Method and system for verifying and evaluating intelligent network connection signal coverage capability of low-altitude meta-universe

Abstract

This invention provides a method and system for verifying and evaluating the signal coverage capability of intelligent connected vehicles in a low-altitude metaverse environment. It is used to automatically evaluate the signal coverage status between low-altitude aircraft flight paths and ground-based connected devices in a three-dimensional real-world environment. The method involves discretely sampling the low-altitude aircraft flight path in a three-dimensional real-world environment, using ray detection to obtain the number of obstructions, and combining free-space path loss and nonlinear cumulative penetration loss models to calculate the received signal strength and determine the coverage status. The system ultimately calculates the coverage rate and generates a verification report. This invention establishes a quantitative correlation between ray detection and signal loss, achieving accurate, efficient, and automated evaluation of low-altitude signal coverage capability under complex obstruction conditions.

Description

Technical Field

[0001] This invention belongs to the technical fields of low-altitude communication coverage assessment, three-dimensional real-scene simulation and intelligent connected vehicle communication verification, specifically involving a method and system for verifying and evaluating the intelligent connected signal coverage capability for the low-altitude metaverse. Background Technology

[0002] The large-scale operation of intelligent connected aircraft such as low-altitude unmanned aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft places stringent demands on the continuous coverage capability of low-altitude communication signals. In complex environments such as urban canyons and densely built-up areas, the direct path between aircraft and ground-based connected devices is easily obstructed by buildings, leading to signal attenuation or even communication interruption, directly impacting flight safety and operational efficiency. Therefore, pre-simulation evaluation of signal coverage capability for preset routes in a three-dimensional real-world environment is of great significance for the safe operation of low-altitude intelligent connected systems.

[0003] However, existing technologies face the following challenges:

[0004] Among the challenges mentioned above, the accuracy of the signal attenuation model has a decisive impact on the reliability of the evaluation results. The linear penetration loss model used in the prior art assumes that the loss is the same on each wall and simply adds up the total loss. This fails to reflect the physical law of the energy reduction of the signal after multiple penetrations, resulting in significant deviations in scenarios with multiple walls blocking the signal and often underestimating the actual signal coverage.

[0005] 1. Lack of refined signal coverage simulation: Traditional line-of-sight analysis based on digital elevation models (DEM) cannot accurately simulate complex obstructions such as buildings and vegetation, while the low-altitude metaverse requires a high-fidelity three-dimensional real-world environment.

[0006] 2. The signal attenuation model is too simplistic: Most methods only consider distance attenuation (free space model), ignore the penetration loss of building materials, or use linear superposition, which leads to physical distortion.

[0007] 3. Low computational efficiency in large scenes: For large-area 3D real scenes (tens of square kilometers) at the city level, the performance of existing ray detection methods is insufficient and it is difficult to support real-time verification requirements.

[0008] 4. Insufficient automated assessment capabilities: It lacks one-click verification and structured report output, and cannot be integrated into the automated operation and maintenance process of the low-altitude metaverse.

[0009] Therefore, there is an urgent need for a method to verify and evaluate the signal coverage capability of intelligent connected vehicles in the low-altitude metaverse, which can efficiently and accurately evaluate the signal coverage of flight routes to ground connected devices based on Unreal Engine 3D reality. Summary of the Invention

[0010] Purpose of the invention: The technical problem to be solved by the present invention is to provide a method and system for verifying and evaluating the signal coverage capability of intelligent connected vehicles in the low-altitude metaverse, which is in response to the shortcomings of the existing technology. It can perform high-precision automated evaluation of the signal coverage status between low-altitude aircraft flight paths and ground connected devices in a three-dimensional real-world environment, taking into account free space distance attenuation and penetration loss of obstructions. This solves the problems of insufficient line-of-sight analysis accuracy, overly simplified signal attenuation models, and low computational efficiency in large scenes in the existing technology.

[0011] The method includes the following steps:

[0012] Step 1: Obtain a 3D real-world scene, parameters of at least one ground-based connected device, and a preset flight path; wherein, the 3D real-world scene is a 3D model containing obstructions, and the parameters of the ground-based connected device include transmission power, operating frequency, antenna gain, and spatial coordinates;

[0013] Step 2, discrete sampling of flight routes;

[0014] Step 3: Perform calculations by traversing each sampling point;

[0015] Step 4: Calculate the overall signal coverage.

[0016] In step 1, M ground-based networked devices are set up in the scene, and a preset route A is imported. The route consists of a series of 3D waypoints W1, W2, ..., W K The structure consists of coordinates (x, y, z) and an optional timestamp for each waypoint. The three-dimensional coordinates of the k-th waypoint are (x, y, z). k ,y k ,z k The three-dimensional coordinates of the (k+1)th waypoint are (x... k+1 ,y k+1 ,z k+1 ), where W K This represents the k-th three-dimensional waypoint.

[0017] Step 2 includes: defining the sampling step size Δ and calculating the total route length L.

[0018] ,

[0019] Number of sampling points ;

[0020] The arc length parameter s corresponding to the i-th sampling point is s = i•Δ; i = 0, 1, ..., N-1;

[0021] Find the flight segment [W] corresponding to the arc length of the i-th sampling point. k W k+1 Let the flight segment [W] be... k W k+1 The cumulative arc length at the starting point is L.k The cumulative arc length at the endpoint is L. k+1 Then the interpolation coefficient t is:

[0022] ,

[0023] Sampling point coordinates ;

[0024] In step 3, for each sampling point P i And each connected device Q j For j=0,…,M-1, perform the following steps:

[0025] Step 3-1, calculate the straight-line distance;

[0026] Let sampling point P i The three-dimensional coordinates are (x i ,y i ,z i ), Equipment Q j The three-dimensional coordinates are (x j ,y j ,z j If ), then the Euclidean distance d is:

[0027] ;

[0028] Convert to kilometers: d km =d / 1000; d km This is the converted distance;

[0029] Step 3-2, X-ray detection and hit count statistics;

[0030] From sampling point P i To ground-based network devices Q j Emit a ray and obtain all collision surfaces that the ray passes through from the starting point to the ending point;

[0031] The starting point for X-ray detection is set to the sampling point coordinates P. i The endpoint is set to the coordinates Q of the ground-based connected device. j ;

[0032] After completing the ray detection, obtain the set of hit collision surfaces and get the number of hits n;

[0033] Step 3-3, Free space path loss calculation;

[0034] Free space path loss L fs The calculation uses the free space propagation model, and the expression is:

[0035] ,

[0036] Among them, fMHz Operating frequency;

[0037] Steps 3-4: Calculate the penetration loss;

[0038] Steps 3-5: Calculate the received signal strength RSSI;

[0039] Steps 3-6: Coverage determination.

[0040] Steps 3-4 include: defining the penetration loss L pen It is a non-linear accumulation function with the number of hits n as the variable. The function results are obtained for different values ​​of the number of hits n.

[0041] .

[0042] In steps 3-5, the device transmit power P is defined. t With the device antenna gain G t The received signal strength RSSI is calculated using the following formula:

[0043] .

[0044] Steps 3-6 include: setting the signal coverage threshold RSSI th The result of CoverageFlag(i,j) is obtained, showing that the i-th sampling point is covered by the j-th device.

[0045] ,

[0046] CoverageFlag(i,j) is set to 1 to indicate coverage and 0 to indicate no coverage.

[0047] Step 4 includes: After traversing all sampling points N and all devices M, let the total number of coverage points S be:

[0048] ,

[0049] The overall signal coverage rate is: Coverage Rate = [S / (N×M)]×100%;

[0050] For the calculation of auxiliary statistical indicators, the coverage rate of the j-th device for:

[0051] ;

[0052] Average received signal strength for: ;

[0053] The minimum received signal strength RSSI is: min i,jRSSI(i,j).

[0054] The present invention also provides a verification and evaluation system for intelligent connected signal coverage capability in the low-altitude metaverse for implementing the method, comprising the following modules:

[0055] Scene management module: used to load 3D real-scene scenes and connected devices;

[0056] Route sampling module: used for discrete sampling of routes;

[0057] Loss calculation module: used to calculate free space path loss and penetration loss, and obtain the received signal strength RSSI;

[0058] Coverage statistics module: used to summarize the judgment results and calculate the coverage rate.

[0059] The present invention also provides an electronic device, including a processor and a memory, the memory storing program code that, when executed by the processor, causes the processor to perform the steps of the method.

[0060] The method of this invention can be applied to low-altitude UAV route planning, urban air traffic communication support, and signal coverage analysis and prediction in low-altitude digital twin scenarios.

[0061] Beneficial effects: This invention quantitatively couples ray detection results from a 3D real-world scene with a signal loss model, accurately simulating the attenuation characteristics of a signal after passing through multiple obstructions using a nonlinear penetration loss model. This achieves high-precision automated assessment of low-altitude flight path signal coverage capabilities in complex obstruction environments. This method does not rely on a single implementation of a specific software platform and can serve as a general technical solution applicable to various 3D simulation environments.

[0062] In the coverage statistics step of the above method, the comprehensive signal coverage rate is the ratio of the number of covered sampling points - device pairs to the total number of sampling points - device pairs. At the same time, the independent coverage rate of each device can be calculated according to the device dimension.

[0063] In the verification report generation step of the above method, the verification report also includes weak coverage area information, which includes the location coordinates of the sequence of continuously lost sampling points and the corresponding main obstruction identifiers.

[0064] This invention achieves high-precision, physically reasonable, and highly efficient verification of low-altitude intelligent network signal coverage, filling a gap in the field of low-altitude metaverse communication simulation. Attached Figure Description

[0065] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0066] Figure 1 This is an overall flowchart of the intelligent connected signal coverage capability verification and evaluation method for the low-altitude metaverse described in this invention.

[0067] Figure 2 This is a schematic diagram of the route discrete sampling method described in this invention, showing the spatial relationship between waypoints and discrete sampling points on a preset route.

[0068] Figure 3 This is a schematic diagram of the ray detection principle based on a three-dimensional real-scene as described in this invention, showing the spatial relationship between sampling points, ground-based network equipment, obstructing buildings, and ray hit locations. Detailed Implementation

[0069] This invention provides a method for verifying and evaluating the signal coverage capability of intelligent connected vehicles for the low-altitude metaverse, comprising the following steps:

[0070] Step 1, scenario and data preparation, such as Figure 1 As shown;

[0071] Step 1-1: Import the 3D real-world scene;

[0072] A 3D reality model containing occlusions such as buildings is loaded into a 3D simulation environment. This 3D reality model can be acquired through methods such as oblique photogrammetry and laser scanning, and stored in a format importable into the simulation environment. Each geometry in the 3D reality model is configured with collision information to support subsequent ray detection operations, ensuring that rays emitted from sampling points can correctly identify their hit relationships with building surfaces. In an optional implementation, the 3D reality model can be imported using Unreal Engine's Datasmith technology, and the model's collision preset can be set to support object type detection.

[0073] Steps 1-2: Configuring ground-based network equipment;

[0074] Set up M terrestrial network devices (e.g., 5G communication base stations) in the scenario. Each device is configured with the following transmitter parameters: Transmit power Pt: in dBm, typically ranging from 10dBm to 30dBm (a typical value of 20dBm is used here). Each terrestrial network device is also configured with the following receiver parameters: Receiver sensitivity: in dBm, typically -90dBm to -100dBm (this parameter determines the signal coverage threshold RSSIth). Receiver antenna gain Gr: in dBi (can be set according to actual conditions, default value is 0dBi). Operating frequency f: in MHz, typically ranging from 900MHz to 5.8GHz (a typical value of 2400MHz, 2.4GHz ISM band). Antenna gain Gt: in dBi (typically ranging from 0dBi to 6dBi, 0dBi is usually used for omnidirectional antennas). Spatial coordinates (x... j ,y j ,z j ): World coordinates in a 3D scene.

[0075] Steps 1-3: Import flight routes;

[0076] Import the preset route A, which consists of a series of 3D waypoints W1, W2, ..., W K Each waypoint is composed of coordinates (x, y, z) and an optional timestamp. Flight routes can be generated using relevant platform components, such as the Spline component of a standard engine, or loaded from an external CSV file.

[0077] Step 2: Discrete sampling of flight routes, such as Figure 2 As shown;

[0078] To transform a continuous flight path into discrete calculation points, a sampling step size Δ is defined, typically ranging from 0.1m to 1.0m; in this embodiment, 0.5m is used. The total flight path length L is then calculated first:

[0079] ,

[0080] It should be noted that the selection of the sampling step size Δ should comprehensively consider the total length of the flight path, the scale of typical occluders in the scene, and the target evaluation accuracy. While a step size that is too small can increase sampling density, it will significantly increase the computational load; a step size that is too large may miss small-scale occluders. It is recommended to use half the width of the narrowest typical occluder in the scene as the initial step size, and fine-tune it to the optimal value through comparison and verification in actual testing.

[0081] Number of sampling points The arc length parameter s = i•Δ corresponds to the i-th sampling point (i = 0, 1, ..., N-1). Find the flight segment [W] containing this arc length. k W k+1Let the cumulative arc length at the starting point of this flight segment be L. k The cumulative arc length at the endpoint is L. k+1 Then the interpolation coefficients are:

[0082] ,

[0083] Sampling point coordinates ;

[0084] In practice, it is recommended to use Unreal Engine's GetLocationAtDistanceAlongSpline function to automate the process. The default step size Δ = 0.5 meters, which can be adjusted according to the scene size and accuracy requirements (for example, 1 meter can reduce the amount of computation, but may miss small areas of occlusion).

[0085] Step 3: Perform traversal calculations point by point;

[0086] For each sampling point P i (i = 0, ..., N-1) and each connected device Q j (j=0,…,M-1), execute the following sub-steps.

[0087] In practical deployments, the relevant parameters for ray detection can be adjusted according to the scale of the scene and the accuracy requirements. For smaller scenes or verification tasks with high accuracy requirements, complex colliders can be enabled to improve hit detection accuracy; for large-scale scenes in cities or tasks that only require rapid screening, simplified colliders can be used to improve computational efficiency.

[0088] Step 3-1, calculate the straight-line distance;

[0089] Let the three-dimensional coordinates of sampling point Pi be (x... i ,y i ,z i The three-dimensional coordinates of device Qj are (x, y, y). j ,y j ,z j If ), then the Euclidean distance d is:

[0090] ,

[0091] For use in subsequent formulas, convert to kilometers: d km =d / 1000.

[0092] Step 3-2, X-ray detection and hit count statistics, such as Figure 3 As shown;

[0093] Ray ray detection utilizes collision information from the geometric configuration of a 3D real-world scene, starting from sampling point P. i To ground-based network devices Q jA ray is emitted, and all collision surfaces traversed by the ray from its starting point to its ending point are obtained. In a specific implementation, an object-type-based ray detection method can be used. This method does not depend on the collision response settings of each geometry (i.e., it can detect objects regardless of whether they are set to blocking or overlapping mode), but only filters detectable targets based on object type, thereby returning the collision surface information of all matching objects traversed by the ray.

[0094] The starting point for X-ray detection is set to the sampling point coordinates P. i The endpoint is set to the coordinates Q of the ground-based connected device. j The object type filtering condition is set to cover the types corresponding to all static and dynamic geometry in the 3D reality model to ensure that occlusions such as buildings and terrain are included in the detection range.

[0095] After completing raycasting, obtain the set of hit collision surfaces. Define the number of hits, n, as:

[0096] ,

[0097] n represents the number of times the ray penetrates the surface of the object from the starting point to the ending point. For a hollow 3D reality model (such as a building shell), a ray passing through a building usually results in 2 hits (entry and exit), but due to the complex geometry of the facade (balconies, decorative columns, etc.), the actual number of hits may be between 5 and 10.

[0098] Step 3-3, Free space path loss calculation;

[0099] Free space path loss L fs The calculation uses the free space propagation model, and the expression is:

[0100] ,

[0101] Where, d km f represents distance (in kilometers). MHz This refers to the operating frequency (unit: MHz). If using distance units in meters, the formula needs to be adjusted as follows: This formula is based on the physical derivation of free-space propagation, and the constant term comes from unit conversion. To maintain consistency in calculations, distances in this invention are uniformly expressed in kilometers.

[0102] Steps 3-4: Calculate the penetration loss;

[0103] According to actual measurement data, the signal loss of a 2.4GHz signal penetrating a reinforced concrete wall is approximately 35dB per wall. However, when penetrating multiple walls, the total loss is less than the linear superposition. This is because after the signal energy attenuates, the remaining portion is replenished through diffraction, reflection, and other paths, resulting in a decrease in the additional loss introduced by subsequent walls.

[0104] Based on this, a nonlinear accumulation function L is defined. pen =f(n):

[0105] ,

[0106] The physical meaning of this model is as follows: First wall: The signal penetrates directly, resulting in the greatest loss (35dB). Second wall: The signal has attenuated, and the transmission efficiency has decreased, but some diffracted energy enters, reducing the additional loss to 15dB (cumulative 50dB). Third wall: Further decreases, with an additional loss of 10dB (cumulative 60dB). Fourth wall and beyond: The signal is extremely weak; with each additional penetration, the additional loss is approximately 5dB, approaching saturation.

[0107] Steps 3-5: Calculate the Received Signal Strength (RSSI):

[0108] ,

[0109] Unit: dBm. For example, if the transmit power P t =20dBm, antenna gain G t =0dBi, free space path loss L fs =100dB, penetration loss L pen =35dB; then the received signal strength is... .

[0110] Steps 3-6: Coverage determination;

[0111] Set the signal coverage threshold RSSI th Based on the sensitivity of a typical communication base station receiver, RSSI is recommended. th =-90dBm. Therefore:

[0112] CoverageFlag(i, ,

[0113] CoverageFlag(i,j) represents the coverage determination flag, which is set to 1 when the i-th sampling point is covered by the j-th device, and 0 otherwise.

[0114] Users can adjust the threshold according to the specific device performance. For example, if high reliability is required, it can be set to -85dBm, and if weak coverage is allowed, it can be set to -95dBm.

[0115] Step 4: Calculate the overall signal coverage.

[0116] After traversing all sampling points and all devices, let the total number of coverage points be:

[0117] ,

[0118] The overall signal coverage rate is: CoverageRate = [S / (N×M)]×100%;

[0119] Then, auxiliary statistical indicators can be calculated, such as the coverage rate of the j-th device. for:

[0120] ;

[0121] The average RSSI is: ;

[0122] The minimum RSSI is: min i,j RSSI(i,j);

[0123] In areas with weak coverage, a sequence of points will be lost consecutively (at least 5 consecutive sampling points). The starting and ending coordinates of these points will be recorded, and the main occluders that caused the loss will be analyzed (the label or type of the first hit object can be extracted from the OutHits set).

[0124] The "Shenzhen North Railway Station Area Verification" example illustrates the verification process, which uses a 24-square-kilometer area extending northeastward from Shenzhen North Railway Station in Longhua District, Shenzhen as the core. The 3D reality model in the scenario has a resolution of 0.05m / pixel and approximately 250 million triangle faces. The intelligent device network consists of 50 communication base stations with an average spacing of approximately 500 meters. The flight paths within the scenario are primarily for logistics drones, totaling 12.3 kilometers in length, with waypoint altitudes ranging from 50 to 120 meters. Using a sampling step size of 0.5 meters, approximately 24,600 sampling points were obtained; the transmission power P of the communication base stations... t =20dBm, operating frequency f=2400MHz, antenna gain G t =0dBi, signal strength threshold RSSI t =-90dBm.

[0125] The next step is to perform X-ray detection on each sampling point-device pair to obtain a total number of X-rays = 24,600 × 50 = 1,230,000.

[0126] The calculation was then performed using the principle of ray detection, which took approximately 22 seconds in total. The number of hits, n, was then counted, and the average value was approximately 4.2 hits per ray. The final RSSI was calculated as follows: RSSI in the unobstructed area was approximately -66 dBm, RSSI in a single-building obstructed area was approximately -86 dBm, and RSSI in three-building obstructed areas was approximately -106 dBm.

[0127] The overall coverage rate was 87.3%, with an average RSSI of -68.5 dBm and a minimum RSSI of -112 dBm (located behind a super high-rise building in the North Station business district). The coverage distribution across different devices was as follows: 48 devices had coverage exceeding 85%, and 2 devices had coverage rates of 72.1% and 68.4%, both located in the core business district with the highest building density. Analysis of weak coverage areas showed that continuously lost sampling points were mainly concentrated in densely populated areas with buildings exceeding 80 meters in height and less than 15 meters between buildings. Signal obstruction was severe in these areas, and it is recommended to adjust flight path altitude or add supplementary network equipment in these areas.

[0128] Weak coverage areas are mainly concentrated in areas with buildings taller than 80 meters and high density. It is recommended to adjust flight routes or add supplementary network equipment.

[0129] Five test points were selected in the comparative verification environment, and RSSI was measured using a handheld spectrum analyzer. The results were compared with the simulation results, and the mean absolute error was 4.2 dB, with a maximum error of 9.1 dB, which verified the effectiveness of the model.

[0130] To evaluate the sensitivity of this technical solution to different input parameters, parameter sensitivity analysis was performed. The sampling step size and RSSI threshold were varied, and the changes in overall coverage were observed. When the sampling step size was 0.25m, 0.5m, and 1.0m, the corresponding overall coverage were 87.6%, 87.3%, and 86.9%, respectively, with differences of less than 1 percentage point, indicating that this method is not sensitive to the sampling step size, and a step size of 0.5m is sufficient. When the RSSI threshold was -85dBm, -90dBm, and -95dBm, the corresponding overall coverage were 82.1%, 87.3%, and 91.5%, respectively, indicating that the threshold selection has a significant impact on the results, and users can flexibly set it according to the reliability requirements of the task.

[0131] To verify the rationality of the nonlinear accumulation model used in this technology, a penetration loss model is compared with that in the traditional linear model (L... pen =35×n) for comparison. Under the linear model, the penetration loss L when penetrating three buildings. pen =105dB, far exceeding the 60dB of the model in this technology. Recalculating using a linear model, the overall coverage rate plummeted from 87.3% to 12.7%, a serious discrepancy with the measured results. This indicates that the linear model overestimates signal attenuation, leading to erroneous assessments, thus proving the necessity of using a nonlinear model in this technology.

[0132] Since this technology involves the calculation of a large number of sampling points, it consumes a lot of hardware computing power. Performance optimization measures can be used, especially for the problem of large computational load in ray detection in large-scale 3D real-world scenes. The following optimization strategies can be adopted:

[0133] In one alternative implementation, ray detection is performed using asynchronous batch processing, allocating the detection tasks of multiple sampling point-device pairs to background computing resources to avoid ray detection calculations blocking the main execution process and maintain the smoothness of simulation operation.

[0134] In one alternative implementation, a batch processing strategy is adopted, in which all sampling points are divided into multiple batches and the detection and calculation are performed in batches. Each batch processes a fixed number of sampling points, and the batches are advanced at a preset time interval, thereby controlling the computing load of a single frame while ensuring the integrity of the calculation.

[0135] In one alternative implementation, simplified colliders (such as convex hulls or bounding boxes) are pre-generated for the 3D reality model to reduce the geometric intersection computation cost of a single ray detection. Simplified colliders reduce the number of triangles involved in collision detection while maintaining the overall outline of the occluded object, significantly improving the computational efficiency of large-scale ray detection.

[0136] This invention provides a method and system for verifying and evaluating the coverage capability of intelligent connected signals in the low-altitude metaverse. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. A method for verifying and evaluating the coverage capability of intelligent connected signals in the low-altitude metaverse, characterized in that, Includes the following steps: Step 1: Obtain a 3D real-world scene, parameters of at least one ground-based connected device, and a preset flight path; wherein, the 3D real-world scene is a 3D model containing obstructions, and the parameters of the ground-based connected device include transmission power, operating frequency, antenna gain, and spatial coordinates; Step 2, discrete sampling of flight routes; Step 3: Perform calculations by traversing each sampling point; Step 4: Calculate the overall signal coverage.

2. The method as described in claim 1, characterized in that, In step 1, M ground meshed devices are set in the scene, and a preset flight path A is imported, which is composed of a series of three-dimensional waypoints W1, W2,..., W K M, each waypoint contains coordinates (x, y, z) and optional timestamp, the three-dimensional coordinates of the kth waypoint are (x k ,y k ,z k ), the three-dimensional coordinates of the k+1th waypoint are (x k+1 ,y k+1 ,z k+1 ), where W K represents the kth three-dimensional waypoint.

3. The method as described in claim 2, characterized in that, Step 2 includes: defining the sampling step size Δ and calculating the total route length L. ， Number of sampling points ; The arc length parameter s corresponding to the i-th sampling point is s = i•Δ; i = 0, 1, ..., N-1; Find the flight segment [W] corresponding to the arc length of the i-th sampling point. k W k+1 Let the flight segment [W] be... k W k+1 The cumulative arc length at the starting point is L. k The cumulative arc length at the endpoint is L. k+1 Then the interpolation coefficient t is: ， Sampling point coordinates .

4. The method as described in claim 3, characterized in that, In step 3, for each sampling point P i And each connected device Q j For j=0,…,M-1, perform the following steps: Step 3-1, calculate the straight-line distance; Let sampling point P i The three-dimensional coordinates are (x i ,y i ,z i ), Equipment Q j The three-dimensional coordinates are (x j ,y j ,z j If ), then the Euclidean distance d is: ； Convert to kilometers: d km =d / 1000; d km This is the converted distance; Step 3-2, X-ray detection and hit count statistics; From sampling point P i To ground-based network devices Q j Emit a ray and obtain all collision surfaces that the ray passes through from the starting point to the ending point; The starting point for X-ray detection is set to the sampling point coordinates P. i The endpoint is set to the coordinates Q of the ground-based connected device. j ; After completing the ray detection, obtain the set of hit collision surfaces and get the number of hits n; Step 3-3, Free space path loss calculation; Free space path loss L fs The calculation uses the free space propagation model, and the expression is: ， Among them, f MHz Operating frequency; Steps 3-4: Calculate the penetration loss; Steps 3-5: Calculate the received signal strength RSSI; Steps 3-6: Coverage determination.

5. The method as described in claim 4, characterized in that, Steps 3-4 include: defining the penetration loss L pen : 。 6. The method as described in claim 5, characterized in that, In steps 3-5, the device transmit power P is defined. t With the device antenna gain G t The received signal strength RSSI is calculated using the following formula: 。 7. The method as described in claim 6, characterized in that, Steps 3-6 include: setting the signal coverage threshold RSSI th The result of CoverageFlag(i,j) is obtained, showing that the i-th sampling point is covered by the j-th device. CoverageFlag(i, , CoverageFlag(i,j) is set to 1 to indicate coverage and 0 to indicate no coverage.

8. The method as described in claim 7, characterized in that, Step 4 includes: After traversing all sampling points N and all devices M, let the total number of coverage points S be: ， The overall signal coverage rate is: Coverage Rate = [S / (N×M)]×100%; For the calculation of auxiliary statistical indicators, the coverage rate of the j-th device for: ； Average received signal strength for: ; The minimum received signal strength RSSI is: min i,j RSSI(i,j).

9. A verification and evaluation system for intelligent connected signal coverage capability in low-altitude metaverse for implementing the method described in any one of claims 1 to 8, characterized in that, Includes the following modules: Scene management module: used to load 3D real-scene scenes and connected devices; Route sampling module: used for discrete sampling of routes; Loss calculation module: used to calculate free space path loss and penetration loss, and obtain the received signal strength RSSI; Coverage statistics module: used to summarize the judgment results and calculate the coverage rate.

10. An electronic device, characterized in that, It includes a processor and a memory, the memory storing program code that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 9.

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