Low-altitude network signal testing method and device and readable storage medium

Abstract

The invention provides a low-altitude network signal test method and device and a readable storage medium, and the method comprises the steps: carrying out the grid division of a test region, and setting the flight height and flight path of an unmanned plane according to a grid division result; based on the set flight height and the flight path, network signal testing is carried out through the unmanned aerial vehicle, and a testing result is obtained; performing data analysis according to the test result to obtain an analysis result; and generating a network optimization suggestion according to the analysis result. According to the method, signal testing can be carried out on a low-altitude area more accurately, effective coverage of a signal blind area and a signal weak area is ensured, identification of a signal strong area and a signal weak area is facilitated, the severity of the signal is quantized, and intelligent suggestions are provided for subsequent network optimization according to severity scores.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a low-altitude network signal testing method, device and readable storage medium. Background Art

[0002] With the rapid development of 5G technology, low-altitude network applications (such as drones) have put forward higher requirements for network coverage quality. However, the current construction and optimization of 5G networks are mainly focused on the coverage of ground users, and the coverage of low-altitude environments has not been fully considered, especially in complex urban environments and areas with dense high-rise buildings. The propagation of low-altitude signals is greatly affected by factors such as buildings and terrain, resulting in serious signal attenuation and even blind spots. This unstable signal quality has affected low-altitude applications such as drone communications based on 5G networks, and there is an urgent need to evaluate and optimize low-altitude 5G network coverage.

[0003] At present, there are some 5G network coverage test solutions that use drones to carry signal test modules to conduct flight tests at specific altitudes and paths. Existing solutions usually use a fixed-radius flight path, with drones flying at a constant speed to collect parameters such as signal strength (RSRP) and signal-to-noise ratio (SNR), and transmit the data to the test terminal to analyze the coverage.

[0004] However, there are still some problems with the existing solutions, which lead to inaccurate and incomplete signal data collection. Summary of the invention

[0005] The technical problem to be solved by the present application is to provide a low-altitude network signal testing method, device and readable storage medium in view of the above-mentioned deficiencies in the prior art, so as to solve the problems existing in the prior art.

[0006] In a first aspect, the present application provides a low-altitude network signal testing method, the test is implemented based on a drone, and the method includes:

[0007] S1. Grid the test area and set the flight altitude and flight path of the drone according to the grid division results;

[0008] S2. Based on the set flight altitude and flight path, the network signal test is performed by the drone to obtain the test result;

[0009] The network signal testing process includes:

[0010] Performing a primary path test based on the flight path to obtain a primary path test result;

[0011] Performing an intermediate path test based on the signal fluctuation grid in the primary path test result to obtain an intermediate path test result;

[0012] Performing advanced path testing based on the signal blind area grid or signal weak area grid in the intermediate path test result to obtain an advanced path test result;

[0013] S3, performing data analysis according to the test results to obtain analysis results;

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

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

[0016] Based on the flight path, a network signal test is performed by the UAV to obtain network signal strength parameters corresponding to each sampling point in each grid area.

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

[0018] According to the network signal strength parameters corresponding to each sampling point in each grid area, the number of sampling point pairs in each grid area whose positions are adjacent and whose network signal strength difference exceeds a preset threshold is determined. If the number reaches the preset threshold, the corresponding grid area is determined to be a signal fluctuation grid;

[0019] Performing secondary grid division on the signal fluctuation grid to obtain subgrids of the signal fluctuation grid, and setting a secondary flight path of the UAV according to the secondary grid division result;

[0020] Based on the secondary flight path, a network signal test is performed by a drone to obtain network signal strength parameters corresponding to each sampling point in each sub-grid in the signal fluctuation grid.

[0021] In some embodiments, in S2, performing an advanced path test based on the signal blind area grid or the signal weak area grid in the intermediate path test result to obtain an advanced path test result includes:

[0022] According to the network signal strength parameters corresponding to each sampling point in each sub-grid in 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 area grid or a signal weak area grid;

[0023] Generate an optimal flight path according to the signal blind area grid or the signal weak area grid;

[0024] Based on the optimal flight path, a network signal test is performed by a drone to obtain network signal strength parameters corresponding to each sampling point in the signal blind area grid or the signal weak area grid.

[0025] In some embodiments, generating an optimal flight path according to the signal blind area grid or the signal weak area grid includes:

[0026] Through the adaptive path algorithm, the optimal sampling points and paths in the signal blind area grid or the signal weak area grid are calculated based on the signal model, the signal gradient and the signal change direction are calculated, and the optimal flight path is generated.

[0027] In some embodiments, S3 includes:

[0028] According to the test results, a three-dimensional distribution heat map of signal intensity is generated by a spatial interpolation algorithm;

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

[0030] In some embodiments, S4 includes:

[0031] Perform regional signal severity assessment based on the identified weak signal areas and signal blind areas to obtain the problem severity score for each area;

[0032] Generate corresponding network optimization suggestions based on the problem severity scores of each area;

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

[0034] In a second aspect, the present application provides a low-altitude network signal testing device, the device comprising:

[0035] A grid division module is configured to divide the test area into grids and set the flight altitude and flight path of the UAV according to the grid division results;

[0036] A signal test module is configured to perform a network signal test through a drone based on a set flight altitude and flight path to obtain a test result;

[0037] The network signal testing process includes:

[0038] Performing a primary path test based on the flight path to obtain a primary path test result;

[0039] Performing an intermediate path test based on the signal fluctuation grid in the primary path test result to obtain an intermediate path test result;

[0040] Performing advanced path testing based on the signal blind area grid or signal weak area grid in the intermediate path test result to obtain an advanced path test result;

[0041] A data analysis module, which is configured to perform data analysis based on the test results to obtain analysis results;

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

[0043] In a third aspect, the present application provides a low-altitude network signal testing device, comprising 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] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the low-altitude network signal testing method described in the first aspect is implemented.

[0045] The present application provides a low-altitude network signal testing method, device and readable storage medium, the test is implemented based on a drone, the method includes: gridding the test area, setting the flight altitude and flight path of the drone according to the grid division result; based on the set flight altitude and flight path, performing a network signal test through the drone to obtain a test result; 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 an intermediate path test based on the signal fluctuation grid in the primary path test result to obtain an intermediate path test result; performing an advanced path test based on the signal blind area grid or signal weak area grid in the intermediate path test result to obtain an advanced path test result; performing data analysis based on the test result to obtain an analysis result; and generating a network optimization suggestion based on the analysis result. The present application provides a low-altitude network signal testing method, which can perform signal testing on a low-altitude area more accurately based on a three-level dynamic path, ensure that a more refined test can be performed on a grid with a poor signal, and dynamically adjust the flight path of the drone through a signal gradient method so that it can avoid a weak signal area and automatically approach a strong signal area, thereby ensuring effective coverage of a signal blind area and a weak area. The system can automatically identify blind spots and adjust the 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., 3D signal heat map) is generated to accurately display the signal distribution of the entire test area. This 3D heat map not only helps to identify areas with strong and weak signals, but also quantifies the severity of the signal. According to the severity score of the signal problem mentioned above, a detailed report is generated through an automated algorithm, and intelligent suggestions are provided for subsequent network optimization based on the severity score. The generated automated report can provide network optimizers with accurate and quantitative decision-making basis, thereby helping to prioritize the most serious network coverage problems. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0047] Figure 1 A flowchart of a low-altitude network signal testing method provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of setting the flight altitude and path of a drone provided in an embodiment of the present application;

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

[0050] Figure 4A schematic diagram of the structure of another low-altitude network signal testing device provided in an embodiment of the present application.

[0051] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the technical solution of the present application, the implementation mode of the present application will be further described in detail below with reference to the accompanying drawings.

[0053] It should be understood that the specific embodiments and drawings described herein are only used to explain the present application, rather than to limit the present application.

[0054] It can be understood that, in the absence of conflict, the various embodiments in the present application and the various features in the embodiments can be combined with each other.

[0055] It can be understood that, for the convenience of description, the drawings of the present application only show the parts related to the present application, while the parts unrelated to the present application are not shown in the drawings.

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

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

[0058] It is understandable that, in the absence of conflict, the functions and steps marked in the flowcharts and block diagrams of the present application may occur in an order different from that marked in the drawings.

[0059] It is understood that the flowcharts and block diagrams of the present application illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to the various embodiments of the present application. Among them, each box in the flowchart or block diagram may represent a unit, module, program segment, code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented by a hardware-based system that implements the specified functions, or by a combination of hardware and computer instructions.

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

[0061] Although existing low-altitude 5G signal testing solutions can collect data through drones and cover a large area, their shortcomings are also very obvious.

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

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

[0064] Thirdly, the existing schemes lack multi-level data collection and redundancy mechanisms and cannot accurately evaluate the signal quality distribution at different altitude levels and areas.

[0065] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a flexible and fine 5G low-altitude coverage test method and system. First, the test area is divided, and the range of different height tests is different. Then, based on the three-level dynamic path, the signal test can be more accurately performed on the low-altitude area, ensuring that more refined tests can be performed in grids with poor signals. Through the signal gradient method, the flight path of the drone is dynamically adjusted so that it can avoid weak signal areas and automatically approach strong signal areas, thereby ensuring effective coverage of signal blind areas and weak areas. The system can automatically identify blind areas and adjust the 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 heat map) is generated to accurately display the signal distribution of the entire test area. This 3D heat map not only helps to identify strong and weak signal areas, but also quantifies the severity of the signal. According to the aforementioned signal problem severity score, a detailed report is generated through an automated algorithm, and intelligent suggestions are provided for subsequent network optimization based on the severity score. The generated automated report can provide network optimizers with accurate and quantitative decision-making basis, thereby helping to prioritize the most serious network coverage problems.

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

[0067] The present application provides a low-altitude network signal testing method, the working process of which can be implemented by electronic devices, such as computers, handheld smart terminals, etc. For ease of explanation, the present application describes the method execution subject as a computer in each embodiment.

[0068] Figure 1 A schematic diagram of a low-altitude network signal testing method provided in an embodiment of the present application, such as Figure 1 As shown, the present application provides a low-altitude network signal testing method, the test is implemented based on a drone, and the method includes S1-S4, which are as follows:

[0069] S1. Grid the test area and set the flight altitude and flight path of the drone according to the grid division results;

[0070] Specifically, the flight area is first gridded and the test area is finely divided to ensure that the base station signal test is comprehensive and efficient.

[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 signal strength in dBm.

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

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

[0075] (4) Latency: The delay from the sender to the receiver, measured in milliseconds.

[0076] For ease of understanding, the following is an example of a drone testing the RSRP data of a base station.

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

[0078] Then, 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 the coverage is ensured and the flight range is determined.

[0079] For example, Figure 2 A schematic diagram of setting the flight altitude and path of a drone provided in an embodiment of the present application is shown in FIG. Figure 2 As shown in the figure, when setting the flight altitude of the drone (for example, 10 meters, 20 meters, 40 meters, and 60 meters), the flight altitude is set according to the height of ground obstacles, the location of the base station, and actual application requirements. The following is a specific example:

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

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

[0082] 20-40 meters: The signal is mainly transmitted in line of sight, and the path range is 200-500 meters.

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

[0084] S2. Based on the set flight altitude and flight path, the network signal test is performed by the drone to obtain the test result;

[0085] The network signal test 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 a signal fluctuation grid in the primary path test result to obtain an intermediate path test result; performing an advanced path test based on a signal blind area grid or a signal weak area grid in the intermediate path test result to obtain an advanced path test result;

[0086] In this step, based on the signal test feedback of 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, so that the test process can adapt to signal fluctuations. Specifically, the flight path of the test area is divided into three levels of dynamic paths:

[0087] (1) Primary path: A preset three-dimensional grid path is used as the initial test path.

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

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

[0090] The following explains the three-level dynamic paths respectively:

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

[0092] Based on the flight path, a network signal test is performed by the UAV to obtain network signal strength parameters corresponding to each sampling point in each grid area.

[0093] Specifically, we first conduct a primary path test (macro-basic coverage), quickly complete the base station signal sampling in the entire test area, and obtain the preliminary base station signal data distribution at different locations in the area. The specific implementation process is as follows:

[0094] The test area is divided into multiple three-dimensional grids, and the side length of each grid is set to 20 meters (horizontally) and 20 meters (vertically), or other values ​​(specific values ​​can be adjusted according to actual conditions). The drone will perform test sampling at each grid node to cover the signal conditions of the entire test area. The smaller the spacing between the grids, the higher the accuracy of the test, especially suitable for complex terrain or densely built areas. The initial path flies along each grid node in turn to form a rough scanning path, and collects base station signal data of the entire area in the shortest time, such as RSRP data, and performs redundant sampling at each grid node. That is, sampling is performed at multiple locations in the same grid, with an interval of 3 seconds between each data collection.

[0095] This embodiment performs a primary path test (macro 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 path refinement.

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

[0097] According to the network signal strength parameters corresponding to each sampling point in each grid area, the number of sampling point pairs in each grid area whose positions are adjacent and whose network signal strength difference exceeds a preset threshold is determined. If the number reaches the preset threshold, the corresponding grid area is determined to be a signal fluctuation grid;

[0098] Performing secondary grid division on the signal fluctuation grid to obtain subgrids of the signal fluctuation grid, and setting a secondary flight path of the UAV according to the secondary grid division result;

[0099] Based on the secondary flight path, a network signal test is performed by a drone to obtain network signal strength parameters corresponding to each sampling point in each sub-grid in the signal fluctuation grid.

[0100] Specifically, when conducting intermediate path testing (local fine sampling), based on the primary path, local fine path testing is performed on grids with large 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 by the primary path, the base station signal fluctuation grid nodes are identified. The specific method is as follows:

[0102] In the grid, assuming that 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] Among them, RSRPi and RSRPj are the RSRP values ​​collected at position i and position j in the grid, respectively, and ΔRSRPij represents the signal strength difference between the two points. When the proportion of signal differences of all adjacent sampling points exceeding a predetermined threshold (such as 3dB or 5dB) reaches a certain proportion, such as 10%, it is marked as a possible fluctuation area.

[0105] (2) In order to more comprehensively identify the fluctuation area, it is necessary to introduce the local signal fluctuation (Local Signal Fluctuation) to further analyze the signal instability in the local area. The calculation formula of local fluctuation is as follows:

[0106]

[0107] Among them, 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 the value exceeds the preset fluctuation threshold (such as 3dB), the area is considered to have large fluctuations.

[0108] (3) In order to comprehensively consider the signal strength change and fluctuation, a comprehensive fluctuation index (CFI) is defined to evaluate the fluctuation degree of the region. The CFI calculation formula is:

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

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

[0111] (4) Local path reconstruction: Within the grid node where the base station signal data fluctuates, the grid node is refined twice, and the grid nodes are divided into smaller grids: for example, 5 meters (horizontally) and 5 meters (vertically), forming a denser three-dimensional grid. On the new grid node, the drone performs redundant sampling. That is, sampling is performed at multiple locations within the same grid node, with an interval of 3 seconds between each data collection to ensure that more base station signal data, such as RSRP, is obtained.

[0112] This embodiment performs intermediate path testing (local fine sampling), and the intermediate path 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, in S2, performing an advanced path test based on the signal blind area grid or the signal weak area grid in the intermediate path test result to obtain an advanced path test result includes:

[0114] According to the network signal strength parameters corresponding to each sampling point in each sub-grid in 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 area grid or a signal weak area grid;

[0115] Generate an optimal flight path according to the signal blind area grid or the signal weak area grid;

[0116] Based on the optimal flight path, a network signal test is performed by a drone to obtain network signal strength parameters corresponding to each sampling point in the signal blind area grid or the signal weak area grid.

[0117] Specifically, when conducting advanced path testing (adaptive fine coverage), intelligent path optimization is performed on specific signal blind areas or obvious weak areas to improve the accuracy of signal acquisition and coverage effect. The specific implementation process is as follows:

[0118] After completing the intermediate path sampling, analyze the sampling data of the base station. If the average value of the base station signal collected at a certain 5m (horizontal) * 5m (vertical) grid node is lower than the threshold for more than a certain proportion, it is defined as a signal blind area grid or a weak area grid. For example, if the RSRP average value of the sampling points in the grid is between -110dBm and -120dBm for more than 20%, the grid node is marked as a signal weak area grid. If the proportion is less than -120dbm for 30%, it is a signal blind area grid. These thresholds can be set as needed.

[0119] In some embodiments, an optimal flight path is generated according to the signal blind spot grid or the signal weak spot grid, including: calculating the optimal sampling points and paths within the signal blind spot grid or the signal weak spot grid based on the signal model through 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, It represents the gradient of signal strength in space and the rate of change of signal strength in each direction.

[0123] In this embodiment, the drone should fly in the direction of increasing signal gradient, gradually approach the signal strong area, hover at the key position of the optimal path, collect more data, and ensure a comprehensive understanding of the signal quality in the blind or weak area. If the drone is in an area with weak signal, the optimal flight direction is calculated according to the gradient method to gradually approach the area with strong signal.

[0124] During this process, the path adjustment formula is as follows, which is used to calculate the distance adjustment of the drone flying in the direction of increasing signal gradient:

[0125]

[0126] Among them, Δd is the adjustment distance of the path, RSRP current is the current signal strength, RSRP optimal is the target signal strength (for example, set to -90dBm), d based is the initial flight path adjustment amplitude (e.g. 3 meters). Through the above formula, the drone will adaptively adjust the flight path according to the difference between the current signal strength and the target signal strength.

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

[0128] (1) Accurately locate signal blind spots and weak areas: Signal blind spots and weak areas usually 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 the ideal value, or there is no signal at all. Gradually approaching the signal strong area can help testers accurately identify and locate these blind spots or weak areas, because during the flight, the drone can gradually move out of the blind spot and move towards the signal strong area. By gradually adjusting the path, the drone can fly in the direction of increasing signal strength to avoid missing any potential weak signal areas, thereby ensuring that these areas are fully sampled.

[0129] (2) Optimizing flight paths and resource utilization: Signal blind spots usually mean that the drone cannot obtain valid data and may waste time in these areas. By gradually approaching areas with strong signals, the drone no longer wastes too much time and resources in blind spots. Instead, it automatically selects the direction in which the signal is most likely to be enhanced through the signal gradient method and quickly enters the area with strong signals. In this way, the test work is more efficient and can focus on collecting valuable data.

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

[0131] (4) More sophisticated network optimization scheme: By gradually approaching the signal strong area, 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 adjustment. For example, if a small range of abnormal fluctuations are found in the process of gradually strengthening the signal in a certain area, it means that the area may be affected by some local obstacles. Adjusting the base station or antenna angle may help solve the problem. The strategy of gradually approaching the signal strong area helps optimize network performance and ensure all-round coverage of the low-altitude 5G network.

[0132] In addition, in areas with low signal strength, especially in signal blind spots and areas with large signal fluctuations, the drone will hover locally at the identified feature locations to collect detailed signal data. This includes not only conventional RSRP and SINR, but also more detailed network performance parameters such as delay and data rate. The collected data can be transmitted to the ground control end in real time through the wireless communication system for further analysis of the signal characteristics of the area. If the signal quality at the hovering position does not meet expectations (for example, large delay, etc.), the system will determine whether further adjustments to the flight trajectory or altitude are needed.

[0133] Through the real-time data transmission system, the ground test terminal will receive signal data from the drone, including data from all hovering points. These data are analyzed in real time, and if it is detected that the signal quality in certain areas is still not ideal (for example, the delay is too large or the rate is too low), the system will adjust the flight path again to optimize signal coverage. This process forms a closed loop to ensure that signal blind spots and weak areas can be fully covered.

[0134] S3, performing data analysis according to the test results to obtain analysis results;

[0135] In some embodiments, S3 includes:

[0136] According to the test results, a three-dimensional distribution heat map of signal intensity is generated by a spatial interpolation algorithm;

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

[0138] In this step, a spatial distribution map of the signal strength is generated through real-time collected signal data, and a spatial interpolation algorithm (such as Kriging interpolation) is used to generate a 3D heat map, which shows the three-dimensional distribution of signal strength in the test area. Then, based on this heat map, the distribution of the signal can be further analyzed to identify weak signal areas (areas with weak signals) and blind areas (areas with no signals at all). Finally, based on these analysis results, targeted optimization suggestions are intelligently generated, such as adding base stations and adjusting antenna directions, to help optimize the coverage quality of low-altitude 5G networks.

[0139] Specifically, since signal testing cannot achieve infinitely dense sampling in every area, there is usually a certain sampling interval. In order to convert these discrete data points into a continuous signal strength distribution map, a spatial interpolation algorithm (such as Kriging interpolation or inverse distance weighted method) is required for interpolation processing. The collected data is used to generate a 3D signal coverage heat map through the Kriging interpolation method to intuitively display the spatial distribution of the signal.

[0140] Kriging interpolation is a statistical method commonly used for geospatial data interpolation, which can infer the values ​​of unknown points based on the data of known points. Its core idea is based on spatial autocorrelation, that is, points that are close to each other in space may have more 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 the signal strength (such as RSRP), and the X and Y axes represent the geographic coordinates of the test area. In the generation of 3D heat maps based on Kriging interpolation, the goal is to infer the signal distribution of the entire area based on the location and signal strength (such as RSRP) of the sampling points. The core formula of Kriging interpolation is as follows:

[0141]

[0142] Where Z(s) is the signal strength (e.g. RSRP) at location s. i ) is the known position s i The signal strength at λ i is each known point s i The weight coefficient for estimating the target point s. n is the number of known data points.

[0143] Kriging interpolation weight coefficient λ i It is calculated based on spatial autocorrelation. Spatial autocorrelation reflects the changing trend of the signal in space, that is, the signal strengths of two sampling points that are closer to each other are usually more similar. Therefore, the Kriging method calculates these weight coefficients so that the sampling points closer to the target point contribute more to the estimation of the point. The specific weight coefficient calculation formula is:

[0144] C·λ=C(s)

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

[0146] Through the above Kriging interpolation formula, the signal strength of the entire test area (including unsampled points) can be calculated, thereby obtaining a continuous 3D signal strength map, namely a heat map. The generated heat map is a signal distribution map in a three-dimensional space, where the X-axis and Y-axis represent the spatial coordinates of the test area (for example, longitude and latitude on the ground), and the Z-axis represents the signal strength (such as RSRP). The color of the heat map is usually used to indicate the strength of the signal, and the color gradually changes 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 so that the signal strength changes continuously in the test area instead of discretely. This enables the generated heat map to show the smooth change of the signal and the signal strength of each area, rather than just the measured value of a certain point. The color distribution in the heat map can intuitively show the quality of signal coverage. For example, areas with strong signals will appear as red or yellow areas, while areas with weak signals and blind spots will appear as blue or colorless areas. This information provides an important basis for network optimization and helps identify areas with insufficient signal coverage.

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

[0149] Identification of weak signal areas:

[0150] By setting a signal strength threshold, for example, when the proportion of RSRP between -110dBm and -120dbm at a sampling point of a certain grid node (the grid node can be different in size from the grid node tested by the drone) reaches a certain threshold, such as 30%, the area is considered a weak signal area. All collected signal data will be compared with the threshold, and areas below the threshold will be automatically marked as weak signal areas. Through the heat map, you can see which areas have weak signals, which are usually located in areas far away from the base station or where there are many obstacles.

[0151] Identification of blind spots:

[0152] A blind area is an area with no signal coverage at all. Based on the collected data, if the percentage of the average RSRP of a certain grid node below -120dbm exceeds a certain threshold, such as 50%, the area will be marked as a blind area. In the heat map, a blind area will appear as an area with no color fill at all, 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] Perform regional signal severity assessment based on the identified weak signal areas and signal blind areas to obtain the problem severity score for each area;

[0156] Generate corresponding network optimization suggestions based on the problem severity scores of the respective areas.

[0157] In some embodiments, when evaluating the severity of regional signals based on the determined weak signal areas and blind signal areas, the evaluation indicators include:

[0158] The spread of weak signal areas;

[0159] Adjacency and connectivity of weak signal areas;

[0160] Signal strength fluctuation;

[0161] The geometric characteristics of the signal problem area.

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

[0163] Specifically, suppose a three-dimensional space is divided into N cube-shaped three-dimensional grid nodes of the same volume. Some grid nodes are signal weak area (blind area) nodes, and some are signal normal grid nodes. The signal problem severity assessment formula is constructed by diffusion degree, adjacency, connectivity, and signal strength fluctuation, as follows:

[0164] (1) Diffusion of weak signal areas

[0165] First, quantify the diffusion degree of the weak signal area. Since the weak signal area is often irregularly distributed, its diffusion directly affects its severity. Assuming that the nodes in the weak signal area form a three-dimensional connected area, the diffusion degree of the area can be evaluated by the adjacency relationship of the nodes.

[0166] For a node i in a weak signal area, we calculate the Euclidean distance (dij) between it and other weak signal area nodes in its neighborhood, that is, the distance between node i and node j:

[0167]

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

[0169] The diffusion of the weak signal area can be measured by calculating the average distance between the nodes in the weak signal area. Assuming that the area contains Nweak weak signal area nodes, its diffusion Dspread can be defined as:

[0170]

[0171] The above formula calculates the average distance between all nodes in the weak signal area. The larger the distance, the higher the diffusion, indicating that the signal problem is widespread and more serious.

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

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

[0174] The connectivity metric is calculated based on the connectivity of these adjacent nodes. Assuming that there are Nweak nodes in the weak signal area, the number of edges Econnect is the number of all adjacent node pairs, indicating the connectivity between nodes in the weak signal area. The connectivity Cconnect can be expressed as:

[0175]

[0176] Among them, Econnect is calculated by traversing all pairs of nodes (i, j) in weak signal areas. If the distance between two nodes is less than dthresh, they are considered to be connected. This metric reflects the spatial connectivity of nodes inside the weak signal area. The higher the connectivity, the larger the scale of the signal problem area and the higher the severity.

[0177] (3) Signal strength fluctuation

[0178] In order to evaluate the volatility of signal problems, it is necessary to measure the change in signal strength. For each node in a weak signal area, calculate the standard deviation of its signal strength to obtain the volatility of the entire weak area. Assuming that the signal strength of each node in a weak signal area is RSRPi, calculate the volatility σweak in this area:

[0179]

[0180] Among them, RSˉRP is the average signal strength of all nodes in the weak signal area. The greater the fluctuation, the more unstable the signal in the area, and the greater the fluctuation, the more serious the problem.

[0181] (4) Geometric characteristics of signal problem areas

[0182] The area of ​​signal problems depends not only on the number of nodes, but also on the geometry of the area. To quantify this factor, we calculate the volume (Vcluster) and boundary complexity of the area. The volume reflects the coverage of the area in three-dimensional space. A larger volume usually means that the signal problem in the area is more extensive.

[0183] Assuming that the spatial range of the weak signal area is a cube with a side length of l, the volume Vcluster is:

[0184] V cluster = l 3

[0185] Boundary complexity can be evaluated by the contact boundaries of nodes. The longer the contact boundaries, the higher the degree of connection between the area and the surrounding areas.

[0186] (5) Comprehensive severity assessment formula

[0187] Finally, the severity score of the signal problem Sseverity can comprehensively consider 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 spread of the weak signal area reflects the scope and scalability of the signal problem. The larger the spread Dspread, the wider the impact of the weak signal area and the more serious the problem.

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

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

[0193] Vcluster: The volume of the signal problem area, indicating the spatial range of the area. The larger the volume Vcluster, the wider the spatial range affected by the signal problem, and the higher the difficulty of solving the problem and the priority of optimization.

[0194] In some embodiments, according to the aforementioned signal problem severity score (Sseverity), a detailed report can be generated through an automated algorithm, and intelligent suggestions can be provided for subsequent network optimization based on the severity score. The generated automated report can provide network optimizers with accurate and quantitative decision-making basis, thereby helping them to prioritize solving the most serious network coverage problems.

[0195] Specifically, after all test data are collected and the severity score of each weak signal area or blind area is calculated, the scores of each weak area or blind area can be summarized to form a severity analysis of the entire area.

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

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

[0198] (2) Moderate problem area: The Sseverity is relatively medium, indicating that the weak signal areas in this area are relatively concentrated and have a certain extension, but will not affect the entire network.

[0199] (3) Severe problem area: A high Sseverity means that the signal problem is widespread and may affect multiple users, seriously degrading the network quality.

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

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

[0202] 1. Overview:

[0203] Overall description of signal issues: Give an overall description of the signal coverage in the test area, briefly explaining the network quality and major issues in the test area.

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

[0205] 2. Detailed problem analysis:

[0206] Severity score of each weak area or blind area: Lists the severity score Sseverity of each weak area or blind area, and their ranking.

[0207] Spatial distribution of problem areas: Use heat maps or 3D maps to show the spatial distribution of weak signal areas and blind areas, marking 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 serious in certain areas.

[0209] 3. Visualization part:

[0210] 3D heat map of weak signal areas and blind spots: Displays a 3D heat map of signal coverage to help users understand the signal quality distribution more intuitively.

[0211] Distribution of regional severity scores: Displays the distribution of severity scores for each region through bar charts, pie charts, etc., to help users understand the optimization priority of the entire region.

[0212] 4. Priority recommendations:

[0213] Identification and prioritization of critical problem areas: Automatically sort each area based on its severity score to display the areas most in need of optimization.

[0214] Assessment of the number of affected users: The number of affected users in each area is estimated based on user density (such as base station load and user traffic data in the area) to determine the optimization priority.

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

[0216] First, all problem areas are prioritized according to 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 optimization priority list.

[0217] For example:

[0218] Serious problem area (high Sseverity score): urgent need for base station adjustment, antenna optimization, power enhancement, addition of small base stations, etc.

[0219] Moderate problem area (medium score): fine-tuning is required in planning, and coverage may be optimized through signal scheduling and frequency adjustment.

[0220] Mild problem areas (low score): can be handled during routine maintenance and may only require periodic inspections and small-scale optimization.

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

[0222] 1. Base station optimization:

[0223] For serious problem areas, it is recommended to increase base station density or small base stations, and adjust the base station layout, especially in the intersection areas of signal blind spots or weak signal areas.

[0224] For moderate problem areas, consider adding auxiliary antennas or adjusting the transmit 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 directional antennas, especially in complex urban environments where buildings have a strong shielding effect on signals.

[0227] 3. Frequency optimization:

[0228] Perform frequency optimization on base stations in areas with weak coverage, allocate spectrum reasonably, reduce interference and improve signal quality.

[0229] 4. Network Load Balancing:

[0230] For signal-weak areas with high user density, consider load balancing and alleviate congestion by reasonably scheduling bandwidth and network resource allocation, especially in urban hot spots.

[0231] 5. Coverage enhancement:

[0232] Within the problem area, consider using directional antennas, wireless backhaul and other means 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, a real-time signal quality test is performed to evaluate the effectiveness of the optimization measures.

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

[0236] Finally, some long-term optimization suggestions 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 demand.

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

[0239] (1) Overview: A brief description of the signal coverage, main issues and severity analysis of the test area.

[0240] (2) Heat map and data: Displays weak signal areas, blind areas, and severity scores through charts.

[0241] (3) Prioritization: List the priority optimization areas and provide optimization suggestions for each area.

[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 follow-up evaluation and monitoring plans after optimization implementation to ensure the optimization effect.

[0244] Finally, the results of the entire process, including signal heat maps, markings of weak and blind areas, and optimization suggestions, will be visualized on the ground test terminal to form a comprehensive analysis report. Through the graphical interface, network optimizers can intuitively see the current status of signal coverage and specific optimization directions, and make adjustment decisions accordingly.

[0245] Based on the severity score of the signal problem, this embodiment can generate a comprehensive automated report to help operators accurately identify signal problem areas and provide targeted optimization suggestions. These suggestions cover multiple aspects from base station layout, antenna adjustment, frequency optimization to network load balancing, ensuring that network optimization work can be carried out efficiently and accurately. In addition, the report can also provide real-time monitoring and long-term planning to help operators dynamically adjust the network architecture and improve user experience and network performance.

[0246] The present application provides a low-altitude network signal testing method, which can perform signal testing on low-altitude areas more accurately based on a three-level dynamic path, ensuring that more refined testing can be performed in grids with poor signals. The flight path of the drone is dynamically adjusted through the signal gradient method, so that it can avoid weak signal areas and automatically approach strong signal areas, thereby ensuring effective coverage of signal blind areas and weak areas. The system can automatically identify blind areas and adjust the 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 heat map) is generated to accurately display the signal distribution of the entire test area. This 3D heat map not only helps identify strong and weak signal areas, but also quantifies the severity of the signal. According to the aforementioned signal problem severity score, a detailed report is generated through an automated algorithm, and intelligent suggestions are provided for subsequent network optimization based on the severity score. The generated automated report can provide network optimizers with accurate and quantitative decision-making basis, thereby helping to prioritize the most serious network coverage problems.

[0247] It should be understood that, although the various steps in the flowcharts in the above-described embodiments are sequentially displayed according to the indications of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and they can be executed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily to be carried out sequentially, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0248] Figure 3 A schematic diagram of a low-altitude network signal testing device provided in an embodiment of the present application, such as Figure 3 As shown, the present application provides a low-altitude network signal testing device, the device comprising:

[0249] A grid division module 11 is configured to divide the test area into grids and set the flight altitude and flight path of the UAV according to the grid division results;

[0250] A signal testing module 12 is configured to perform a network signal test through the drone based on a set flight altitude and flight path to obtain a test result;

[0251] The network signal testing process includes:

[0252] Performing a primary path test based on the flight path to obtain a primary path test result;

[0253] Performing an intermediate path test based on the signal fluctuation grid in the primary path test result to obtain an intermediate path test result;

[0254] Performing advanced path testing based on the signal blind area grid or signal weak area grid in the intermediate path test result to obtain an advanced path test result;

[0255] A data analysis module 13, which is configured to perform data analysis according to the test results to obtain analysis results;

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

[0257] Regarding the limitation of the low-altitude network signal testing device, reference may be made to the limitation of the low-altitude network signal testing method in the above-mentioned embodiments of the present application, and this embodiment will not be repeated here.

[0258] Figure 4 Another schematic diagram of the low-altitude network signal testing device provided in the embodiment of the present application is as follows Figure 4 As shown, in some embodiments, the present 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-mentioned embodiments of the present application.

[0259] The memory is connected to the processor, the memory may be a flash memory or a read-only memory or other memory, and the processor may be a central processing unit or a single-chip microcomputer.

[0260] In some embodiments, the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the low-altitude network signal testing method in the above-mentioned embodiments of the present application is implemented.

[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 technology, CD-ROM (Compact Disc Read-Only Memory), digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0262] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present application, but the present 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 the present application, and these modifications and improvements are also considered to be within the scope of protection of the present application.

Claims

1. A low-altitude network signal testing method, characterized in that: The test is implemented based on a drone, and the method includes: S1. Grid the test area and set the flight altitude and flight path of the drone according to the grid division results; S2. Based on the set flight altitude and flight path, the network signal test is performed by the drone to obtain the test result; 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; Performing advanced path testing based on the signal blind area grid or signal weak area grid in the intermediate path test result to obtain an advanced path test result; S3, performing data analysis according to the test results to obtain analysis results; S4. Generate network optimization suggestions based on the analysis results.

2. The low-altitude network signal testing method according to claim 1, characterized in that: In S2, a primary path test is performed based on the flight path to obtain a primary path test result, including: Based on the flight path, a network signal test is performed by the UAV to obtain network signal strength parameters corresponding to each sampling point in each grid area.

3. The low-altitude network signal testing method according to claim 2, characterized in that: In S2, performing an intermediate path test based on the signal fluctuation grid in the primary path test result to obtain an intermediate path test result includes: According to the network signal strength parameters corresponding to each sampling point in each grid area, the number of sampling point pairs in each grid area whose positions are adjacent and whose network signal strength difference exceeds a preset threshold is determined. If the number reaches the preset threshold, the corresponding grid area is determined to be a signal fluctuation grid; Performing secondary grid division on the signal fluctuation grid to obtain subgrids of the signal fluctuation grid, and setting a secondary flight path of the UAV according to the secondary grid division result; Based on the secondary flight path, a network signal test is performed by a drone to obtain network signal strength parameters corresponding to each sampling point in each sub-grid in the signal fluctuation grid.

4. The low-altitude network signal testing method according to claim 3 is characterized in that: In S2, performing an advanced path test based on the signal blind area grid or the signal weak area grid in the intermediate path test result to obtain an advanced path test result includes: According to the network signal strength parameters corresponding to each sampling point in each sub-grid in 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 area grid or a signal weak area grid; Generate an optimal flight path according to the signal blind area grid or the signal weak area grid; Based on the optimal flight path, a network signal test is performed by a drone to obtain network signal strength parameters corresponding to each sampling point in the signal blind area grid or the signal weak area grid.

5. The low-altitude network signal testing method according to claim 4, characterized in that: Generating an optimal flight path according to the signal blind area grid or the signal weak area grid includes: Through the adaptive path algorithm, the optimal sampling points and paths in the signal blind area grid or the signal weak area grid are calculated based on the signal model, the signal gradient and the signal change direction are calculated, and the optimal flight path is generated.

6. The low-altitude network signal testing method according to any one of claims 1 to 5, characterized in that: S3, including: According to the test results, a three-dimensional distribution heat map of signal intensity is generated by a spatial interpolation algorithm; Based on the three-dimensional signal strength distribution heat map, weak signal areas and signal blind areas are determined.

7. The low-altitude network signal testing method according to claim 6, characterized in that: S4, including: Perform regional signal severity assessment based on the identified weak signal areas and signal blind areas to obtain the problem severity score for each area; Generate corresponding network optimization suggestions based on the problem severity scores of each area; Among them, the evaluation indicators of severity assessment include: the degree of diffusion of weak signal areas, the adjacency and connectivity of weak signal areas, signal strength fluctuation, and geometric characteristics of signal problem areas.

8. A low-altitude network signal testing device, characterized in that: The device comprises: A grid division module is configured to divide the test area into grids and set the flight altitude and flight path of the UAV according to the grid division results; A signal test module is configured to perform a network signal test through a drone based on a set flight altitude and flight path to obtain a test result; 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; Performing advanced path testing based on the signal blind area grid or signal weak area grid in the intermediate path test result to obtain an advanced path test result; A data analysis module, which is configured to perform data analysis based on the test results to obtain analysis results; An optimization suggestion module is configured to generate network optimization suggestions based on the analysis results.

9. A low-altitude network signal testing device, characterized in that: It comprises 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 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the low-altitude network signal testing method according to any one of claims 1 to 7 is implemented.

Images