A method, apparatus and readable storage medium for testing low-altitude network signals

Abstract

This application provides a method, apparatus, and readable storage medium for testing low-altitude network signals. The method includes: dividing a test area into a grid; setting the flight altitude and flight path of a UAV based on the grid division result; conducting network signal testing using the UAV based on the set flight altitude and flight path, and obtaining test results; performing data analysis based on the test results to obtain analysis results; and generating network optimization suggestions based on the analysis results. This application can more accurately test signals in low-altitude areas, ensure effective coverage of signal blind spots and weak areas, help identify strong and weak signal areas, quantify the severity of signals, and provide intelligent suggestions for subsequent network optimization based on severity scores.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, apparatus and readable storage medium for testing low-altitude network signals. Background Technology

[0002] With the rapid development of 5G technology, low-altitude network applications (such as drones) are placing higher demands on network coverage quality. However, current 5G network construction and optimization mainly focus on coverage for ground users, with insufficient consideration given to low-altitude environments. This is especially true in complex urban environments and densely populated areas with tall buildings, where low-altitude signal propagation is significantly affected by buildings, terrain, and other factors, leading to severe signal attenuation and even dead zones. This unstable signal quality impacts low-altitude applications such as drone communication based on 5G networks, necessitating urgent assessment and optimization of low-altitude 5G network coverage.

[0003] Currently, there are several 5G network coverage testing solutions that use drones carrying signal testing modules to conduct flight tests at specific altitudes and along specific paths. These existing solutions typically employ a fixed-radius flight path, using the drone to fly at a constant speed to collect parameters such as signal strength ratio (RSRP) and signal-to-noise ratio (SNR), and then transmit the data to a test terminal for coverage analysis.

[0004] However, existing solutions still have some problems, resulting in inaccurate and incomplete signal data acquisition. Summary of the Invention

[0005] The technical problem to be solved by this application is to provide a low-altitude network signal testing method, apparatus and readable storage medium to address the above-mentioned deficiencies of the prior art.

[0006] In a first aspect, this application provides a method for testing low-altitude network signals, wherein the test is performed based on an unmanned aerial vehicle (UAV), and the method includes:

[0007] S1. Divide the test area into grids, and set the drone's flight altitude and flight path based on the grid division results;

[0008] S2. Based on the set flight altitude and flight path, conduct network signal testing using the drone to obtain the test results;

[0009] The network signal testing process includes:

[0010] A primary path test is performed based on the flight path to obtain the primary path test results.

[0011] Intermediate path testing is performed based on the signal fluctuation grid in the primary path test results to obtain intermediate path test results;

[0012] Advanced path tests are performed based on the signal blind zone grid or signal weak zone grid in the intermediate path test results to obtain advanced path test results.

[0013] S3. Perform data analysis based on the test results to obtain the analysis results;

[0014] S4. Generate network optimization suggestions based on the analysis results.

[0015] In some embodiments, S2, performing a primary path test based on the flight path to obtain primary path test results includes:

[0016] Based on the flight path, network signal testing is conducted using a drone to obtain network signal strength parameters corresponding to each sampling point in each grid area.

[0017] In some embodiments, S2, performing an intermediate path test based on the signal fluctuation grid in the primary path test results to obtain intermediate path test results includes:

[0018] Based on the network signal strength parameters corresponding to each sampling point in each grid region, determine the number of sampling point pairs that are adjacent in position and whose network signal strength difference exceeds a preset threshold in each grid region. If the number reaches the preset threshold, the corresponding grid region is determined to be a signal fluctuation grid.

[0019] The signal fluctuation grid is divided into sub-grids to obtain the signal fluctuation grid. The secondary flight path of the UAV is set according to the secondary grid division result.

[0020] Based on the aforementioned secondary flight path, network signal testing is conducted using a drone to obtain the network signal strength parameters corresponding to each sampling point within each subgrid of the signal fluctuation grid.

[0021] In some embodiments, S2, the advanced path test is performed based on the signal blind zone grid or signal weak zone grid in the intermediate path test results to obtain advanced path test results, including:

[0022] Based on the network signal strength parameters corresponding to each sampling point in each sub-grid of the signal fluctuation grid, the average signal strength of each sub-grid is obtained. If the average signal strength is lower than a preset threshold, the corresponding sub-grid is determined to be a signal blind zone grid or a signal weak zone grid.

[0023] The optimal flight path is generated based on the signal blind zone grid or the signal weak zone grid.

[0024] Based on the optimal flight path, network signal testing is conducted using a drone to obtain the network signal strength parameters corresponding to each sampling point in the signal blind zone grid or signal weak zone grid.

[0025] In some embodiments, generating an optimal flight path based on the signal blind zone grid or signal weak zone grid includes:

[0026] The optimal sampling point and path within the signal blind zone grid or signal weak zone grid are calculated based on the signal model using an adaptive path algorithm. The signal gradient and signal change direction are calculated, and the optimal flight path is generated.

[0027] In some embodiments, S3 includes:

[0028] Based on the test results, a three-dimensional heat map of signal intensity distribution is generated using a spatial interpolation algorithm;

[0029] Based on the three-dimensional heat map of signal strength distribution, weak signal areas and signal blind areas are identified.

[0030] In some embodiments, S4 includes:

[0031] Based on the identified weak signal areas and blind signal areas, the severity of regional signal is assessed to obtain a problem severity score for each area;

[0032] Based on the severity score of the problem in each region, corresponding network optimization suggestions are generated.

[0033] The severity assessment indicators include: the degree of diffusion of weak signal areas, the adjacency and connectivity of weak signal areas, the signal strength fluctuation, and the geometric characteristics of the signal problem area.

[0034] Secondly, this application provides a low-altitude network signal testing device, the device comprising:

[0035] The grid generation module is set to divide the test area into grids and set the drone's flight altitude and flight path based on the grid generation results.

[0036] The signal testing module is configured to conduct network signal tests using a drone based on a set flight altitude and flight path, and obtain the test results.

[0037] The network signal testing process includes:

[0038] A primary path test is performed based on the flight path to obtain the primary path test results.

[0039] Intermediate path testing is performed based on the signal fluctuation grid in the primary path test results to obtain intermediate path test results;

[0040] Advanced path tests are performed based on the signal blind zone grid or signal weak zone grid in the intermediate path test results to obtain advanced path test results.

[0041] The data analysis module is configured to perform data analysis based on the test results and obtain analysis results.

[0042] The optimization suggestion module is configured to generate network optimization suggestions based on the analysis results.

[0043] Thirdly, this application provides a low-altitude network signal testing device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the low-altitude network signal testing method described in the first aspect above.

[0044] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the low-altitude network signal testing method described in the first aspect.

[0045] This application provides a low-altitude network signal testing method, apparatus, and readable storage medium. The testing is implemented using a UAV. The method includes: dividing the test area into grids; setting the UAV's flight altitude and flight path based on the grid division results; conducting network signal testing using the UAV based on the set flight altitude and flight path to obtain test results; wherein the network signal testing process includes: performing a primary path test based on the flight path to obtain a primary path test result; performing a secondary path test based on the signal fluctuation grids in the primary path test results to obtain a secondary path test result; performing a higher-level path test based on the signal blind zone grids or weak signal zone grids in the secondary path test results to obtain a higher-level path test result; performing data analysis based on the test results to obtain analysis results; and generating network optimization suggestions based on the analysis results. This application provides a low-altitude network signal testing method based on a three-level dynamic path, which can more accurately test signals in low-altitude areas, ensuring more refined testing in grids with poor signals. By using a signal gradient method, the UAV's flight path is dynamically adjusted to avoid weak signal zones and automatically approach strong signal zones, thereby ensuring effective coverage of signal blind zones and weak signal zones. The system can automatically identify blind spots and adjust its flight direction based on real-time signal strength data to obtain more accurate signal data. Through spatial interpolation technology, it generates a continuous signal strength distribution map (i.e., a 3D signal heatmap), accurately displaying the signal distribution across the entire test area. This 3D heatmap not only helps identify strong and weak signal areas but also quantifies the severity of the signal. Based on the aforementioned signal problem severity score, it generates a detailed report through automated algorithms and provides intelligent suggestions for subsequent network optimization based on the severity score. The generated automated report provides network optimization personnel with precise and quantitative decision-making basis, thereby helping to prioritize the resolution of the most severe network coverage issues. Attached Figure Description

[0046] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0047] Figure 1 A flowchart illustrating a low-altitude network signal testing method provided in this application embodiment;

[0048] Figure 2 A schematic diagram illustrating the setting of flight altitude and path for a drone, provided for an embodiment of this application;

[0049] Figure 3 A schematic diagram of a low-altitude network signal testing device provided in an embodiment of this application;

[0050] Figure 4This is a schematic diagram of another low-altitude network signal testing device provided in an embodiment of this application.

[0051] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0052] To enable those skilled in the art to better understand the technical solution of this application, the embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0053] It is understood that the specific embodiments and accompanying drawings described herein are merely for explaining this application and are not intended to limit this application.

[0054] It is understood that, without conflict, the various embodiments and features in the embodiments of this application can be combined with each other.

[0055] It is understood that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, while parts unrelated to this application are not shown in the drawings.

[0056] It is understood that each unit or module involved in the embodiments of this application may correspond to only one entity structure, or may be composed of multiple entity structures, or multiple units or modules may be integrated into one entity structure.

[0057] It is understood that the terms "first," "second," etc., used in the embodiments of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0058] It is understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of this application may occur in a different order than those marked in the accompanying drawings.

[0059] It is understood that the flowcharts and block diagrams of this application illustrate the possible architecture, functions, and operations of systems, apparatuses, devices, and methods according to various embodiments of this application. Each block in a flowchart or block diagram may represent a unit, module, program segment, or code, containing executable instructions for implementing the specified function. Furthermore, each block or combination of blocks in the block diagrams and flowcharts may be implemented using a hardware-based system to implement the specified function, or using a combination of hardware and computer instructions.

[0060] It is understood that the units and modules involved in the embodiments of this application can be implemented by software or by hardware. For example, the units and modules can be located in the processor.

[0061] While existing low-altitude 5G signal testing solutions can collect data using drones and cover a wide area, their shortcomings are also quite obvious.

[0062] First, most existing methods use preset flight paths, which cannot flexibly adjust the path according to changes in the on-site signal, thus affecting the accuracy and completeness of signal data acquisition.

[0063] Secondly, the existing solution does not take into account dynamic environmental factors (such as the impact of obstacles like buildings and trees on the signal), and the test results may have large errors in areas with large signal fluctuations.

[0064] Furthermore, existing solutions lack multi-level data acquisition and redundancy mechanisms, making it impossible to accurately assess the signal quality distribution at different altitudes and in different regions.

[0065] To address the shortcomings of existing technologies, the purpose of this invention is to provide a flexible and precise 5G low-altitude coverage testing method and system. First, the test area is divided, with different testing ranges at different altitudes. Then, based on a three-level dynamic path, signal testing in low-altitude areas can be performed more accurately, ensuring more refined testing even in areas with poor signal. Using a signal gradient method, the drone's flight path is dynamically adjusted to avoid weak signal areas and automatically approach strong signal areas, thus ensuring effective coverage of signal blind spots and weak areas. The system can automatically identify blind spots and adjust its flight direction based on real-time signal strength data to obtain more accurate signal data. Through spatial interpolation technology, a continuous signal strength distribution map (i.e., a 3D signal heatmap) is generated, accurately displaying the signal distribution across the entire test area. This 3D heatmap not only helps identify strong and weak signal areas but also quantifies the severity of the signal. Based on the aforementioned signal problem severity score, a detailed report is generated through automated algorithms, providing intelligent suggestions for subsequent network optimization based on the severity score. The generated automated report provides network optimization personnel with precise and quantitative decision-making basis, helping to prioritize the resolution of the most severe network coverage problems.

[0066] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0067] This application provides a method for testing low-altitude network signals. The working process of this method can be implemented by electronic devices, such as computers, handheld smart terminals, etc. For ease of explanation, the embodiments of this application are described with the computer as the subject of the method execution.

[0068] Figure 1 This is a schematic diagram of the low-altitude network signal testing method provided in the embodiments of this application, as shown below. Figure 1 As shown, this application provides a method for testing low-altitude network signals. The test is implemented based on an unmanned aerial vehicle (UAV). The method includes steps S1-S4, as follows:

[0069] S1. Divide the test area into grids, and set the drone's flight altitude and flight path based on the grid division results;

[0070] Specifically, the first step is to divide the flight area into grids, refining the test area to ensure comprehensive and efficient base station signal testing.

[0071] In this application, the base station signals collected by the drone at each grid node include:

[0072] (1) RSRP (Reference Signal Receiving Power): measures the strength of the signal, in dBm.

[0073] (2) SINR (Signal to Interference plus Noise Ratio): A metric for measuring signal quality, measured in dB.

[0074] (3) Data rate: The data transmission rate of the network, measured in Mbps.

[0075] (4) Delay: The time it takes for data to travel from the sending end to the receiving end, measured in milliseconds.

[0076] To make it easier to understand, the following explanation will use the example of a drone testing the RSRP data of a base station.

[0077] In this step, the testing area (assumed to be a rectangular or circular area) is divided into multiple three-dimensional grids of preset sizes. For example, each three-dimensional grid has a side length of 10 meters (horizontal XY plane) and 10 meters (vertical Z axis). The smaller the spacing between the grids, the higher the test accuracy, which is especially suitable for complex terrain or densely built-up areas.

[0078] Next, the flight altitude and path of the drone are set. Specifically, the flight altitude is set according to the characteristics of the low-altitude environment (such as obstacles such as buildings and trees), and coverage is ensured, and the flight range is determined.

[0079] For example, Figure 2 This is a schematic diagram illustrating the setting of the flight altitude and path of a drone, as provided in an embodiment of this application. Figure 2 As shown, when setting the drone's flight altitude (e.g., 10 meters, 20 meters, 40 meters, and 60 meters), the flight altitude setting depends on the height of ground obstacles, the location of the base station, and actual application requirements. Specific examples are shown below:

[0080] 0-10 meters: Suitable for complex low-altitude environments, with shorter paths, avoiding high obstacles, and usually planned with a radius of 50 meters around the base station.

[0081] 10-20 meters: At this point, the impact of obstacles is reduced, and the path can be slightly expanded, with a planning range of 150-200 meters.

[0082] 20-40 meters: The signal mainly propagates at line of sight, with a path range of 200-500 meters.

[0083] 40-60 meters: At this distance, there are almost no obstacles to interfere with the path, and a path with a radius greater than 500 meters can be set.

[0084] S2. Based on the set flight altitude and flight path, conduct network signal testing using the drone to obtain the test results;

[0085] The network signal testing process includes: performing a primary path test based on the flight path to obtain a primary path test result; performing an intermediate path test based on the signal fluctuation grid in the primary path test result to obtain an intermediate path test result; and performing an advanced path test based on the signal blind zone grid or signal weak zone grid in the intermediate path test result to obtain an advanced path test result.

[0086] In this step, based on signal test feedback from the low-altitude environment, a multi-level dynamic optimization and adjustment method is used to cover the test area in a layered and regional manner, enabling the test process to adapt to signal fluctuations. Specifically, the flight path of the test area is set to be divided into three levels of dynamic paths:

[0087] (1) Primary path: Preset three-dimensional mesh path as the initial test path.

[0088] (2) Intermediate path: Based on the signals of each sampling point tested by the primary path, a local fine path is generated, and a local grid is regenerated in areas with large differences in signal strength to improve the sampling accuracy of these areas.

[0089] (3) Advanced path: If an abnormal signal is detected (such as a large attenuation of RSRP strength), the adaptive model is started to calculate the optimal path.

[0090] The three levels of dynamic paths are explained below:

[0091] In some embodiments, S2, performing a primary path test based on the flight path to obtain primary path test results includes:

[0092] Based on the flight path, network signal testing is conducted using a drone to obtain network signal strength parameters corresponding to each sampling point in each grid area.

[0093] Specifically, the initial path test (macro-level basic coverage) is conducted first to quickly sample base station signals across the entire test area, obtaining preliminary base station signal data distribution at different locations within the area. The specific implementation process is as follows:

[0094] The test area is divided into multiple three-dimensional grids, each with side lengths set to 20 meters (horizontal) and 20 meters (vertical), or other values ​​(adjustable based on actual conditions). The drone will sample signals at each grid node, covering the signal conditions of the entire test area. The smaller the spacing between grids, the higher the test accuracy, especially suitable for complex terrain or densely built-up areas. The initial path flies sequentially along each grid node, forming a coarse scan path to collect base station signal data, such as RSRP data, for the entire area in the shortest possible time. Redundant sampling is performed at each grid node; that is, sampling is conducted at multiple locations within the same grid, with a 3-second interval between each data collection.

[0095] This embodiment conducts a primary path test (macro-level basic coverage). The primary path has a wide coverage area and can quickly identify the base station signal strength, providing data reference for the next step of refined path testing.

[0096] In some embodiments, S2, performing an intermediate path test based on the signal fluctuation grid in the primary path test results to obtain intermediate path test results includes:

[0097] Based on the network signal strength parameters corresponding to each sampling point in each grid region, determine the number of sampling point pairs that are adjacent in position and whose network signal strength difference exceeds a preset threshold in each grid region. If the number reaches the preset threshold, the corresponding grid region is determined to be a signal fluctuation grid.

[0098] The signal fluctuation grid is divided into sub-grids to obtain the signal fluctuation grid. The secondary flight path of the UAV is set according to the secondary grid division result.

[0099] Based on the aforementioned secondary flight path, network signal testing is conducted using a drone to obtain the network signal strength parameters corresponding to each sampling point within each subgrid of the signal fluctuation grid.

[0100] Specifically, during intermediate path testing (local fine-grained sampling), based on the primary path, local fine-grained path testing is performed on grids with significant fluctuations in base station signal data to further improve sampling density and data accuracy. The specific implementation process is as follows:

[0101] (1) Base station signal fluctuation grid identification: Based on the parameters collected in the primary path, the base station signal fluctuation grid nodes are identified. The specific method is as follows:

[0102] Within the grid, the RSRP difference between two adjacent sampling points (i.e., the points closest to themselves) can be expressed by the following formula:

[0103] ΔRSRP ij =|RSRP i -RSRP j |

[0104] Here, RSRPi and RSRPj are the RSRP values ​​collected at positions i and j within the grid, respectively, and ΔRSRPij represents the signal strength difference between the two points. When the proportion of all adjacent sampling points whose signal differences exceed a predetermined threshold (e.g., 3dB or 5dB) reaches a certain percentage, such as 10%, it is marked as a possible fluctuation area.

[0105] (2) To more comprehensively identify fluctuating regions, it is necessary to introduce local signal fluctuation to further analyze signal instability in local areas. The formula for calculating local fluctuation is as follows:

[0106]

[0107] Where Nk is the number of test points in the local area, RSˉRPk is the average RSRP value of all test points in the local area, and LocalFluctuationk measures the degree of signal fluctuation in the area. If this value exceeds a preset fluctuation threshold (e.g., 3dB), the area is considered to have large fluctuations.

[0108] (3) To comprehensively consider signal strength changes and volatility, a Comprehensive Fluctuation Index (CFI) is defined to assess the degree of volatility in a region. The CFI calculation formula is:

[0109] CFI k =α·ΔRSRP avg +β·Local Fluctuation k

[0110] Where ΔRSRPavg is the average signal strength difference between all adjacent points within the local region. LocalFluctuationk is the local volatility, and α and β are weighting coefficients, typically set to α = 0.7 and β = 0.3, adjusted according to the specific environment. In practical applications, if the CFI value exceeds a certain threshold (such as 1.5 or 2), the region is determined to have significant signal fluctuations and is marked as a signal fluctuation region.

[0111] (4) Local path reconstruction: Within the base station signal data fluctuation grid nodes, the grid nodes are further refined, dividing the grid nodes into smaller grids: for example, 5 meters (horizontal) and 5 meters (vertical), forming a denser three-dimensional grid. On the new grid nodes, the UAV performs redundant sampling. That is, sampling is performed at multiple locations within the same grid node, with a 3-second interval between each data acquisition, ensuring that more base station signal data, such as RSRP, is obtained.

[0112] This embodiment performs intermediate path testing (local fine sampling). Intermediate path testing can refine the sampling data in the base station signal fluctuation area, eliminate possible errors, and provide accurate data support for detecting and optimizing the causes of signal fluctuations.

[0113] In some embodiments, S2, the advanced path test is performed based on the signal blind zone grid or signal weak zone grid in the intermediate path test results to obtain advanced path test results, including:

[0114] Based on the network signal strength parameters corresponding to each sampling point in each sub-grid of the signal fluctuation grid, the average signal strength of each sub-grid is obtained. If the average signal strength is lower than a preset threshold, the corresponding sub-grid is determined to be a signal blind zone grid or a signal weak zone grid.

[0115] The optimal flight path is generated based on the signal blind zone grid or the signal weak zone grid.

[0116] Based on the optimal flight path, network signal testing is conducted using a drone to obtain the network signal strength parameters corresponding to each sampling point in the signal blind zone grid or signal weak zone grid.

[0117] Specifically, during advanced path testing (adaptive fine coverage), intelligent path optimization is performed on specific signal blind spots or significantly weak areas to improve signal acquisition accuracy and coverage performance. The specific implementation process is as follows:

[0118] After completing intermediate path sampling, the base station sampling data is analyzed. If the proportion of base station signal average values ​​collected at a certain 5m (horizontal) * 5m (vertical) grid node that are below a certain threshold exceeds a certain percentage, it is defined as a signal dead zone grid or a weak zone grid. For example, if the proportion of sampling points within a grid whose average RSRP is between -110dBm and -120dBm exceeds 20%, then that grid node is marked as a weak zone grid; if the proportion below -120dBm is 30%, then it is a signal dead zone grid. These thresholds can be set as needed.

[0119] In some embodiments, generating an optimal flight path based on the signal blind zone grid or the signal weak zone grid includes: calculating the optimal sampling points and paths within the signal blind zone grid or the signal weak zone grid based on a signal model using an adaptive path algorithm, calculating the signal gradient and the direction of signal change, and generating the optimal flight path.

[0120] For example, the signal gradient at a point P(x0,y0,z0) can be expressed as:

[0121]

[0122] in, The gradient represents the signal strength in space, and the rate of change of the signal strength in each direction represents the rate of change of the signal strength.

[0123] In this embodiment, the UAV should fly along the direction of increasing signal gradient, gradually approaching the area with strong signal, hovering at key locations on the optimal path to collect more data and ensure a comprehensive understanding of the signal quality in blind or weak areas. If the UAV is in an area with weak signal, the optimal flight direction is calculated based on the gradient method to gradually approach the area with stronger signal.

[0124] In this process, the path adjustment formula is as follows, used to calculate the distance adjustment of the UAV flying along the direction of increasing signal gradient:

[0125]

[0126] Where Δd is the path adjustment distance, RSRP current RSRP represents the current signal strength. optimal For the target signal strength (e.g., set to -90dBm), d based This represents the initial flight path adjustment range (e.g., 3 meters). Using the above formula, the drone will adaptively adjust its flight path based on the difference between the current signal strength and the target signal strength.

[0127] In this embodiment, the purpose of gradually approaching a strong signal area within a signal blind or weak area is to maximize the accuracy and efficiency of signal acquisition, while ensuring that the UAV can comprehensively and accurately test the signal coverage in low-altitude areas. The specific functions are as follows:

[0128] (1) Precisely Locating Signal Blind Spots and Weak Spots: Signal blind spots and weak spots typically refer to areas with insufficient signal coverage due to environmental factors (such as buildings, trees, or terrain) or network infrastructure (such as uneven distribution of base stations). In these areas, the signal strength is far below ideal, or there is no signal at all. Gradually approaching areas with strong signals helps testers accurately identify and locate these blind spots or weak spots because, during flight, the drone can gradually move out of the blind spot and towards the area with strong signals. By gradually adjusting its path, the drone can fly in the direction of increasing signal strength, avoiding missing any potentially weak signal areas, thus ensuring that these areas are adequately sampled.

[0129] (2) Optimizing Flight Paths and Resource Utilization: Signal blind spots typically mean that UAVs cannot obtain effective data and may waste time in these areas. By gradually approaching areas with strong signals, UAVs no longer waste too much time and resources in blind spots. Instead, they automatically select the direction in which the signal is most likely to strengthen using the signal gradient method, quickly entering areas with stronger signals. This makes testing more efficient and allows UAVs to focus on collecting valuable data.

[0130] (3) Providing accurate data for subsequent network optimization: Signal blind spots and weak areas are usually key areas for network optimization. Gradually approaching areas with strong signals and collecting detailed data can provide accurate information needed for network planning and optimization. For example, if a region is found to have slow signal recovery or "signal blind spots," the test data can provide direct basis for base station layout optimization, antenna angle adjustment, etc. By accurately testing these weak and blind areas, more targeted and practical reference data can be provided for subsequent 5G network optimization.

[0131] (4) More refined network optimization schemes: By gradually approaching areas with strong signals, the optimal signal strength boundary can be determined more precisely. This not only helps identify weak areas but also provides a reference for subsequent network design and adjustments. For example, if small-scale abnormal fluctuations are found during the gradual strengthening of the signal in a certain area, it means that the area may be affected by some local obstacles, and adjusting the base station or antenna angle may help solve the problem. The strategy of gradually approaching areas with strong signals helps optimize network performance and ensures comprehensive coverage of low-altitude 5G networks.

[0132] In addition, in areas with low signal strength, especially signal blind spots and areas with large signal fluctuations, the drone will hover locally at identified characteristic locations to collect detailed signal data. This includes not only standard RSRP and SINR measurements, but also more detailed network performance parameters such as latency and data rate. The collected data can be transmitted in real time to the ground control station via a wireless communication system for further analysis of the signal characteristics of the area. If the signal quality at the hovering location does not meet expectations (e.g., significant latency), the system will determine whether further adjustments to the flight path or altitude are necessary.

[0133] Through a real-time data transmission system, the ground test terminal receives signal data from the UAV, including data from all hovering points. This data is analyzed in real time, and if signal quality in certain areas remains unsatisfactory (e.g., excessive latency or low speed), the system readjusts the flight path to optimize signal coverage. This process forms a closed loop, ensuring adequate coverage of signal blind spots and weak areas.

[0134] S3. Perform data analysis based on the test results to obtain the analysis results;

[0135] In some embodiments, S3 includes:

[0136] Based on the test results, a three-dimensional heat map of signal intensity distribution is generated using a spatial interpolation algorithm;

[0137] Based on the three-dimensional heat map of signal strength distribution, weak signal areas and signal blind areas are identified.

[0138] In this step, a spatial distribution map of signal strength is generated using real-time acquired signal data. A 3D heatmap is then created using a spatial interpolation algorithm (such as Kriging interpolation), illustrating the three-dimensional distribution of signal strength within the test area. Based on this heatmap, the signal distribution can be further analyzed to identify weak signal areas (regions with relatively weak signals) and dead zones (regions with no signal at all). Finally, based on these analysis results, targeted optimization suggestions are intelligently generated, such as adding base stations and adjusting antenna orientation, to help optimize the coverage quality of the low-altitude 5G network.

[0139] Specifically, since signal testing cannot achieve infinitely dense sampling in every area, there is usually a certain sampling interval. To transform these discrete data points into a continuous signal intensity distribution map, spatial interpolation algorithms (such as Kriging interpolation or inverse distance weighting) are needed for interpolation processing. The collected data is used to generate a 3D signal coverage heatmap using the Kriging interpolation method to visually display the spatial distribution of the signal.

[0140] Kriging interpolation is a statistical method commonly used for geospatial data interpolation, capable of inferring the values ​​of unknown points based on data from known points. Its core idea is based on spatial autocorrelation, meaning that points spatially close to each other are likely to have similar attribute values ​​(such as RSRP). Through Kriging interpolation, all collected signal data points can be smoothly "filled" into a continuous three-dimensional signal distribution map. In three-dimensional space, the Z-axis represents signal strength (e.g., RSRP), while the X and Y axes represent the geographic coordinates of the test area. In 3D heatmap generation based on Kriging interpolation, the goal is to infer the signal distribution of the entire area based on the location of sampling points and signal strength (e.g., RSRP). The core formula for Kriging interpolation is as follows:

[0141]

[0142] Where Z(s) is the signal strength (e.g., RSRP) at location s. i ) is a known position s i The signal strength at that location. λ i It is each known point s i The weighting coefficients estimated for the target point s. n is the number of known data points.

[0143] Kriging interpolation weighting coefficient λ i It is calculated based on spatial autocorrelation. Spatial autocorrelation reflects the trend of signal variation in space; that is, two sampling points that are closer together usually have similar signal strengths. Therefore, the Kriging method calculates these weighting coefficients so that sampling points closer to the target point contribute more to the estimation of that point. The specific formula for calculating the weighting coefficients is as follows:

[0144] C·λ=C(s)

[0145] Where C is the covariance matrix between known sampling points, representing the similarity between different sampling points; λ is the weight coefficient vector; and C(s) is the covariance vector between the target point and known points.

[0146] Using the Kriging interpolation formula described above, the signal strength of the entire test area (including unsampled points) can be calculated, resulting in a continuous 3D signal strength map, or heatmap. The generated heatmap is a three-dimensional signal distribution map, where the X and Y axes represent the spatial coordinates of the test area (e.g., latitude and longitude on the ground), and the Z axis represents the signal strength (e.g., RSRP). The colors in the heatmap are typically used to represent signal strength, with colors transitioning from blue (indicating weak signal) to red (indicating strong signal).

[0147] In this embodiment, Kriging interpolation performs weighted averaging and spatial smoothing on the signal data of the sampling points, ensuring that the signal strength varies continuously within the test area, rather than discretely. This allows the generated heatmap to display the smooth changes in signal strength and the signal intensity of each region, rather than just the measurement value at a single point. The color distribution in the heatmap visually indicates the quality of signal coverage. For example, strong signal areas are displayed as red or yellow regions, while weak signal areas and blind spots are displayed as blue or colorless regions. This information provides important data for network optimization, helping to identify areas with insufficient signal coverage.

[0148] After generating the 3D signal coverage heatmap, the next step is to use algorithms to identify weak signal areas and blind spots, as follows:

[0149] Identification of weak signal areas:

[0150] By setting a signal strength threshold, for example, if the proportion of sampling points with an RSRP between -110dBm and -120dBm at a certain grid node (which may be different in size from the grid node used in the drone test) reaches a certain threshold, such as 30%, the area is considered a weak signal area. All collected signal data is compared with this threshold, and areas below the threshold are automatically marked as weak signal areas. Heatmaps can then be used to see which areas have weak signals; these areas are typically located far from base stations or in areas with many obstacles.

[0151] Blind spot identification:

[0152] A dead zone is an area with absolutely no signal coverage. Based on the collected data, if the proportion of test points with an average RSRP below -120 dBm at a certain grid node exceeds a certain threshold, such as 50%, that area will be marked as a dead zone. In a heatmap, a dead zone will appear as an area with no color fill or an area with very low signal strength.

[0153] S4. Generate network optimization suggestions based on the analysis results.

[0154] In some embodiments, S4 includes:

[0155] Based on the identified weak signal areas and blind signal areas, the severity of regional signal is assessed to obtain a problem severity score for each area;

[0156] Based on the severity score of the problem in each region, corresponding network optimization suggestions are generated.

[0157] In some embodiments, when assessing the severity of regional signal based on identified weak signal areas and dead signal areas, the assessment metrics include:

[0158] The degree of diffusion in weak signal areas;

[0159] Adjacency and connectivity of weak signal regions;

[0160] Signal strength fluctuation;

[0161] Geometric features of the signal problem region.

[0162] The above indicators are all directly proportional to the severity of the problem.

[0163] Specifically, suppose a three-dimensional space is divided into N cubic 3D mesh nodes of equal volume. Some mesh nodes are weak signal areas (blind spots), while others are normal signal areas. A formula for assessing the severity of signal problems is constructed based on diffusion degree, adjacency relationship, connectivity, and signal strength fluctuation, as follows:

[0164] (1) The degree of diffusion in weak signal regions

[0165] First, the extent of signal weakness in a region is quantified. Since signal weakness is often irregularly distributed, its spread directly affects its severity. Assuming the nodes in the signal weakness region form a three-dimensional connected region, the extent of its spread can be assessed through the adjacency relationships of the nodes.

[0166] For a node i in a weak signal region, we calculate its Euclidean distance (dij) to other weak signal region nodes in its neighborhood, i.e., the distance between node i and node j:

[0167]

[0168] Where (xi,yi,zi) and (xj,yj,zj) are the three-dimensional coordinates of nodes i and j, respectively.

[0169] The spread of a weak signal region can be measured by calculating the average distance between nodes in the weak signal region. Assuming the region contains N weak signal nodes, its spread Dspread can be defined as:

[0170]

[0171] The above formula calculates the average distance between all weak signal nodes. The greater the distance, the higher the diffusion, indicating a wider range and greater severity of the signal problem.

[0172] (2) Adjacency and connectivity of weak signal regions

[0173] Analyze the adjacency relationships of weak signal regions, that is, the degree of spatial connectivity between adjacent weak signal region nodes. If the distance between two weak signal region nodes i and j is less than a set threshold dthresh, they are considered to be adjacent, and there is a connecting edge between these two nodes.

[0174] Connectivity metrics are calculated based on the connectivity between these neighboring nodes. Assuming there are Nweak nodes in a weak signal region, the number of edges, Econnect, is the total number of pairs of neighboring nodes, representing the connectivity between nodes within the weak signal region. Connectivity Cconnect can be expressed as:

[0175]

[0176] Econnect is calculated by traversing all pairs of (i,j) nodes in the weak signal region. A connection is considered to exist between two nodes if the distance between them is less than dthresh. This metric reflects the spatial connectivity of nodes within the weak signal region; higher connectivity indicates a larger and more severe signal problem area.

[0177] (3) Signal strength fluctuation

[0178] To assess the volatility of a signal problem, it is necessary to measure the variation in signal strength. For each weak signal node, the standard deviation of its signal strength is calculated to obtain the volatility of the entire weak signal region. Assuming the signal strength of each weak signal node is RSRPi, the volatility σweak within that region is calculated as follows:

[0179]

[0180] Here, RSˉRP represents the average signal strength of all nodes within the weak signal region. Greater fluctuations indicate signal instability in that region, and the more severe the problem, the greater the fluctuation.

[0181] (4) Geometric features of the signal problem region

[0182] The region of a signal problem depends not only on the number of nodes but also on the geometry of that region. To quantify this factor, we calculate the region's volume (Vcluster) and boundary complexity. The volume reflects the extent of the region's coverage in three-dimensional space; a larger volume generally indicates a more widespread signal problem in that region.

[0183] Assuming the spatial extent of the weak signal region is a cube with side length l, then the volume Vcluster is:

[0184] V cluster =l 3

[0185] Boundary complexity can be evaluated using the contact boundaries of nodes. The longer the contact boundary, the higher the degree of connection between the region and the surrounding regions.

[0186] (5) Comprehensive Severity Assessment Formula

[0187] Ultimately, the severity score (Sseverity) of a signaling problem can be comprehensively considered based on diffusion, connectivity, volatility, and geometric characteristics. We define a comprehensive severity assessment formula as follows:

[0188] Sseverity=Dspread*Cconnect*σweak*Vcluster

[0189] in:

[0190] Dspread: The diffusion of a weak signal region, reflecting the scope and extent of a signal problem. The larger the diffusion (Dspread), the wider the impact of the weak signal region and the more severe the problem.

[0191] Cconnect: The connectivity of weak signal regions reflects the spatial density of signal problem areas. The higher the Cconnect connectivity, the closer the connections between multiple weak signal nodes, indicating a large-scale signal problem area and a higher severity.

[0192] σweak: The fluctuation of signal strength, representing the stability of the signal. The larger the fluctuation σweak, the more unstable the signal in that area, the worse the network quality, and the higher the priority.

[0193] Vcluster: The volume of the signal problem area, representing the spatial extent of that area. A larger Vcluster means that the signal problem affects a wider spatial area, making the problem more difficult to solve and requiring higher optimization priority.

[0194] In some embodiments, based on the aforementioned signal problem severity score, a detailed report can be generated using automated algorithms, providing intelligent recommendations for subsequent network optimization based on the severity score. The generated automated report can provide network optimization personnel with precise, quantitative decision-making support, helping them prioritize addressing the most severe network coverage issues.

[0195] Specifically, after collecting all test data and calculating the severity score of each weak or blind zone, the scores of each weak or blind zone can be summarized to form a severity analysis of the entire region.

[0196] Specifically, based on the severity score (Sseverity) for each weak or dead signal area, the entire test area is graded to assess the degree of signal problems in each area. The severity score for each area can be categorized according to the following criteria:

[0197] (1) Mild problem area: The Sseverity is low, indicating that the signal problem is local and has a small impact.

[0198] (2) Moderate problem area: Sseverity is moderate, indicating that the weak signal area is relatively concentrated and has a certain expansion, but it will not affect the entire network.

[0199] (3) Severity area: The higher Sseverity means that the signal problem is widespread and may affect multiple users, resulting in a serious decline in network quality.

[0200] (4) Extremely severe problem area: Sseverity is extremely high, indicating that the signal problem in this area has expanded into a large-scale connected area and requires urgent optimization.

[0201] Based on the severity rating classification above, we can automatically generate an analysis report on the signal problem. The report includes:

[0202] 1. Overview Section:

[0203] Overall description of signal issues: Provide an overall description of the signal coverage in the test area, and briefly explain the network quality and main problems in the test area.

[0204] Regional signal problem distribution overview: Provides the total number of weak signal areas and blind spots, as well as their distribution density within the region.

[0205] 2. Detailed problem analysis:

[0206] Severity rating for each weak or blind zone: List the severity rating (Sseverity) for each weak or blind zone, along with their ranking.

[0207] Spatial distribution of problem areas: Use heat maps or 3D maps to display the spatial distribution of weak signal areas and blind spots, and mark serious problem areas and minor problem areas.

[0208] Regional diffusion, connectivity, and volatility analysis: Displays indicators such as diffusion, connectivity, and volatility for each region to help users understand why signal problems are more severe in certain regions.

[0209] 3. Visualization section:

[0210] 3D heatmap of weak and blind areas: Displays a 3D heatmap of signal coverage, helping users to understand the distribution of signal quality more intuitively.

[0211] Distribution of regional severity scores: The distribution of severity scores for each region is displayed using bar charts, pie charts, and other methods to help users understand the optimization priorities for the entire region.

[0212] 4. Priority Recommendations:

[0213] Identification and priority ranking of critical problem areas: Based on the severity score of each area, the system automatically sorts and displays the areas that most need optimization.

[0214] Impact on User Count Assessment: Estimate the number of affected users in each area by combining user density (such as base station load and user traffic data in the region) to determine the optimization priority.

[0215] In some embodiments, the system can automatically generate network optimization recommendations based on the severity score calculated for each region.

[0216] First, all problem areas are prioritized based on their severity scores. Areas with higher severity scores should be placed in the priority optimization list. By quantifying the severity score of each area, the system can provide the network optimization team with a clear list of optimization priorities.

[0217] For example:

[0218] Severe Problem Area (High Severity Rating): Urgent adjustments are needed, such as base station adjustments, antenna optimization, power enhancement, and the addition of small base stations.

[0219] Moderate problem area (medium score): Fine-tuning is needed in the planning, possibly through signal scheduling and frequency adjustment to optimize coverage.

[0220] Minor issue area (low score): Can be handled during routine maintenance, possibly requiring only periodic checks and minor optimizations.

[0221] Then, based on the severity of each problem area, specific optimization strategies are generated:

[0222] 1. Base station optimization:

[0223] For areas with severe problems, it is recommended to increase the density of base stations or use smaller base stations, and adjust the layout of base stations, especially in areas where signal blind spots or weak signal areas meet.

[0224] For areas with moderate problems, consider adding auxiliary antennas or adjusting the transmission power of existing base stations to cover areas with weak signals.

[0225] 2. Antenna adjustment:

[0226] For areas with uneven signal coverage, it is recommended to adjust the direction of the base station antenna or add a directional antenna, especially in complex urban environments where buildings have a strong shielding effect on signals.

[0227] 3. Frequency optimization:

[0228] Frequency optimization is performed on base stations in areas with weak coverage to allocate spectrum reasonably, reduce interference, and improve signal quality.

[0229] 4. Network load balancing:

[0230] For areas with weak signal and high user density, load balancing should be considered, and congestion should be alleviated by reasonably scheduling bandwidth and allocating network resources, especially in urban hotspot areas.

[0231] 5. Enhanced coverage:

[0232] Within the problem area, consider using directional antennas, wireless backhaul, and other methods to enhance the stability of signal coverage.

[0233] In addition, optimization recommendations should also include real-time monitoring and dynamic adjustment of the network, specifically:

[0234] After optimization, real-time signal quality testing is conducted to evaluate the effectiveness of the optimization measures.

[0235] Continuously monitor the signal status in each area to ensure that the dynamically changing network environment can be continuously optimized.

[0236] Finally, long-term optimization recommendations for certain areas:

[0237] In future network planning, we will consider dynamically adjusting the network layout and resources based on factors such as traffic trends and changes in user needs.

[0238] In this embodiment, the generated automated report and optimization suggestions will include the following:

[0239] (1) Overview: Briefly describe the signal coverage of the test area, the main problems and their severity.

[0240] (2) Heatmap and data: The charts show the weak signal area, blind area and severity score.

[0241] (3) Priority sorting: List the regions that should be optimized first, and provide optimization suggestions for each region.

[0242] (4) Optimization strategy: Provide detailed network optimization solutions for each problem area, including base station adjustment, antenna optimization, load balancing and other strategies.

[0243] (5) Follow-up tracking: Provide a follow-up evaluation and monitoring plan after the optimization is implemented to ensure the optimization effect.

[0244] Ultimately, the results of the entire process, including signal heatmaps, marking of weak and blind spots, and optimization suggestions, will be visualized on the ground testing terminal, forming a comprehensive analysis report. Through the graphical interface, network optimization personnel can intuitively see the current status of signal coverage and specific optimization directions, and make adjustment decisions accordingly.

[0245] This embodiment, based on a severity score for signal problems, generates a comprehensive automated report to help operators accurately identify areas with signal issues and provide targeted optimization recommendations. These recommendations cover multiple aspects, from base station layout and antenna adjustments to frequency optimization and network load balancing, ensuring efficient and accurate network optimization. Furthermore, the report provides real-time monitoring and long-term planning, helping operators dynamically adjust their network architecture to improve user experience and network performance.

[0246] This application provides a low-altitude network signal testing method based on a three-level dynamic path, enabling more precise signal testing in low-altitude areas. This ensures more refined testing even in areas with poor signal. By using a signal gradient method, the method dynamically adjusts the UAV's flight path to avoid weak signal areas and automatically approach strong signal areas, thus ensuring effective coverage of signal blind spots and weak areas. The system can automatically identify blind spots and adjust flight direction based on real-time signal strength data to obtain more accurate signal data. Through spatial interpolation technology, a continuous signal strength distribution map (i.e., a 3D signal heatmap) is generated, accurately displaying the signal distribution across the entire test area. This 3D heatmap not only helps identify strong and weak signal areas but also quantifies signal severity. Based on the aforementioned signal problem severity score, a detailed report is generated through an automated algorithm, providing intelligent suggestions for subsequent network optimization based on the severity score. The generated automated report provides network optimization personnel with precise and quantitative decision-making basis, helping to prioritize the resolution of the most severe network coverage problems.

[0247] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0248] Figure 3 This is a schematic diagram of the low-altitude network signal testing device provided in the embodiments of this application, as shown below. Figure 3 As shown, this application provides a low-altitude network signal testing device, the device comprising:

[0249] The grid division module 11 is configured to divide the test area into grids and set the flight altitude and flight path of the UAV based on the grid division results.

[0250] The signal testing module 12 is configured to conduct network signal testing via the drone based on a set flight altitude and flight path, and obtain the test results.

[0251] The network signal testing process includes:

[0252] A primary path test is performed based on the flight path to obtain the primary path test results.

[0253] Intermediate path testing is performed based on the signal fluctuation grid in the primary path test results to obtain intermediate path test results;

[0254] Advanced path tests are performed based on the signal blind zone grid or signal weak zone grid in the intermediate path test results to obtain advanced path test results.

[0255] Data analysis module 13 is configured to perform data analysis based on the test results and obtain analysis results;

[0256] The optimization suggestion module 14 is configured to generate network optimization suggestions based on the analysis results.

[0257] Regarding the limitations on the low-altitude network signal testing device, please refer to the limitations on the low-altitude network signal testing method in the above embodiments of this application, which will not be repeated here.

[0258] Figure 4 Another schematic diagram of the low-altitude network signal testing device provided in the embodiments of this application is shown below. Figure 4 As shown, in some embodiments, this application provides a low-altitude network signal testing device, including a memory 22 and a processor 21. The memory stores a computer program, and the processor is configured to run the computer program to execute the low-altitude network signal testing method in the above embodiments of this application.

[0259] The memory is connected to the processor. The memory can be flash memory, read-only memory or other types of memory. The processor can be a central processing unit or a microcontroller.

[0260] In some embodiments, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the low-altitude network signal testing method in the above embodiments of this application.

[0261] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules, or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory or other memory technologies, CD-ROM (Compact Disc Read-Only Memory), DVD or other optical disc storage, cartridges, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer.

[0262] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of this application, and this application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this application, and these modifications and improvements are also considered to be within the scope of protection of this application.

Claims

1. A method for testing low-altitude network signals, characterized in that, The test is conducted using a drone, and the method includes: S1. Divide the test area into grids, and set the drone's flight altitude and flight path based on the grid division results; S2. Based on the set flight altitude and flight path, conduct network signal testing using the drone to obtain the test results; The network signal testing process includes: A primary path test is performed based on the flight path to obtain the primary path test results. Intermediate path testing is performed based on the signal fluctuation grid in the primary path test results to obtain intermediate path test results; Advanced path tests are performed based on the signal blind zone grid or signal weak zone grid in the intermediate path test results to obtain advanced path test results. S3. Perform data analysis based on the test results to obtain the analysis results; S4. Generate network optimization suggestions based on the analysis results; In step S2, a primary path test is performed based on the flight path to obtain the primary path test results, including: Based on the flight path, network signal testing is conducted using a drone to obtain network signal strength parameters corresponding to each sampling point in each grid area. In step S2, an intermediate path test is performed based on the signal fluctuation grid in the primary path test results to obtain the intermediate path test results, including: Based on the network signal strength parameters corresponding to each sampling point in each grid region, determine the number of sampling point pairs that are adjacent in position and whose network signal strength difference exceeds a preset threshold in each grid region. If the number reaches the preset threshold, the corresponding grid region is determined to be a signal fluctuation grid. The signal fluctuation grid is divided into sub-grids to obtain the signal fluctuation grid. The secondary flight path of the UAV is set according to the secondary grid division result. Based on the aforementioned secondary flight path, network signal testing is conducted using a drone to obtain the network signal strength parameters corresponding to each sampling point within each subgrid of the signal fluctuation grid.

2. The low-altitude network signal testing method according to claim 1, characterized in that, In S2, a high-level path test is performed based on the signal blind zone grid or signal weak zone grid in the intermediate path test results to obtain high-level path test results, including: Based on the network signal strength parameters corresponding to each sampling point in each sub-grid of the signal fluctuation grid, the average signal strength of each sub-grid is obtained. If the average signal strength is lower than a preset threshold, the corresponding sub-grid is determined to be a signal blind zone grid or a signal weak zone grid. The optimal flight path is generated based on the signal blind zone grid or the signal weak zone grid. Based on the optimal flight path, network signal testing is conducted using a drone to obtain the network signal strength parameters corresponding to each sampling point in the signal blind zone grid or signal weak zone grid.

3. The low-altitude network signal testing method according to claim 2, characterized in that, Based on the aforementioned signal blind zone grid or signal weak zone grid, an optimal flight path is generated, including: The optimal sampling point and path within the signal blind zone grid or signal weak zone grid are calculated based on the signal model using an adaptive path algorithm. The signal gradient and signal change direction are calculated, and the optimal flight path is generated.

4. The low-altitude network signal testing method according to any one of claims 1-3, characterized in that, S3 includes: Based on the test results, a three-dimensional heat map of signal intensity distribution is generated using a spatial interpolation algorithm; Based on the three-dimensional heat map of signal strength distribution, weak signal areas and signal blind areas are identified.

5. The low-altitude network signal testing method according to claim 4, characterized in that, S4 includes: Based on the identified weak signal areas and blind signal areas, the severity of regional signal is assessed to obtain a problem severity score for each area; Based on the severity score of the problem in each region, corresponding network optimization suggestions are generated. The severity assessment indicators include: the degree of diffusion of weak signal areas, the adjacency and connectivity of weak signal areas, the signal strength fluctuation, and the geometric characteristics of the signal problem area.

6. A low-altitude network signal testing device, characterized in that, The device includes: The grid generation module is set to divide the test area into grids and set the drone's flight altitude and flight path based on the grid generation results. The signal testing module is configured to conduct network signal tests using a drone based on a set flight altitude and flight path, and obtain the test results. The network signal testing process includes: A primary path test is performed based on the flight path to obtain the primary path test results. Intermediate path testing is performed based on the signal fluctuation grid in the primary path test results to obtain intermediate path test results; Advanced path tests are performed based on the signal blind zone grid or signal weak zone grid in the intermediate path test results to obtain advanced path test results. The data analysis module is configured to perform data analysis based on the test results and obtain analysis results. The optimization suggestion module is configured to generate network optimization suggestions based on the analysis results. The process of conducting a primary path test based on the flight path to obtain the primary path test results includes: Based on the flight path, network signal testing is conducted using a drone to obtain network signal strength parameters corresponding to each sampling point in each grid area. The intermediate path test is performed based on the signal fluctuation grid in the primary path test results to obtain the intermediate path test results, including: Based on the network signal strength parameters corresponding to each sampling point in each grid region, determine the number of sampling point pairs that are adjacent in position and whose network signal strength difference exceeds a preset threshold in each grid region. If the number reaches the preset threshold, the corresponding grid region is determined to be a signal fluctuation grid. The signal fluctuation grid is divided into sub-grids to obtain the signal fluctuation grid. The secondary flight path of the UAV is set according to the secondary grid division result. Based on the aforementioned secondary flight path, network signal testing is conducted using a drone to obtain the network signal strength parameters corresponding to each sampling point within each subgrid of the signal fluctuation grid.

7. A low-altitude network signal testing device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to implement the low-altitude network signal testing method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the low-altitude network signal testing method as described in any one of claims 1-5.

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