path.

[0342] 5. Path generation: For each base station, use the selected algorithm to generate a path to its destination base station.

[0343] 6. Path optimization: Optimize the initially generated path, such as smoothing, deleting redundant points, etc., to improve flight efficiency.

[0344] 8. Task allocation verification: Ensure that all base stations are assigned tasks and that each base station is at most the end point of one path.

[0345] The above method not only ensures the integrity and efficiency of the detection mission, but also achieves balanced use of UAV resources by reasonably allocating the terminal base stations. The generated path provides detailed guidance for subsequent task execution and resource scheduling, which helps to improve the reliability and flexibility of the entire detection system.

[0346] Next, the flight time of the electromagnetic detection drone in the optimal path of the drone is calculated based on the drone flight parameters preset by the mobile drone base station. This calculation process needs to take into account multiple factors to ensure accurate estimation of the flight time and provide a basis for subsequent task coordination and resource allocation. Specifically, it is first necessary to understand and parse the preset drone flight parameters, including maximum flight speed (Vmax), cruising speed (Vcruise), acceleration (a) and deceleration (d), turning radius (R) or maximum turning angular velocity (ω), climb / descent rate. The optimal path is then decomposed into multiple straight segments and turning segments. For each path segment, the change in speed over time is calculated. For the turning segment, the time is calculated using the angular velocity and angle change. If the path contains significant altitude changes, the climb or descent time needs to be considered, and finally the time of all path segments is accumulated to obtain the flight time of the electromagnetic detection drone in the optimal path of the drone.

[0347] If the flight time calculated in a certain mobile drone base station is the maximum flight time among the flight times calculated by all mobile drone base stations, then for this mobile drone base station, the drone detection mission of the electromagnetic detection drone can be directly configured in combination with the drone optimal path and drone flight parameters. The drone detection mission not only includes accurate flight paths and time arrangements, but also covers detection parameters, energy management, communication strategies and other aspects, ensuring the efficiency and reliability of mission execution.

[0348] If the flight time calculated in a certain mobile drone base station is not the maximum flight time among the flight times calculated by all mobile drone base stations, then for this mobile drone base station, it is necessary to adjust the drone flight parameters until the flight time is the same as the maximum flight time, and configure the drone detection task of the electromagnetic detection drone in combination with the optimal drone path and the adjusted drone flight parameters. The purpose of this step is to ensure that all drones can complete the task synchronously. The specific implementation process of adjusting the drone flight parameters is as follows: First, determine the parameters that need to be adjusted, usually including cruising speed, hovering time, sampling frequency, etc.; use optimization algorithms (such as gradient descent or genetic algorithms) to gradually adjust these parameters; after each adjustment, recalculate the flight time until it matches the maximum flight time. If the speed adjustment is not enough to reach the target time, consider fine-tuning the flight path. You can add circling or zigzag flight segments to extend the flight time.

[0349] After the parameters are adjusted, all adjusted parameters and plans are integrated to generate a detailed mission execution script, including specific instructions for each waypoint, to ensure that the mission configurations of all drones are synchronized in time. The final mission configuration is securely transmitted to each drone. The above steps achieve the time synchronization of all drone missions. By fine-tuning the flight parameters and mission details, it not only improves the coordination and efficiency of the entire detection system, but also enhances the flexibility and adaptability of the system.

[0350] In one embodiment, analyzing the first area detection result and the second area detection result to obtain the radiation source positions and radiation source types of all radiation sources in the target area includes the following steps:

[0351] Combine the detection results of the first area and the detection results of the second area to analyze and obtain the electromagnetic environment situation awareness results in the target area;

[0352] Identify the radiation source latitude and longitude data of all radiation sources in the target area based on the electromagnetic environment situation awareness results;

[0353] Based on the longitude and latitude data of the radiation sources and using the clustering classification algorithm, all the radiation sources are clustered and analyzed to obtain the radiation source types of all the radiation sources.

[0354] In this embodiment, the electromagnetic environment situation awareness results in the target area are obtained by combining the detection results of the first area and the detection results of the second area. Specifically, the detection results of the two areas need to be preprocessed first, including noise removal, outlier detection and data standardization. This can be achieved using techniques such as wavelet denoising and median filtering. Then, data alignment and fusion are performed to ensure that data from different sources are consistent in time and space. This may involve timestamp correction and spatial interpolation techniques. Advanced signal processing techniques are used to analyze the fused data. Including spectrum analysis (such as fast Fourier transform FFT), time-frequency analysis (such as short-time Fourier transform STFT or wavelet transform), and modulation identification (such as cyclic spectrum analysis). These analyses help identify the characteristics of the signal, such as center frequency, bandwidth, signal strength and modulation type. For the analysis of spatial distribution, geostatistical methods such as kriging or inverse distance weighted (IDW) can be used to generate a spatial distribution map of electromagnetic field intensity.

[0355] In order to identify potential radiation sources and interference, signal source location algorithms such as time difference of arrival (TDOA) or angle of arrival (AOA) methods can be applied. In addition, time series analysis is required to identify the time-varying characteristics and periodic patterns of the signal. This can be achieved through methods such as the autoregressive integrated moving average (ARIMA) model or the long short-term memory network (LSTM). Finally, all these analysis results are combined to form the electromagnetic environment situational awareness results. Specifically, it includes: an overview of the use of the electromagnetic spectrum, the location and characteristics of the main radiation sources, the identification of potential interference sources, the detection of abnormal signals, and the temporal and spatial variation trends of the electromagnetic environment.

[0356] Based on the electromagnetic environment situational awareness results, the radiation source latitude and longitude data of all radiation sources in the target area are identified. Specifically, the situational awareness results need to be analyzed first to extract possible radiation source characteristics. This includes the identification of signal strength peaks, analysis of spectrum characteristics, and consideration of time correlation. For each potential radiation source, a variety of positioning techniques are used to determine its geographical location. Common methods include: triangulation, angle of arrival (AOA), time difference (TDOA), frequency difference (FDOA), etc. These methods can be used in combination to improve positioning accuracy. For example, weighted least squares can be used to integrate multiple measurement results. In practical applications, it is also necessary to consider the impact of factors such as multipath effects and atmospheric refraction on positioning accuracy. Ray tracing or statistical models can be used to compensate for these effects. For multiple radiation sources in dense areas, high-resolution algorithms such as the MUSIC (Multiple Signal Classification) algorithm may be needed to separate and locate overlapping signal sources. After obtaining preliminary positioning results, error analysis and accuracy evaluation are required. This can be achieved through Monte Carlo simulation or covariance matrix analysis. For results that do not meet the accuracy requirements, it may be necessary to add measurement points or adjust the measurement strategy. Finally, the positioning results are converted into a standard geographic coordinate system (such as WGS84) to obtain the longitude and latitude data of each radiation source. This conversion process needs to consider the conversion parameters between different coordinate systems and the earth ellipsoid model.

[0357] Based on the longitude and latitude data of the radiation sources, all radiation sources are clustered and analyzed using a clustering classification algorithm to obtain the radiation source types of all radiation sources. The purpose of this step is to group radiation sources with similar characteristics and infer the types of radiation sources based on the characteristics of these groups. Implementing this step involves the following stages:

[0358] 1. In addition to longitude and latitude data, other features that may affect classification need to be extracted, such as signal strength, frequency range, modulation type, time pattern, etc. These features can form a multidimensional vector to represent each radiation source.

[0359] 2. Standardize or normalize the extracted features to ensure that features of different scales can fairly influence the clustering results. Common methods include Z-score standardization or Min-Max scaling.

[0360] 3. According to the characteristics of the data and the expected clustering structure, select an appropriate clustering algorithm, such as the K-means clustering algorithm.

[0361] 4. Implement the clustering algorithm. Taking K-means as an example, the basic steps are as follows:

[0362] a) Randomly select K center points

[0363] b) Assign each point to the nearest center point

[0364] c) Recalculate the center point of each cluster

[0365] d) Repeat b) and c) until convergence or the maximum number of iterations is reached

[0366] 5. Use methods such as the elbow rule, silhouette coefficient, or gap statistics to determine the optimal number of clusters.

[0367] 6. Use internal evaluation metrics (such as silhouette coefficient, Calinski-Harabasz index) and external evaluation metrics (if labeled data is available) to evaluate the clustering quality.

[0368] 7. Analyze the characteristics of each cluster, including geographical distribution, signal characteristics, etc., and infer the type of radiation source that each cluster may represent.

[0369] 8. Based on the clustering results, a classification model (such as support vector machine SVM or random forest) can be trained to automatically classify new radiation sources.

[0370] Through the above steps, a radiation source classification result can be finally obtained, and each radiation source is assigned a type label. This classification not only takes into account the geographical location, but also integrates the signal characteristics, so it can more accurately reflect the essential attributes of the radiation source. This classification result helps to understand the distribution pattern of different types of radiation sources in the target area and identify potential interference sources or abnormal signals.

[0371] The present invention also discloses an electromagnetic environment analysis system combining situational awareness and space modeling, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electromagnetic environment analysis method combining situational awareness and space modeling as described in any one of the above embodiments is implemented.

[0372] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0373] Among them, the memory can be an internal storage unit of a computer device, such as a hard disk or memory of a computer device, or an external storage device of a computer device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD) or a flash memory card (FC) equipped on the computer device, and the memory can also be a combination of an internal storage unit and an external storage device of a computer device. The memory is used to store computer programs and other programs and data required by the computer device. The memory can also be used to temporarily store data that has been output or is to be output, and this application does not impose any restrictions on this.

[0374] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of protection of the present application is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as above, which are not provided in detail for the sake of simplicity.

[0375] One or more embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the present application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present application should be included in the protection scope of the present application.

Claims

1. An electromagnetic environment analysis method combining situational awareness and spatial modeling, It is characterized in that Applied to an electromagnetic environment detection system, the electromagnetic environment detection system includes three mobile detection devices with the same configuration, each of which includes a mobile drone base station and an electromagnetic signal acquisition and analysis device, and an electromagnetic detection drone is deployed in each of the mobile drone base stations. Any two of the mobile detection devices maintain a communication connection, and the electromagnetic detection drone is in communication connection with all the mobile drone base stations, and the electromagnetic detection drone can stay at any of the mobile drone base stations; The method comprises the following steps: Obtain regional map information of the target area where electromagnetic environment analysis is required; Based on the regional map information and through the GIS system, a regional three-dimensional model of the target area is constructed; Identifying the area range of the target area according to the area boundary of the target area in the area map information; Taking the center of the largest inscribed circle in the target area as the range expansion base point, and generating a virtual area after the target area is proportionally enlarged according to a preset enlargement ratio; taking an area between a virtual area boundary of a virtual area and the area boundary in the area map information as a preselected deployment area; Using the regional three-dimensional model, all areas in the pre-selected deployment area that do not meet the preset detection requirements are screened out, and all the retained pre-selected deployment areas are used as target deployment areas; Taking all the position coordinate points in the target deployment area as an initial deployment point set; Randomly selecting three position coordinate points from the initial deployment point set as initial deployment points based on a preset distance constraint condition, wherein the distance constraint condition is that the straight-line distance between any one of the initial deployment points and the other two of the initial deployment points is equal; The deployment point coverage area in the target area is calculated according to the deployment point coordinates of all the initial deployment points, the deployment point coverage area is the overlapping area between the complete coverage area formed by all the initial deployment points and the target area, and the complete coverage area is the area enclosed by the shortest straight line segment between any two of the initial deployment points; Taking maximizing the coverage area of ​​the deployment points as the optimization goal, all the initial deployment points are adjusted to the optimal deployment points based on the distance constraint condition and using an optimization algorithm; The optimal deployment point is used as the device deployment point of the mobile detection device, all the device deployment points are located at the edge of the area boundary of the target area and outside the target area, and the straight-line distance between any one of the device deployment points and the other two device deployment points is equal; Deploy all the mobile detection devices according to the device deployment points; After all the mobile detection devices are deployed to the device deployment point, the drone detection task of the electromagnetic detection drone and the equipment detection task of the electromagnetic signal acquisition and analysis device are configured in combination with the device deployment point and the area range of the target area in the area map information; Using the mobile drone base station to send the drone detection task to the electromagnetic detection drone currently deployed by the mobile drone base station; Control all the electromagnetic detection drones to perform the drone detection task in silent communication mode, so that the electromagnetic detection drones detect the electromagnetic environment situation in the target area, and control all the electromagnetic signal acquisition and analysis equipment to perform the equipment detection task, so that the electromagnetic signal acquisition and analysis equipment detects the electromagnetic environment situation in the target area; The mobile detection device obtains the first area detection results of all the electromagnetic detection drones when they complete the drone detection task, and simultaneously obtains the second area detection results of all the electromagnetic signal acquisition and analysis devices when they complete the device detection task; The radiation source positions and radiation source types of all radiation sources in the target area are obtained by combining the first area detection result and the second area detection result for analysis.

2. The electromagnetic environment analysis method combining situational awareness and space modeling according to claim 1, It is characterized in that The optimization goal of maximizing the coverage area of ​​the deployment points is to adjust all the initial deployment points to the optimal deployment points based on the distance constraint and using an optimization algorithm, and the steps include: Constructing an objective function with maximizing the coverage area of ​​the deployment points as the optimization goal; Constructing a first initial population of an optimization algorithm based on all of the initial deployment points; Based on the distance constraint condition and taking approximation of the objective function as the optimization direction, the first initial population is iteratively updated using the optimization algorithm to obtain a first optimal population under the distance constraint condition; If the first optimal population satisfies the objective function, an optimal deployment point is determined according to the first optimal population.

3. The electromagnetic environment analysis method combining situational awareness and space modeling according to claim 2, It is characterized in that The method further comprises the steps of: If the first optimal population cannot satisfy the objective function, the distance constraint condition is adjusted so that the straight-line distance difference between any one of the initial deployment points and the other two initial deployment points is less than a preset difference threshold; Using the first optimal population as a second initial population of the optimization algorithm; Based on the adjusted distance constraint condition and taking approximation of the objective function as the optimization direction, the second initial population is iteratively updated using the optimization algorithm to obtain a second optimal population under the adjusted distance constraint condition; The optimal deployment point is determined according to the second optimal population.

4. The electromagnetic environment analysis method combining situational awareness and space modeling according to claim 1, It is characterized in that The method of using the regional three-dimensional model to screen out all areas in the pre-selected deployment area that do not meet the preset detection requirements, and taking all the retained pre-selected deployment areas as target deployment areas comprises the following steps: Acquire the model heights of all boundary models located at the boundary of the target area in the regional three-dimensional model; Mark the boundary model whose model height exceeds the obstacle height threshold in the preset detection requirement as an ultra-high boundary model, and obtain the model longitude and latitude coordinates of both ends of the ultra-high boundary model in the three-dimensional model of the region; Generating a first prohibited deployment area that does not meet the preset detection requirements in the preselected deployment area of ​​the regional map information based on the model longitude and latitude coordinates; Screening out all the first prohibited deployment areas in the pre-selected deployment areas, and taking all the retained pre-selected deployment areas as preferred deployment areas; Based on the geological requirements for device deployment in the preset detection requirements and using the GIS system, a regional screening step is performed on the preferred deployment areas, and all the retained preferred deployment areas are used as target deployment areas.

5. The electromagnetic environment analysis method combining situational awareness and space modeling according to claim 4, It is characterized in that The step of performing a regional screening step on the preferred deployment area based on the device deployment geological requirements in the preset detection requirements and using the GIS system, and taking all the retained preferred deployment areas as target deployment areas comprises the following steps: Acquiring regional geological information of the preferred deployment area through the GIS system; Combining the regional geological information and the device deployment geological requirements in the preset detection requirements, generating a second prohibited deployment area in the preferred deployment area that does not meet the preset detection requirements; All the second prohibited deployment areas in the preferred deployment area are screened out, and all the retained preferred deployment areas are used as target deployment areas.

6. The electromagnetic environment analysis method combining situational awareness and space modeling according to claim 1, It is characterized in that The configuration of the drone detection task of the electromagnetic detection drone and the equipment detection task of the electromagnetic signal acquisition and analysis device in combination with the device deployment point and the area range of the target area in the area map information comprises the following steps: Based on the device deployment point and according to the device preset parameters of the electromagnetic signal acquisition and analysis device, generating the device perception range of the electromagnetic signal acquisition and analysis device in the regional map information; Identifying the area range of the target area according to the area boundary of the target area in the area map information; Eliminate the overlapping portion of the area range with the sensing range of the device, and use the remaining area range as the detection range of the electromagnetic detection drone; Based on the detection range and the device deployment point, respectively configure the drone detection task of the electromagnetic detection drone in each of the mobile drone base stations; The device detection task of the electromagnetic signal acquisition and analysis device is configured in combination with the device sensing range and the drone detection time in the drone detection task.

7. The electromagnetic environment analysis method combining situational awareness and space modeling according to claim 6, It is characterized in that The drone detection task of configuring the electromagnetic detection drone in each of the mobile drone base stations based on the detection range and the device deployment point includes the following steps: Determine an initial range division point in the detection range based on the device deployment point, wherein the straight-line distances between the initial range division point and all the device deployment points are equal; generating a segmentation boundary in the detection range by the shortest straight line between the initial range segmentation point and the device deployment point; Assigning a base station detection range to each of the mobile drone base stations in combination with the segmentation boundary and the range boundary of the detection range; For each of the mobile drone base stations, based on the detection range of the base station and with another mobile drone base station as the path endpoint, an optimal path algorithm is used to generate the drone optimal path of the electromagnetic detection drone, and all the mobile drone base stations can only serve as the path endpoint of one drone optimal path at most in the same drone detection mission; Calculating the flight time of the electromagnetic detection drone in the optimal path of the drone based on the drone flight parameters preset by the mobile drone base station; If the flight time is the maximum flight time among the flight times calculated by all the mobile drone base stations, configuring the drone detection task of the electromagnetic detection drone in combination with the drone optimal path and the drone flight parameters; If the flight time is not the maximum flight time among the flight times calculated by all the mobile UAV base stations, the UAV flight parameters are adjusted until the flight time is the same as the maximum flight time, and the UAV detection mission of the electromagnetic detection UAV is configured in combination with the optimal UAV path and the adjusted UAV flight parameters.

8. The electromagnetic environment analysis method combining situational awareness and space modeling according to claim 1, It is characterized in that The analyzing and obtaining the radiation source positions and radiation source types of all radiation sources in the target area by combining the first area detection result and the second area detection result comprises the following steps: Combining the first area detection result and the second area detection result to analyze and obtain the electromagnetic environment situation awareness result in the target area; Identify the radiation source longitude and latitude data of all radiation sources in the target area based on the electromagnetic environment situation awareness result; Based on the latitude and longitude data of the radiation sources and using a clustering classification algorithm, a clustering analysis is performed on all the radiation sources to obtain the radiation source types of all the radiation sources.

9. An electromagnetic environment analysis system combining situational awareness and spatial modeling, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the computer program, the electromagnetic environment analysis method combining situational awareness and space modeling as described in any one of claims 1 to 8 is implemented.

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