Urban low-altitude airspace communication network performance test method, device, equipment and medium

Abstract

The invention relates to the technical field of communication networks, and provides an urban low-altitude airspace communication network performance test method and device, equipment and a medium. The method comprises the following steps: acquiring test constraint data, wherein the test constraint data comprises a test area boundary, building data, no-fly zone information, unmanned aerial vehicle performance parameters and battery energy constraint; according to the test constraint data, performing flight route planning processing of the unmanned aerial vehicle to generate a test route; controlling the unmanned aerial vehicle carrying the test terminal to fly according to the test route, controlling the test terminal to perform a communication test with the test server by using the to-be-tested communication network in the flight process of the unmanned aerial vehicle, and synchronously collecting space-time position information of the unmanned aerial vehicle and communication performance data obtained in the communication test; and associating and processing the space-time position information and the communication performance data, and calculating a performance index of the communication network to be tested. The invention provides a feasible method for testing the performance of the low-altitude communication network, and the safety and efficiency of the test are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of communication network technology, and in particular to a method, apparatus, equipment and medium for testing the performance of urban low-altitude airspace communication networks. Background Technology

[0002] With the rapid development of the low-altitude economy, the management and utilization of urban low-altitude airspace has created an urgent need for safe and reliable communication networks. As a critical infrastructure, the performance of 5G / 5G-A networks in this scenario directly impacts drone monitoring, flight path safety, and the feasibility of various application services. Currently, testing methods for terrestrial mobile networks are relatively mature, typically based on fixed road tests or simple preset routes, with relatively stable and singular test environments, terminal states, and motion patterns.

[0003] However, directly applying existing methods to urban low-altitude airspace communication network testing faces significant challenges. The urban low-altitude environment possesses significant three-dimensional characteristics, and UAV flights are subject to multiple stringent constraints, including airspace control, geographical obstacles, and their own endurance. Traditional two-dimensional planar test planning methods cannot effectively characterize these three-dimensional spatial constraints. It is difficult to plan test routes that can efficiently and comprehensively evaluate the network's three-dimensional performance while ensuring flight safety and compliance. This results in biased and inefficient test results that fail to accurately reflect the network's performance in real-world urban low-altitude operating environments. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method, apparatus, equipment and medium for testing the performance of urban low-altitude airspace communication networks, so as to solve the above-mentioned technical problem.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A method for testing the performance of urban low-altitude airspace communication networks, comprising: acquiring test constraint data, the test constraint data including test area boundaries, building data, no-fly zone information, UAV performance parameters, and battery energy constraints; performing UAV flight path planning processing based on the test constraint data to generate a test flight path; controlling a UAV equipped with a test terminal to fly along the test flight path, and controlling the test terminal to conduct communication tests between the communication network under test and the test server during the flight of the UAV, simultaneously collecting the spatiotemporal position information of the UAV and the communication performance data obtained in the communication test; associating and processing the spatiotemporal position information and the communication performance data, and calculating the performance indicators of the communication network under test.

[0006] The beneficial effects of this invention are as follows: This method integrates multi-dimensional test constraint data for three-dimensional flight path planning and simultaneously collects spatiotemporal position and communication performance data during flight. Under the premise of strictly adhering to urban airspace safety and UAV physical limitations, it can automatically generate comprehensive and representative three-dimensional test routes, achieving standardized and accurate three-dimensional evaluation of urban low-altitude communication network performance, significantly improving the safety, efficiency, and practical value of the evaluation results.

[0007] Based on the above technical solution, the present invention can be further improved as follows.

[0008] Further, the step of performing UAV flight path planning processing based on the test constraint data to generate a test route includes: discretizing the test area into spatial grids at each preset test altitude layer to obtain a discretized test area; determining the no-fly zones at each preset test altitude layer based on the discretized test area, the building data, the no-fly zone information, and a preset safety margin; for each preset test altitude layer, generating a UAV traversal path covering the preset test altitude layer based on the discretized test area; for each preset test altitude layer, spatially pruning the UAV traversal path at the preset test altitude layer to remove track segments that pass through the no-fly zones at the preset test altitude layer, obtaining multiple candidate track segments at the preset test altitude layer; and selecting multiple target track segments from the candidate track segments at each preset test altitude layer based on a preset altitude layer execution order and the battery energy constraint, so as to generate the test route based on the multiple target track segments.

[0009] Furthermore, the step of selecting multiple target trajectory segments from candidate trajectory segments at each preset test altitude level based on the preset altitude layer execution order and the battery energy constraint, and generating the test route based on the multiple target trajectory segments, includes: quantitatively evaluating each candidate trajectory segment and calculating the coverage gain and flight energy cost of each candidate trajectory segment; selecting a set of target trajectory segments that satisfy the battery energy constraint from all candidate trajectory segments based on the preset altitude layer execution order, the coverage gain and flight energy cost of each candidate trajectory segment, with the goal of maximizing the total coverage gain; and generating the test route based on the selected set of target trajectory segments.

[0010] Furthermore, the method also includes: periodically evaluating the state of the UAV during its flight; when the UAV's state is characterized by localized communication anomalies, generating a supplementary test route based on the UAV's current location, and controlling the UAV to fly along the supplementary test route before returning to the test route; when the UAV's state is characterized by insufficient remaining energy to support the completion of the remaining routes and return, adjusting the unexecuted routes in the test route to generate a route to be executed, and controlling the UAV to fly along the route to be executed; when the UAV's state is characterized by communication interruption, controlling the UAV to return to the target location along a preset backup route.

[0011] Furthermore, the step of controlling the test terminal to conduct communication tests between the test terminal and the test server using the communication network under test during the flight of the UAV, and synchronously collecting the spatiotemporal location information of the UAV and the communication performance data obtained in the communication test, includes: controlling the test terminal to periodically send Ping data packets to the test server using the communication network under test during the flight of the UAV, and / or controlling the test terminal to perform FTP file upload tasks to the test server using the communication network under test; and collecting and recording the spatiotemporal location information of the UAV in real time, as well as the communication performance data generated by the test terminal during the interaction with the test server.

[0012] Further, the association and processing of the spatiotemporal location information and the communication performance data to calculate the performance indicators of the communication network under test includes: aligning and associating the spatiotemporal location information with the communication performance data based on a unified timestamp to form multiple associated data sets; for each preset test altitude layer, calculating the round-trip latency of all successful Ping interactions corresponding to the preset test altitude layer based on the associated data; for each preset test altitude layer, calculating the average or a specific quantile of the round-trip latency of all successful Ping interactions corresponding to the preset test altitude layer to obtain the transmission latency of the monitoring and identification information at the preset test altitude layer; for each spatial grid passed by the UAV, determining whether the communication service of the spatial grid meets the standards based on all associated data falling within the spatial grid; for each preset test altitude layer... At a predetermined test altitude, the percentage of all spatial grids meeting communication service standards in that altitude level relative to the total number of spatial grids traversed by the UAV in that altitude level is calculated to obtain the network coverage of that altitude level. The flight test time of the UAV is acquired and divided into continuous, equal-length segments. For each time segment, the continuity of communication services within that time segment is determined based on all associated data. For each predetermined test altitude level, the percentage of all continuous communication service time segments corresponding to that altitude level relative to the total number of time segments corresponding to that altitude level is calculated to obtain the service continuity of that altitude level. Based on the transmission delay, network coverage, and service continuity of the monitoring and identification information at each predetermined test altitude level, the performance indicators of the communication network under test are obtained.

[0013] Furthermore, each of the aforementioned UAVs traverses a planar flight path in the shape of a square or a bow.

[0014] To address the aforementioned technical problems, the present invention also provides a performance testing device for urban low-altitude airspace communication networks, comprising: The data acquisition module is used to acquire test constraint data, which includes test area boundaries, building data, no-fly zone information, UAV performance parameters, and battery energy constraints. The flight path generation module is used to perform UAV flight path planning processing based on the test constraint data and generate a test flight path. The communication test module is used to control the drone equipped with the test terminal to fly along the test route, and during the flight of the drone, control the test terminal to conduct communication tests with the test server using the communication network under test, and synchronously collect the spatiotemporal location information of the drone and the communication performance data obtained in the communication test; The performance calculation module is used to correlate and process the spatiotemporal location information and the communication performance data to calculate the performance indicators of the communication network under test.

[0015] To address the aforementioned technical problems, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the urban low-altitude airspace communication network performance testing method as described above.

[0016] To address the aforementioned technical problems, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the urban low-altitude airspace communication network performance testing method described above. Attached Figure Description

[0017] Figure 1 This is a flowchart of the urban low-altitude airspace communication network performance testing method of the present invention; Figure 2 This is a schematic diagram of the U-shaped planar flight path of the urban low-altitude airspace communication network performance testing method of the present invention. Figure 3 This is a schematic diagram of the bow-shaped planar flight path of the urban low-altitude airspace communication network performance testing method of the present invention; Figure 4 This is a schematic diagram of the urban low-altitude airspace communication network performance testing device of the present invention; Figure 5 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0018] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0019] Example 1 like Figure 1 As shown, this embodiment provides a method for testing the performance of urban low-altitude airspace communication networks, including: S101. Obtain test constraint data, which includes test area boundaries, building data, no-fly zone information, UAV performance parameters, and battery energy constraints. S102. Based on the test constraint data, perform UAV flight path planning and generate test flight paths; S103. Control the drone equipped with the test terminal to fly along the test route, and control the test terminal to conduct communication tests between the test terminal and the test server using the communication network to be tested during the drone's flight, and simultaneously collect the drone's spatiotemporal location information and the communication performance data obtained in the communication test. S104. Correlate and process the spatiotemporal location information and communication performance data to calculate the performance indicators of the communication network under test.

[0020] Low-altitude communication base station equipment, specifically 5G or 5G-A base stations, was deployed in the test area. This equipment provides wireless signal coverage, establishing a 5G network signal in the test airspace. All performance metrics tested are essentially evaluations of the network centered around this base station in a low-altitude environment.

[0021] In this embodiment, as a preferred option, the test terminal is either a dedicated 5G terminal or a commercial 5G terminal. The dedicated 5G terminal has a minimum memory capacity of 12GB and a minimum storage capacity of 512GB. The selected dedicated 5G terminal should support 5G frequency bands and coding schemes, possess Multiple-Input Multiple-Output (MIMO) technology, and have a sampling density of at least once every 200ms. Commercial 5G terminals generally use terminals with a high market share, requiring a minimum memory capacity of 12GB and a minimum storage capacity of 512GB.

[0022] The test terminal is equipped with a SIM card, which it uses to access the 5G network provided by the base station. It is advisable to use a test card with unlimited data allowance or sufficient data balance during testing.

[0023] The test terminal runs test software. Existing test software can be used, or it can be modified or enhanced; no specific limitations are specified here. The test software automatically controls the test terminal to initiate and stop Ping or FTP tests according to a preset script. It also collects communication performance data and spatiotemporal position data from the test terminal chip and the UAV flight controller at a high frequency (e.g., every 200ms) and timestamps them.

[0024] The test terminal is used to execute test traffic generation actions such as Ping and FTP upload according to the instructions of the test software. It also collects the lowest-level communication data in real time, such as signal strength, signaling messages, precise throughput, and application layer latency.

[0025] The test server is a high-performance server deployed in a ground data center or in the cloud. It serves as the target IP address for ping commands or the recipient of files uploaded via FTP. The test server's uplink and downlink bandwidth should be no less than 10Gbps (dedicated) or 100Gbps (shared).

[0026] The maximum flight speed of a drone should be less than 20 meters per second, and the actual test flight speed is generally 8 meters per second.

[0027] Optionally, in this embodiment, based on test constraint data, UAV flight path planning is performed to generate test routes, including: discretizing the test area into spatial grids at each preset test altitude level to obtain a discretized test area; determining no-fly zones at each preset test altitude level based on the discretized test area, building data, no-fly zone information, and preset safety margin; for each preset test altitude level, generating a UAV traversal path covering the preset test altitude level based on the discretized test area; for each preset test altitude level, spatially pruning the UAV traversal path at the preset test altitude level to remove track segments that pass through no-fly zones at the preset test altitude level, obtaining multiple candidate track segments at the preset test altitude level; and selecting multiple target track segments from the candidate track segments at each preset test altitude level based on a preset altitude level execution order and battery energy constraints to generate a test route based on the multiple target track segments.

[0028] In terms of flight path organization, the entire test mission can be viewed as a combined mission executed sequentially at multiple altitude levels, with each altitude level corresponding to a set of planar flight paths. The execution order of the altitude levels can be set according to the test requirements, typically proceeding from low to high, or prioritizing relevant altitude levels in areas with dense buildings or high communication risks.

[0029] The test area boundary is a polygonal boundary used to define the test area. No-fly zone information includes a set of no-fly / altitude-restricted / sensitive areas, obtained through local policy information. Building data includes building outlines and heights (GIS / vector / point cloud representations are acceptable). Drone performance parameters include: available battery power (or available flight time), cruise speed, rate of climb / rate of descent, minimum turning radius, and reserved power for return (e.g., 20% of total battery capacity).

[0030] In this embodiment, the preset test heights are 120 meters, 200 meters, 300 meters, 400 meters, 500 meters, and 600 meters.

[0031] The testing was conducted at multiple altitude levels, not as a repetitive exercise, but to address the objective reality of the significant three-dimensional characteristics of the urban low-altitude communication environment. At different flight altitudes, the line-of-sight conditions, building obstructions, multipath effects, and co-channel interference of UAVs all differ significantly. By conducting tests at multiple typical altitude levels, a characteristic description of communication performance varying with altitude can be formed, providing a basis for assessing the layered operation of low-altitude air routes and communication support capabilities, and facilitating an accurate reflection of the network's overall performance in low-altitude airspace.

[0032] The test area is discretized into spatial grids at each preset test height layer to obtain the discretized test area, specifically: First, create a 2D grid: divide the test area into a grid set G; Each grid point A grid node is formed on each preset test height layer. , where h is the corresponding height.

[0033] The grid ruler is typically set to 50m or 100m.

[0034] Based on building data, for each building Perform expansion (i.e., set the horizontal obstacle avoidance distance, such as...) ), to obtain the buffer And the height is constrained, specifically: in the mesh nodes. If ( If a vertical safety margin is required (e.g., ≥20m), then that node cannot be flown; where, Describe the height of the building at grid point c. Also, nodes falling within the no-fly zone Z cannot fly.

[0035] Thus, the set of flyable nodes is obtained: .

[0036] Optionally, in an embodiment, each drone traverses a planar flight path in the shape of a square or a bow.

[0037] Within each test altitude layer, a traversal flight path is first generated on the corresponding two-dimensional plane. The path can adopt one of two typical structures: a bow-shaped or a U-shaped pattern. The bow-shaped path achieves uniform coverage of the test area by arranging parallel scan lines at fixed intervals (e.g., 200m). Figure 3 As shown. The zigzag path starts from the center of the test area and gradually expands outward to form a rectangular loop. It is suitable for scenarios where the central area needs to be heavily covered, such as... Figure 2 As shown.

[0038] After generating the basic scan path, it is spatially clipped along with the no-fly zone formed by buildings and no-fly zones to remove non-flying segments, ultimately resulting in a set of candidate track segments that satisfy safety and airspace constraints at each altitude level. .

[0039] Optionally, in an embodiment, based on a preset altitude layer execution order and battery energy constraints, multiple target trajectory segments are selected from candidate trajectory segments at each preset test altitude layer to generate a test route based on the multiple target trajectory segments. This includes: quantitatively evaluating each candidate trajectory segment and calculating the coverage gain and flight energy cost of each candidate trajectory segment; selecting a set of target trajectory segments that meet the battery energy constraints from all candidate trajectory segments based on the preset altitude layer execution order, the coverage gain and flight energy cost of each candidate trajectory segment, with the goal of maximizing the total coverage gain; and generating a test route based on the selected set of target trajectory segments.

[0040] Specifically, each track segment in the candidate track segment set is quantitatively evaluated, and each candidate track segment is evaluated... Definition: The coverage gain corresponding to a candidate track segment is determined by the number of newly added raster cells. The flight energy cost corresponding to the candidate flight path segment is determined by the flight distance / time / energy consumption (including turning penalty). .

[0041] Flight energy cost The calculation model is as follows: ; in, Candidate track fragments Length; Candidate track fragments Number of turns (or penalty based on curvature, to ensure) ); Candidate track fragments The vertical ascent (usually 0 at the same height level, only when crossing levels); , , All are weighting coefficients.

[0042] The selection of the target flight path segment set is achieved by establishing and solving a classic constrained combinatorial optimization model. Specifically, each candidate flight path segment is considered a selectable target, with known coverage benefits and energy costs. The optimization objective is to select a subset of all segments while maximizing the total coverage benefit, under the hard constraint of the total energy budget (including the energy consumption of the segment itself and the energy consumption of transitional flights between segments). In practical engineering implementation, a general-purpose optimization solver or heuristic algorithm can be used to solve this model to obtain the theoretically optimal flight path combination.

[0043] Specifically, the constraint can be expressed as: ; in, For the battery's usable energy, Reserve power for return flight.

[0044] The resulting route plan can automatically reduce the traversal range when the battery is low, but still retains a route segment that is representative of the overall test area, thus achieving adaptive route planning based on battery conditions.

[0045] Prior to the test, all necessary preparations for the flight should be completed according to the test mission, including drone equipment selection, airspace application, and drone registration verification. The selected drone equipment should meet the requirements for safe completion of the test mission in terms of payload, duration, and securing. The drone pilot should possess a valid certificate issued by the relevant management department. Airspace applications for drone flights should be submitted to the appropriate management department in advance, based on the actual flight schedule.

[0046] Before takeoff, the drone's flight control, Real-Time Kinematic (RTK) or other satellite positioning, and obstacle avoidance and return-to-home settings should be checked according to the standards or manuals provided by the drone manufacturer. Safety control preparations and checks should be conducted for the areas involved in the flight, as required by relevant management departments and the drone manufacturer, and the pilot should wear the prescribed attire. Weather conditions in the test area should be monitored before takeoff, during flight, and during landing to ensure flight safety. Appropriate emergency plans should be prepared for the test area to ensure timely response to any abnormal situations.

[0047] Optionally, in the embodiments, the method further includes: periodically evaluating the state of the UAV during its flight; when the UAV's state is characterized by local area communication anomalies, generating a supplementary test route based on the UAV's current position, and controlling the UAV to fly along the supplementary test route before returning to the test route; when the UAV's state is characterized by insufficient remaining energy to support the completion of the remaining route and return, adjusting the unexecuted routes in the test route, generating a route to be executed, and controlling the UAV to fly along the route to be executed; when the UAV's state is characterized by communication interruption, controlling the UAV to return to the target position along a preset backup route.

[0048] During flight, the flight status is periodically assessed at fixed time intervals (e.g., 1 second or 5 seconds), focusing on monitoring for anomalies that may affect the completion of the test mission in three aspects: communication, safety, and energy. Regarding communication, the system detects continuous packet loss or dropped connections, as well as situations where communication latency exceeds a set threshold or data throughput falls below a threshold for a sustained period. For safety, the system monitors whether wind speed exceeds permissible limits, whether GNSS / RTK positioning is abnormal, and whether obstacle avoidance alarms are triggered. Regarding energy, the system predicts the remaining battery power based on the current flight status to determine if it is sufficient to support the completion of the remaining test route and a safe return.

[0049] When anomaly assessment conditions are triggered during flight, different levels of flight path adjustment strategies can be implemented based on the severity and scope of the anomaly. These strategies include: When communication anomalies occur continuously within a certain spatial grid but the overall flight status remains stable, a small-scale supplementary test route can be generated near the current flight route location. For example, a local grid with a radius of about 100m to 200m or a small zigzag path can be used to conduct intensive testing on the abnormal area. After the supplementary test is completed, the UAV returns to the original main flight route and continues to perform subsequent test tasks.

[0050] When the remaining power is predicted to be insufficient to support all planned routes, unexecuted route segments are cut off. Segments with a higher ratio of coverage benefits to execution costs are retained, while segments with lower coverage contributions or longer distances are canceled, to ensure that flight missions can be completed on the premise of safe return.

[0051] In the event of severe communication disruption or significant security risks, immediately abandon the current test mission, switch to the preset backup route, fly directly to the safe corridor or return point, and record and mark the uncompleted test segments as the basis for subsequent retest missions.

[0052] In this embodiment, instead of employing a computationally complex global replanning approach during online route adjustments, a remecing horizon strategy is introduced to balance real-time performance and system stability. Specifically, each time, only a short-term flight path within a limited future timeframe is planned, such as the next 2 to 5 minutes, and a short segment within that timeframe is executed based on the current optimal result. After approximately 30 to 60 seconds, the planning results are updated based on the latest communication, energy, and safety statuses. This remecing horizon approach allows for timely responses to environmental changes and status anomalies without increasing computational burden, thus ensuring both real-time performance and overall flight stability during route adjustments.

[0053] This method does not merely optimize the collection and calculation process of communication performance indicators. Its core innovation lies in its systematic introduction of an intelligent generation and dynamic execution method for test routes based on 3D constraint modeling, energy consumption budgeting, and online adjustment mechanisms, while adhering to the general framework for low-altitude communication testing. This method aims to address the fundamental problems in existing testing standards and practices, such as the heavy reliance on manual planning for test routes and the difficulty in automatically generating and maintaining the integrity, safety, and representativeness of test routes under the constraints of complex urban 3D spaces, airspace control rules, and the limited endurance of UAVs. This method enables efficient, comprehensive, and reproducible performance evaluation of low-altitude communication networks while ensuring testing safety and compliance.

[0054] Optionally, in this embodiment, during the flight of the UAV, the control test terminal uses the communication network under test to conduct communication tests with the test server, synchronously collecting the spatiotemporal location information of the UAV and the communication performance data obtained in the communication test, including: during the flight of the UAV, the control test terminal periodically sends Ping data packets to the test server using the communication network under test, and / or the control test terminal performs FTP file upload tasks to the test server using the communication network under test; real-time collection and recording of the spatiotemporal location information of the UAV, as well as the communication performance data generated by the test terminal during the interaction with the test server.

[0055] Take off the drone from the selected location. After reaching the designated altitude, the drone will perform a pre-set flight trajectory. Before takeoff, start the Packet Internet Groper (ping) test, performing at least 20 consecutive tests with a 320ms interval, using 32-byte packets, and targeting the test server. For FTP testing, perform FTP upload tests (uploaded files should ideally be larger than 10GB, the number of test threads should ideally be greater than or equal to 30, and the test interval between different files should be 1 second).

[0056] After the drone flies along the preset route, it is controlled to land, ending the test. After the test flight, drone flight trajectory data and communication performance data can be obtained, including time, spatial location, altitude information, transmission latency of surveillance and identification information, data transmission rate, communication success rate, and related event records.

[0057] It should be noted that in urban low-altitude scenarios, signals may originate from ground stations (typically deployed in low-to-mid frequency bands, such as 2.6 GHz or 3.5 GHz) or future dedicated low-altitude base stations (potentially using higher frequency bands). The propagation characteristics (penetration, coverage) of different frequency bands vary significantly. Therefore, it is also necessary to record in detail the key configuration parameters of the communication network under test (such as operating frequency band, system bandwidth, etc.).

[0058] Optionally, in the embodiments, the spatiotemporal location information and communication performance data are correlated and processed to calculate the performance indicators of the communication network under test, including: based on a unified timestamp, the spatiotemporal location information and communication performance data are time-aligned and correlated to form multiple correlated data; for each preset test altitude layer, based on the correlated data corresponding to the preset test altitude layer, the round-trip latency of all successful Ping interactions corresponding to the preset test altitude layer is calculated; for each preset test altitude layer, the average value or a specific quantile of the round-trip latency of all successful Ping interactions corresponding to the preset test altitude layer is calculated to obtain the transmission latency of the monitoring and identification information of the preset test altitude layer; for each spatial grid passed by the UAV, based on all correlated data falling into the spatial grid, it is determined whether the communication service of the spatial grid meets the standard; for each preset At the test altitude layer, the percentage of all spatial grids with compliant communication services at the preset test altitude layer relative to the total number of spatial grids traversed by the UAV at the preset test altitude layer is calculated to obtain the network coverage rate of the preset test altitude layer. The flight test time of the UAV is acquired and divided into continuous equal-length segments. For each time segment, the continuity of communication services within the time segment is determined based on all associated data. For each preset test altitude layer, the percentage of all time segments with continuous communication services corresponding to the preset test altitude layer relative to the total number of time segments corresponding to the preset test altitude layer is calculated to obtain the service continuity of the preset test altitude layer. Based on the transmission delay, network coverage, and service continuity of the monitoring and identification information at each preset test altitude layer, the performance indicators of the communication network under test are obtained.

[0059] After the test is completed, the flight trajectory data and communication performance data are time-aligned and correlated, and data from unstable phases such as takeoff and landing are removed. On this basis, statistical analysis is performed according to spatial location, altitude layer and time dimension to calculate indicators such as coverage, continuity and transmission delay, and judged against the thresholds specified by the standard.

[0060] The round-trip latency of a Ping interaction is obtained as follows: The timestamp of the sent Ping test packet and the timestamp of the received response packet are obtained, and the difference between the two is calculated to obtain the single round-trip latency. The single round-trip latency is then matched with synchronously acquired spatiotemporal location information to determine the spatial grid to which the latency belongs.

[0061] The monitoring and identification information transmission delay refers to the time required from when the test terminal sends out the identification information to when the test server receives the data. The acceptable standard is 1 second.

[0062] Coverage refers to the percentage of the total area within the low-altitude flight path where a communication network can stably provide services with the expected performance. Testing can be conducted using ping latency tests or file transfer protocol (FTP) uploads.

[0063] Continuity refers to the percentage of communication data packets within a low-altitude air route and flight area that the communication network can provide the expected performance service within a fixed time period. Testing can be performed using ping latency tests or FTP uploads.

[0064] For coverage calculation, when using Ping testing, the Ping success rate within each spatial grid is statistically analyzed. Statistics are counted per second; a successful ping is considered successful if it occurs once per second. Within a grid, a drone may fly for several seconds. Within each second, the test terminal may send multiple Ping packets. As long as any Ping within that second receives a response from the server, that second is considered a successful network service second within that grid. The Ping success rate for that grid is calculated as: number of successful network service seconds within that grid / total number of seconds the drone flies within that grid. If the Ping success rate within a grid reaches a first preset threshold, the communication service of that spatial grid is deemed to meet the standard.

[0065] When using FTP testing, the percentage of data with FTP upload speeds greater than 300Kbps within each spatial grid is counted. While traversing the grid, the testing software continuously records the instantaneous FTP upload speed (e.g., once per second). Each instantaneous speed value is compared to the 300Kbps threshold. The speed compliance rate of the grid is calculated as: the number of sampling points in the grid with speeds greater than 300Kbps / the total number of sampling points in the grid. If the speed compliance rate of the grid reaches a second preset threshold, the communication service of the spatial grid is deemed to meet the standard.

[0066] For continuous computation, when using Ping testing, the success rate of Ping is calculated over time. For each time segment, the results of all Ping attempts within that time segment are examined. As long as any Ping attempt within that time segment successfully receives a response, that time segment is considered to have continuous service.

[0067] When using FTP testing, the percentage of upload speeds meeting the target is statistically analyzed over time. For each time segment, the average upload speed within that segment is calculated. If this average speed exceeds a set threshold (300Kbps), the service is considered continuous for that time segment.

[0068] This method is based on mature and common methods in the industry in the selection of testing technology routes, such as using ping, FTP and other methods to conduct communication performance testing, and combining spatial gridded statistics to conduct quantitative analysis of the test results.

[0069] Finally, a test report is generated. The test report should comprehensively reflect the test process and results, and should include at least the following parts: report title; identification of the network under test (such as operator, test location and environmental data); source of test task; test route plan; test date; list of test equipment used; summary of raw test data; statistical analysis and conclusions of test results; information on the testing organization and personnel; and explanation of other factors that may affect the test results.

[0070] To further improve the efficiency and standardization of test result processing and report compilation, an automated analysis system based on rules or generative models can be introduced. This system takes test configuration parameters, flight trajectories, communication performance indicators, and related event records as input. Through automated time alignment, spatial mapping, and statistical analysis, it generates structured results containing indicators such as coverage, continuity, and transmission latency within each altitude layer and space unit.

[0071] Based on this structured result, and according to preset rules and statistical characteristics, regions or height layers that fail to meet performance standards are automatically identified and classified, and supplementary suggestions are provided regarding potential causes such as insufficient coverage and enhanced interference. Simultaneously, based on the spatial and height distribution characteristics of abnormal areas, retesting suggestions are automatically generated, such as increasing sampling density in specific areas or conducting comparative tests at different time periods for specific height layers. It should be clarified that all conclusions and suggestions output by the model are for auxiliary analysis reference only and do not replace final human professional judgment.

[0072] Finally, standardized templates are used to automatically generate test reports and recommendations, and all conclusions are traceable back to the original data and statistical process. This significantly improves the efficiency and consistency of data processing, analysis and report preparation while strictly adhering to established testing methods and judgment principles.

[0073] It should be noted that the specific values ​​listed in this embodiment, such as test terminal specifications, flight speed, altitude layer settings, grid size, test parameters, and time thresholds, are provided for illustrative and exemplary purposes only, aiming to clearly illustrate the technical solution of the present invention, and are not intended to limit the invention. In practical applications, reasonable adjustments and selections can be made according to specific test environments, equipment conditions, and evaluation needs.

[0074] Example 2 like Figure 4 As shown, this embodiment provides a performance testing device 200 for urban low-altitude airspace communication networks, comprising: The data acquisition module 201 is used to acquire test constraint data, which includes test area boundaries, building data, no-fly zone information, UAV performance parameters, and battery energy constraints. The flight path generation module 202 is used to perform UAV flight path planning processing based on test constraint data and generate test flight paths. The communication test module 203 is used to control the UAV carrying the test terminal to fly along the test route, and to control the test terminal to conduct communication tests with the test server using the communication network under test during the flight of the UAV, and to simultaneously collect the spatiotemporal position information of the UAV and the communication performance data obtained in the communication test. The performance calculation module 204 is used to correlate and process spatiotemporal location information and communication performance data to calculate the performance indicators of the communication network under test.

[0075] Optionally, in an embodiment, the route generation module 202 includes: Spatial discretization unit is used to discretize the test area into spatial grids at each preset test height layer to obtain the discretized test area; The area determination unit is used to determine the no-fly zones at each preset test altitude level based on the discretized test area, building data, no-fly zone information, and preset safety margin. The basic path generation unit is used to generate a drone traversal path covering the preset test height layer based on the discretized test area for each preset test height layer. The path trimming unit is used to perform spatial trimming on the UAV traversal path of each preset test altitude layer to remove the track segments that pass through the no-fly zone on the preset test altitude layer in the UAV traversal path of the preset test altitude layer, and obtain multiple candidate track segments of the preset test altitude layer. The route generation unit is used to select multiple target trajectory segments from candidate trajectory segments at each preset test altitude level based on the preset altitude layer execution order and battery energy constraints, so as to generate a test route based on the multiple target trajectory segments.

[0076] Optionally, in an embodiment, the route generation unit includes: The quantitative evaluation subunit is used to perform quantitative evaluation on each candidate track segment, and calculate the coverage gain and flight energy cost of each candidate track segment. The track segment filtering subunit is used to select a set of target track segments that meet the battery energy constraints from all candidate track segments based on the preset altitude layer execution order, the coverage benefits of each candidate track segment and the flight energy cost, with the goal of maximizing the total coverage benefits. The route generation subunit is used to generate test routes based on the selected set of target track segments.

[0077] Optionally, in an embodiment, the apparatus further includes: The status assessment module is used to periodically assess the status of the drone during its flight. The first decision module is used to generate a supplementary test route based on the drone's current position when the drone's status is a local area communication anomaly, and to control the drone to fly according to the supplementary test route and then return to the test route. The second decision module is used to adjust the unexecuted routes in the test route, generate routes to be executed, and control the drone to fly according to the routes to be executed when the drone's remaining energy is expected to be insufficient to support the completion of the remaining route and return. The third decision module is used to control the drone to return to the target location along a preset backup route when the drone's communication status is interrupted.

[0078] Optionally, in an embodiment, the communication test module 203 includes: The communication test unit is used to control the test terminal to periodically send Ping data packets to the test server using the communication network under test during the flight of the UAV, and / or control the test terminal to perform FTP file upload tasks to the test server using the communication network under test; The data acquisition unit is used to collect and record the spatiotemporal location information of the UAV in real time, as well as the communication performance data generated by the test terminal during the interaction with the test server.

[0079] Optionally, in an embodiment, the performance calculation module 204 includes: The alignment and association unit is used to align and associate spatiotemporal location information with communication performance data based on a unified timestamp, forming multiple associated data. The statistics unit is used to calculate the round-trip latency of all successful Ping interactions corresponding to each preset test height layer, based on the associated data corresponding to the preset test height layer. The first calculation unit is used to calculate the average or a specific quantile of the round-trip latency of all successful Ping interactions corresponding to each preset test height layer, so as to obtain the transmission latency of the monitoring and identification information of the preset test height layer. The first judgment unit is used to determine whether the communication service of each spatial grid passed by the UAV meets the standard based on all the associated data falling into the spatial grid. The second calculation unit is used to calculate, for each preset test altitude layer, the percentage of all spatial grids in the preset test altitude layer that meet the communication service standards relative to the number of spatial grids passed by the UAV in the preset test altitude layer, and obtain the network coverage rate of the preset test altitude layer. The time acquisition unit is used to acquire the flight test time of the UAV and divide the flight test time into continuous segments of equal length. The second judgment unit is used to determine whether the communication service of each time segment is continuous based on all the associated data within the time segment. The third calculation unit is used to calculate, for each preset test height layer, the percentage of the number of continuous time segments of all communication services corresponding to the preset test height layer to the total number of time segments corresponding to the preset test height layer, so as to obtain the service continuity of the preset test height layer. The performance index generation unit is used to obtain the performance index of the communication network under test based on the transmission latency, network coverage and service continuity of the monitoring and identification information of each preset test height layer.

[0080] Optionally, in an embodiment, each drone traverses a planar flight path in the shape of a square or a bow.

[0081] In some embodiments, the urban low-altitude airspace communication network performance testing device 200 of the present invention can be implemented in a combination of hardware and software. As an example, the urban low-altitude airspace communication network performance testing device 200 of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the urban low-altitude airspace communication network performance testing method of the present invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0082] The modules described in the embodiments of this invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0083] Example 3 like Figure 5 As shown, this embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the urban low-altitude airspace communication network performance testing method as described in Embodiment 1.

[0084] In other words, an electronic device according to an embodiment of the present invention may include, but is not limited to, a processor and a memory; the memory is used to store computer programs; the processor is used to execute the urban low-altitude airspace communication network performance testing method shown in any embodiment of the present invention by calling the computer program.

[0085] In one alternative embodiment, an electronic device is provided. Figure 5 The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may further include a transceiver 304, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present invention.

[0086] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0087] Bus 302 may include a path for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus 302 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0088] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0089] The memory 303 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 301. The processor 301 executes the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0090] Among them, electronic devices can also be terminal devices, which can be any device that can install applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0091] It should be noted that, Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0092] Example 4 This embodiment provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to execute a performance testing method for urban low-altitude airspace communication networks as described in Embodiment 1.

[0093] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0094] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the aforementioned urban low-altitude airspace communication network performance testing method.

[0095] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0096] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0097] The computer-readable storage medium provided in this invention can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EEPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0098] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0099] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0100] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0101] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0102] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for testing the performance of urban low-altitude airspace communication networks, characterized in that, include: Acquire test constraint data, which includes test area boundaries, building data, no-fly zone information, UAV performance parameters, and battery energy constraints; Based on the test constraint data, the UAV flight path planning process is performed to generate the test path; The test terminal is controlled to fly along the test route, and during the flight of the test terminal, the test terminal is controlled to conduct communication tests with the test server using the communication network under test, and the spatiotemporal location information of the test terminal and the communication performance data obtained in the communication test are collected simultaneously. The spatiotemporal location information and the communication performance data are correlated and processed to calculate the performance indicators of the communication network under test.

2. The method for testing the performance of urban low-altitude airspace communication networks according to claim 1, characterized in that, The step of performing UAV flight path planning processing based on the test constraint data to generate a test path includes: The test area is discretized into spatial grids at each preset test height layer to obtain the discretized test area; Based on the discrete test area, the building data, the no-fly zone information, and the preset safety margin, the no-fly zone at each preset test height level is determined. For each preset test height layer, a drone traversal path covering the preset test height layer is generated based on the discrete test area. For each preset test altitude layer, the UAV traversal path of the preset test altitude layer is spatially truncated to remove the track segments that pass through the no-fly zone on the preset test altitude layer in the UAV traversal path of the preset test altitude layer, so as to obtain multiple candidate track segments of the preset test altitude layer. Based on the preset altitude layer execution order and the battery energy constraint, multiple target trajectory segments are selected from the candidate trajectory segments of each preset test altitude layer to generate the test route based on the multiple target trajectory segments.

3. The method for testing the performance of urban low-altitude airspace communication networks according to claim 2, characterized in that, The step of selecting multiple target trajectory segments from candidate trajectory segments at each preset test altitude level based on the preset altitude layer execution order and the battery energy constraint, and generating the test route based on the multiple target trajectory segments, includes: Each candidate track segment is quantitatively evaluated, and the coverage gain and flight energy cost of each candidate track segment are calculated. Based on the preset altitude layer execution order, the coverage gain and flight energy cost of each candidate track segment, a set of target track segments that meet the battery energy constraints is selected from all candidate track segments with the goal of maximizing the total coverage gain. The test route is generated based on the selected set of target track segments.

4. The method for testing the performance of urban low-altitude airspace communication networks according to claim 1, characterized in that, Also includes: During the flight of the drone, the status of the drone is periodically assessed; When the UAV is in a state of local area communication failure, a supplementary test route is generated based on the UAV's current position, and the UAV is controlled to fly according to the supplementary test route and then return to the test route. When the drone's remaining energy is expected to be insufficient to support the completion of the remaining route and return, the unexecuted routes in the test route are adjusted to generate a route to be executed, and the drone is controlled to fly according to the route to be executed; When the drone is in a communication interruption state, control the drone to return to the target location according to the preset backup route.

5. The method for testing the performance of urban low-altitude airspace communication networks according to claim 1, characterized in that, The process involves controlling the test terminal to conduct communication tests between the test terminal and the test server using the communication network under test during the flight of the UAV, and simultaneously collecting the spatiotemporal location information of the UAV and the communication performance data obtained during the communication tests, including: During the flight of the UAV, the test terminal is controlled to periodically send Ping data packets to the test server using the communication network under test, and / or the test terminal is controlled to perform FTP file upload tasks to the test server using the communication network under test; The system collects and records the spatiotemporal location information of the UAV in real time, as well as the communication performance data generated by the test terminal during its interaction with the test server.

6. The method for testing the performance of urban low-altitude airspace communication networks according to claim 2, characterized in that, The step of associating and processing the spatiotemporal location information and the communication performance data to calculate the performance indicators of the communication network under test includes: Based on a unified timestamp, the spatiotemporal location information and the communication performance data are time-aligned and correlated to form multiple correlated data; For each preset test height layer, based on the associated data corresponding to the preset test height layer, the round-trip latency of all successful Ping interactions corresponding to the preset test height layer is calculated. For each preset test height layer, calculate the average or a specific quantile of the round-trip latency of all successful Ping interactions corresponding to the preset test height layer to obtain the transmission latency of the monitoring and identification information of the preset test height layer; For each spatial grid that the drone passes through, the communication service of the spatial grid is determined to meet the standard based on all associated data falling into the spatial grid. For each preset test altitude layer, calculate the percentage of all spatial grids in the preset test altitude layer that meet the communication service standards to the total number of spatial grids traversed by the UAV in the preset test altitude layer, and obtain the network coverage rate of the preset test altitude layer; The flight test time of the UAV is obtained, and the flight test time is divided into continuous segments of equal length. For each time segment, based on all associated data within the time segment, determine whether the communication service of the time segment is continuous; For each preset test height layer, calculate the percentage of the number of continuous time segments of all communication services corresponding to the preset test height layer to the total number of time segments corresponding to the preset test height layer, and obtain the service continuity of the preset test height layer. The performance indicators of the communication network under test are obtained based on the transmission delay, network coverage, and service continuity of the monitoring and identification information at each preset test height layer.

7. The method for testing the performance of urban low-altitude airspace communication networks according to claim 2, characterized in that, Each of the aforementioned UAVs traverses a planar flight path in the shape of a square or a bow.

8. A performance testing device for urban low-altitude airspace communication networks, characterized in that, include: The data acquisition module is used to acquire test constraint data, which includes test area boundaries, building data, no-fly zone information, UAV performance parameters, and battery energy constraints. The flight path generation module is used to perform UAV flight path planning processing based on the test constraint data and generate a test flight path. The communication test module is used to control the drone equipped with the test terminal to fly along the test route, and during the flight of the drone, control the test terminal to conduct communication tests with the test server using the communication network under test, and synchronously collect the spatiotemporal location information of the drone and the communication performance data obtained in the communication test; The performance calculation module is used to correlate and process the spatiotemporal location information and the communication performance data to calculate the performance indicators of the communication network under test.

9. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the urban low-altitude airspace communication network performance testing method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the urban low-altitude airspace communication network performance testing method according to any one of claims 1 to 7.

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