Airway monitoring network test and evaluation method and related equipment

Abstract

The invention discloses an air route monitoring network testing and evaluating method and related equipment, and relates to the technical field of air route monitoring network testing, and the method comprises the steps: constructing a three-dimensional testing grid covering a low-altitude public air route; constructing an associated test environment based on the three-dimensional test grid; in a laboratory simulation environment and a typical city external field environment, a test target is controlled to carry out simulated flight or actual flight along an air route covered by the three-dimensional test grid, and truth value data provided by a corresponding truth value system and monitoring data output by an air route monitoring network are synchronously collected; and performing time and space alignment on the acquired truth value data and the monitoring data, calculating a performance index, and then evaluating the performance of the air route monitoring network in the urban complex low-altitude environment where the low-altitude public air route is located. According to the method, the performance weakness of the public route monitoring network can be accurately identified, and solid data support and decision basis can be provided for planning and construction, technical optimization, operation management and standard formulation of the low-altitude public route monitoring network.

Description

Technical Field

[0001] This invention relates to the field of airway surveillance network testing technology, and in particular to a method and related equipment for testing and evaluating airway surveillance networks. Background Technology

[0002] As a new type of infrastructure supporting the development of the low-altitude economy, the safe and efficient operation of low-altitude public airways relies on a high-performance, highly reliable low-altitude public airway surveillance network. This network needs to be deployed in the highly complex physical and electromagnetic environment of urban low-altitude areas, facing a series of severe challenges such as building obstruction, multipath reflection, and diverse wireless signal interference. Therefore, how to scientifically, objectively, and comprehensively test and evaluate the actual performance of the airway surveillance network in the complex urban low-altitude environment, and ensure its continuous, stable, and accurate surveillance capabilities, has become a key technical problem that urgently needs to be solved in the construction and operation management of low-altitude airways.

[0003] Currently, the common testing approach for verifying the capabilities of airway surveillance systems relies primarily on controlled laboratory simulation environments. This existing technology involves constructing a semi-physical simulation platform in the laboratory, using a signal simulator to simulate various sensor signals, and injecting simulated flight trajectories to verify the basic functions of the surveillance system, such as data processing, fusion, and output. This method can, to some extent, evaluate the system's algorithmic logic and interface specifications, providing a relatively convenient and safe testing method for performance evaluation during the system development phase.

[0004] However, the existing technologies focusing on laboratory environments have significant limitations. Their main shortcoming lies in the lack or inadequacy of field verification, leading to a disconnect between the testing environment and the real-world operating environment. Laboratory environments struggle to realistically and completely reproduce the complex spatial structure characteristics and dynamic electromagnetic interference of low-altitude urban scenarios. For example, complex factors such as non-line-of-sight obstruction and variable multipath effects caused by building clusters in real cities, GNSS signal interruptions caused by high-rise canyons, and co-channel / adjacent-channel interference from 5G networks and Wi-Fi devices can often only be simplified or partially simulated in the laboratory. This prevents traditional testing methods from fully exposing and verifying the performance bottlenecks and failure risks that airway surveillance networks may encounter in actual deployment, and the test conclusions offer limited support for guiding network optimization and risk management in real-world operating environments.

[0005] Furthermore, performance verification requires a clear evaluation benchmark. Currently, there is a lack of a universally accepted performance index system for airway surveillance networks, and the performance indicators, parameters, and calculation methods used in different testing procedures often vary significantly. This makes it difficult to compare surveillance network performance results obtained under different testing conditions, which is not conducive to the unified application of test conclusions and the standardized promotion of surveillance network technology.

[0006] Therefore, the industry urgently needs a testing and evaluation method that can effectively overcome the above-mentioned shortcomings. This method must be able to connect the laboratory and the real field, and build an integrated testing system that can ensure the repeatability and comparability of tests, while fully incorporating the characteristics of the complex low-altitude environment in cities, so as to achieve an objective, comprehensive and reliable evaluation of the performance of the low-altitude public airway surveillance network. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to address the shortcomings of the prior art, specifically by providing a method and related equipment for testing and evaluating airway surveillance networks, as detailed below: 1) In a first aspect, the present invention provides a method for testing and evaluating airway surveillance networks, the specific technical solution of which is as follows: Construct a three-dimensional test grid covering low-altitude public air routes; Based on a three-dimensional test grid, an associated test environment is constructed, which includes a laboratory simulation environment and a typical urban field environment. In laboratory simulation environments and typical urban field environments, the control test target is controlled to perform simulated flight or actual flight along the route covered by the three-dimensional test grid, and the truth data provided by the corresponding truth system and the monitoring data output by the route monitoring network are collected simultaneously. After aligning the collected ground truth data with the monitoring data in time and space, calculate performance indicators including at least monitoring probability, monitoring information update frequency, alarm latency, and monitoring positioning accuracy. Based on the calculated performance indicators, the performance of the airway surveillance network in the complex low-altitude environment of cities where low-altitude public airways are located is evaluated.

[0008] The beneficial effects of the airway surveillance network testing and evaluation method provided by this invention are as follows: By constructing a three-dimensional test grid covering low-altitude public airways, and simultaneously building associated laboratory simulation environments and typical urban field environments based on this grid, a test system connecting the indoor and outdoor fields is formed. This method overcomes the shortcomings of traditional solutions that rely solely on laboratory simulations, leading to a disconnect between the test environment and the real operating environment. By utilizing the outdoor environment, it introduces real urban obstructions, multipath propagation, and electromagnetic interference, making performance verification more comprehensive and reliable. By controlling the test target to fly along the airways covered by the grid and simultaneously collecting ground truth and surveillance data, and then performing strict temporal and spatial alignment on the data, a standardized data acquisition and quantitative analysis process is established, calculating multiple key performance indicators such as surveillance probability, surveillance information update frequency, alarm latency, and surveillance positioning accuracy. These clearly defined performance indicators provide a unified and operable benchmark for evaluating the capabilities of airway surveillance networks, ensuring the comparability of performance results under different test conditions and environments, and overcoming the problem of inconsistent evaluation standards in existing technologies. Ultimately, performance evaluation based on quantitative indicators can accurately identify the performance shortcomings of airway surveillance networks in various complex low-altitude urban environments, providing reliable data support and decision-making basis for network planning, construction, technical optimization, and operation management.

[0009] Based on the above scheme, the airway surveillance network testing and evaluation method of the present invention can be further improved as follows.

[0010] Furthermore, the three-dimensional test grid is defined based on the centerline of the low-altitude public airway and is divided into grid cells in the horizontal and vertical directions.

[0011] The beneficial effects of adopting the above-mentioned further scheme are as follows: Using the centerline of the low-altitude public airway as a reference, the spatial framework of the three-dimensional test grid is strictly aligned with the actual geometric orientation of the airway. Dividing the grid into horizontal and vertical cells establishes a refined and standardized spatial quantification coordinate system for performance evaluation. This approach ensures that testing activities systematically cover the three-dimensional airspace along the airway, allowing subsequently collected data and calculated performance indicators to be correlated with specific spatial locations, thereby supporting the spatial traceability of performance evaluation and the comparability between different airway segments.

[0012] Furthermore, the area covered by the constructed 3D test grid includes various preset urban environment types, such as densely populated urban areas, high-rise canyon areas, open transition areas, and complex electromagnetic areas.

[0013] The beneficial effects of adopting the above-mentioned further approach are as follows: By requiring the three-dimensional test grid to include various preset environmental types such as densely populated urban areas, high-rise canyon areas, open transition areas, and complex electromagnetic areas, the test design is forced to comprehensively cover typical urban low-altitude scenarios that affect surveillance performance. This makes performance evaluation no longer targeted at a single or ideal environment, but rather allows for a systematic assessment of the airway surveillance network's performance under different challenges such as building obstruction, signal multipath, clean airspace, and strong electromagnetic interference. This results in a complete and environment-specific network performance profile, with conclusions that better reflect actual operational needs.

[0014] Furthermore, the calculation includes performance metrics at least including surveillance probability, surveillance information update frequency, alarm latency, and surveillance positioning accuracy, including: In the real data and the surveillance data after time and space alignment, according to the set evaluation rules, the ratio of the effective surveillance data output by the airway surveillance network to the expected surveillance data is calculated, and this ratio is used as the surveillance probability. In the real data and the monitoring data after time and space alignment, the number of times the effective monitoring information output by the airway surveillance network for the same test target is updated within a unit time, and the number of updates is used as the monitoring information update frequency. In the real data and the monitoring data after time and space alignment, the horizontal error and vertical error between the monitoring data and the real data are calculated respectively, and used as the monitoring positioning accuracy; When the test target triggers a preset abnormal event, the first timestamp of the event and the second timestamp of the alarm information output by the airway surveillance network are recorded, and the alarm delay is calculated based on the time difference between the first timestamp and the second timestamp.

[0015] The beneficial effects of adopting the above-mentioned further solutions are: providing clear and operable calculation definitions for monitoring probability, monitoring information update frequency, monitoring positioning accuracy, and alarm latency, and establishing a unified standard for performance quantification. Based on time- and space-aligned data, it specifies how to statistically analyze valid points, calculate errors, count update times, and measure alarm latency, ensuring the objectivity and repeatability of the measurement of each indicator. This solves the problem of inconsistent evaluation benchmarks, providing a basis for horizontal comparison of performance results under different test procedures and environments, and strongly supporting the establishment of a standardized evaluation system.

[0016] 2) Secondly, the present invention also provides a test and evaluation system for airway surveillance networks, the specific technical solution of which is as follows: It includes a test grid construction module, a test environment construction module, a control acquisition module, an alignment calculation module, and an evaluation module; The test mesh building module is used to: construct a 3D test mesh covering low-altitude public airways; The test environment construction module is used to: build associated test environments based on a 3D test grid, including laboratory simulation environments and typical urban field environments; The control and acquisition module is used to: control the test target to perform simulated flight or actual flight along the route covered by the three-dimensional test grid in laboratory simulation environment and typical urban field environment, and simultaneously acquire the truth data provided by the corresponding truth system and the monitoring data output by the route monitoring network. The alignment calculation module is used to: align the collected true data with the monitoring data in time and space, and then calculate performance indicators including at least the monitoring probability, monitoring information update frequency, alarm latency, and monitoring positioning accuracy. The evaluation module is used to evaluate the performance of the airway surveillance network in complex low-altitude environments in cities where low-altitude public airways are located, based on the calculated performance indicators.

[0017] Based on the above scheme, the airway surveillance network testing and evaluation system of the present invention can be further improved as follows.

[0018] Furthermore, the three-dimensional test grid is defined based on the centerline of the low-altitude public airway and is divided into grid cells in the horizontal and vertical directions.

[0019] Furthermore, the area covered by the constructed 3D test grid includes various preset urban environment types, such as densely populated urban areas, high-rise canyon areas, open transition areas, and complex electromagnetic areas.

[0020] Furthermore, the alignment calculation module is specifically used for: In the real data and the surveillance data after time and space alignment, according to the set evaluation rules, the ratio of the effective surveillance data output by the airway surveillance network to the expected surveillance data is calculated, and this ratio is used as the surveillance probability. In the real data and the monitoring data after time and space alignment, the number of times the effective monitoring information output by the airway surveillance network for the same test target is updated within a unit time, and the number of updates is used as the monitoring information update frequency. In the real data and the monitoring data after time and space alignment, the horizontal error and vertical error between the monitoring data and the real data are calculated respectively, and used as the monitoring positioning accuracy; When the test target triggers a preset abnormal event, the first timestamp of the event and the second timestamp of the alarm information output by the airway surveillance network are recorded, and the alarm delay is calculated based on the time difference between the first timestamp and the second timestamp.

[0021] 3) In a third aspect, the present invention also provides an electronic device, the electronic device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor, so as to enable the electronic device to implement any of the above-mentioned airway surveillance network testing and evaluation methods.

[0022] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-described airway surveillance network testing and evaluation methods.

[0023] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below: Figure 1 This is a flowchart illustrating a method for testing and evaluating airway surveillance networks according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a route surveillance network testing and evaluation system according to an embodiment of the present invention. Detailed Implementation

[0025] 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.

[0026] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0027] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for testing and evaluating a route surveillance network, which includes the following steps: S1. Construct a three-dimensional test grid covering low-altitude public air routes. The three-dimensional test grid is delineated based on the centerline of the low-altitude public air routes and divided into grid cells in the horizontal and vertical directions. The area covered by the three-dimensional test grid includes various preset urban environment types, including dense urban areas, high-rise canyon areas, open transition areas, and complex electromagnetic areas.

[0028] S10. Obtain the digital route definition file of the target low-altitude public airway. This digital route definition file contains a series of coordinate points along the route centerline. Let the first point be the first point on the route centerline. The latitude and longitude of each reference point are Its height is .in, Indicates the first Longitude of a reference point Indicates the first The latitude of a reference point Indicates the first The file must also include the altitude of each reference point. The route width and the vertical altitude layer range are also required. This data forms the spatial reference for constructing the 3D test mesh. The acquired route centerline coordinate point sequence will be used. A smooth interpolation process is performed to create a continuous, smooth three-dimensional spatial reference line. This reference line represents the ideal center trajectory of the flight path in space. Reference points are set at fixed intervals along the smoothed center line. A normal plane is established at each reference point. Using this reference point as the origin of the local coordinate system, a tangential axis pointing in the direction of flight path movement, a normal axis pointing to one side of the flight path, and a vertical axis are defined. In the local coordinate system at this point, the offset distance along the normal axis can be expressed as… The vertical offset height can be expressed as .here, This represents the distance from the reference point along the normal axis. This indicates the altitude offset relative to a reference point. This local coordinate system is used to create a regular grid at each location along the route; the geographic coordinates of the grid cell center point can be obtained through this local coordinate system. Obtained through coordinate transformation.

[0029] S11. Within the normal plane of each reference point, divide the grid along the normal axis and the horizontal axis perpendicular to the normal axis, with that point as the origin. The reference size of the horizontal grid is set as follows: This means that starting from the reference point, extending a certain distance to both sides of the flight path, multiple rectangular cells are divided in 100-meter increments. In flight path sections with urban environments such as densely populated urban areas or high-rise canyon areas, due to the complex electromagnetic propagation environment, the grid size needs to be refined to a higher level. To more finely assess surveillance performance, the lateral boundaries of the grid should fully cover the effective surveillance width of the airway and extend appropriately outward to cover potential signal transition zones. Vertically, the grid should be layered at fixed altitude intervals, starting from the lowest safe altitude for low-altitude public airways and continuing to the highest operating altitude. The vertical layering height is set to... ,in The unit is meters. Each layer is a horizontal plane that intersects with the divided horizontal grid, thus dividing the entire flight path space into a series of three-dimensional cubic units with fixed height layers, namely three-dimensional test grid units.

[0030] S12. Prepare geographic information data and electromagnetic environment mapping data along the flight route in advance. Based on this data, classify each 3D test grid cell into its corresponding urban environment type region, namely, densely populated urban area, high-rise canyon area, open transition area, or complex electromagnetic area. A grid cell may be entirely in one environment type or located in a transition zone; in this case, it should be labeled based on the main influencing characteristics. This work ensures that the test grid can systematically cover various typical urban low-altitude environments that affect monitoring performance. Then, all generated 3D test grids are uniformly numbered and managed. A structured numbering rule is adopted. The unique number of a grid cell can be represented as... .in, It is an airway number, used to identify specific low-altitude public airways; It is the segment sequence number, indicating the segment index along the centerline of the flight path; It is a height layer identifier, corresponding to vertical layering. The code name; This is the grid cell number, representing the horizontal grid cell number within this section and at this height level. This numbering system ensures that each grid cell has a unique identifier, facilitating the recording, location, traceability, and retesting of test data.

[0031] S13. Output the digital definition file of the 3D test mesh. This file should contain the numbers of all mesh cells. The geographical coordinates of its eight vertices, the coordinates of its center point, and its corresponding altitude level. This document includes information such as the associated urban environment type and area labels. It will serve as the spatial framework and foundation for subsequent test environment construction, test route planning, test data recording, and performance evaluation.

[0032] Low-altitude public air routes are public air corridors designated within low-altitude and ultra-low-altitude airspace for simultaneous flight of multiple aircraft. They are comprehensively designed based on factors such as low-altitude geographic information constraints, the low-altitude flight environment, the characteristics of various transport vehicles, and urban air traffic demands. These air corridors are open to the public and shared by multiple aircraft types. Low-altitude public air routes constitute the backbone of urban low-altitude economic activities.

[0033] The centerline of a low-altitude public airway is a virtual spatial curve used to define the geometric center of the airway. It is composed of a series of longitude, latitude, and altitude coordinate points and serves as a spatial baseline for airway planning, airspace management, and performance evaluation. All definitions of the lateral width and vertical layering of the airway are extended with reference to this centerline.

[0034] Urban dense areas refer to urban areas with densely packed and continuously distributed buildings traversed by low-altitude public air routes. These areas present significant signal obstruction and multipath reflection interference, posing a challenge to radio-based surveillance methods and serving as a typical environment for testing the performance degradation of surveillance systems in complex terrain.

[0035] Among them, the "high-rise canyon zone" refers to urban airspace that resembles a canyon, where low-altitude public air routes pass through on both sides, formed by tall buildings. These areas are characterized by severe obstruction of global navigation satellite system signals and ground communication signals, which may lead to decreased positioning accuracy and communication link interruptions. They are key scenarios for assessing the reliability of navigation and surveillance signals.

[0036] The open transition zone refers to the airspace above urban edges or green areas and waterways where low-altitude public air routes pass through areas with sparse buildings and open terrain. These areas have relatively clean electromagnetic environments and good signal propagation conditions, and are usually used as benchmark environments for evaluating the background performance of surveillance systems.

[0037] Complex electromagnetic regions refer to specific areas traversed by low-altitude public air routes. These areas contain dense clusters of man-made radio sources operating on the same or adjacent frequencies, such as interference from 5G mobile communication enhancement technology bands and wireless fidelity technology interference. These areas are used to test the operational stability and anti-interference capabilities of surveillance systems in environments with strong electromagnetic interference.

[0038] S2. Based on the 3D test grid, a related test environment is constructed, which includes a laboratory simulation environment and a typical urban field environment. The specific implementation process is as follows: S20. Establish the overall goals and inputs for building the test environment. The inputs for this process are the digitally defined 3D test mesh file, as well as the defined performance indicators and test requirements. The goal is to create a technical environment system for each 3D test mesh cell, enabling effective and repeatable performance testing. This environment system must include a controlled laboratory simulation environment and a realistic typical urban field environment to ensure that the evaluation has both repeatable benchmark conditions and reflects actual operational challenges.

[0039] S21. Plan the hardware and software platform architecture of the laboratory simulation environment. The laboratory simulation environment needs to be built as a semi-physical simulation platform. The hardware needs to be configured with a high-performance computing server, a flight and signal simulator array, a high-precision clock synchronization system, and monitoring data acquisition and recording equipment. The software needs to deploy a 3D geographic information engine, a flight path and grid visualization system, a multi-target flight trajectory generator, a complex electromagnetic environment simulation module, and a sensor signal simulator. This platform must be able to receive and import digital definition files of 3D test grids, mapping the grid space to a virtual test airspace in the simulation engine.

[0040] S22. Based on the urban environment type region labeled for each 3D test grid cell, configure the corresponding physical and electromagnetic propagation models in the simulation engine. For grids labeled as densely populated urban areas, set up a dense building cluster model in the corresponding virtual space and enable multipath reflection and occlusion algorithms. For grids in high-rise canyon areas, set up towering tower models and configure global navigation satellite system signal occlusion parameters. For grids in complex electromagnetic regions, inject wireless fidelity technology or 5G enhanced communication technology interference signals with corresponding frequency bands and power. Simultaneously, set the corresponding atmospheric and meteorological model parameters according to the vertical height layer of the grid. Thus, each cell of the 3D test grid has a virtual copy in the simulation environment with precisely defined environmental characteristics.

[0041] S23. Let the total number of test targets simulated in the laboratory simulation environment be... . No. The simulated true trajectory of the target at time t The state can be described as a state vector. In this vector, Indicates longitude. Indicates latitude, Indicates altitude, Indicates speed, superscript Identifier One goal, superscript This represents the transpose of a vector. These trajectories are designed to systematically traverse various three-dimensional test grid cells, covering typical maneuvers such as straight lines, turns, ascents, descents, and intersections. The signal simulator operates based on the target state. and the type of grid environment in which it is located Calculate and generate the corresponding signal parameters. Among them, It is a categorical variable, for example The complexity of the entire simulation environment can be quantified as a function. , This represents the simulated electromagnetic interference intensity. The simulated signal is injected into the data interface or sensor front end of the low-altitude public airway surveillance network under test. Simultaneously, a high-precision trajectory generator within the simulation platform provides simulated trajectory data as the true values. The data acquisition system is responsible for synchronously recording all injected analog signals, monitoring data output by the system under test, and internal true data, and classifying and labeling the data according to the numbering of the three-dimensional test grid.

[0042] S24. A typical urban field environment needs to be constructed in a real urban space. Based on the actual geographical location of the low-altitude public airway corresponding to the 3D test grid, one or more physical areas in the city that can cover key segments of the airway should be selected as the field test site. This area must include various urban environment types, such as actual densely built-up areas, high-rise canyon terrain, relatively open transition zones, and known areas with strong electromagnetic interference. The selected field area should spatially coincide with or highly overlap with the actual airway segments defined by the 3D test grid.

[0043] S25. In the field testing area, a series of fixed and mobile facilities need to be deployed to support the testing. Key facilities include one or more Global Navigation Satellite System (GNSS) differential reference stations to provide real-time dynamic positioning services with centimeter-level accuracy, serving as the truth system for field testing. A time synchronization system needs to be deployed, such as a time server using a Precision Time Protocol (PTP) or Network Time Protocol (NAT) to ensure that the timestamp deviation of all data acquisition devices is no greater than 1 millisecond. Electromagnetic environment monitoring equipment needs to be deployed at key locations to continuously record the background noise and interference intensity of the test frequency band. In addition, a ground control station and an emergency landing zone need to be planned and established, and the test airspace needs to be physically isolated from the surrounding airspace by electronic fences.

[0044] S26. The predefined latitude and longitude coordinates and altitude information of the 3D test grid are loaded into the flight management software, ground monitoring software, and data recording system used for field testing. When the target UAV is actually flying in the field, its flight plan is designed to guide the UAV to sequentially fly over the center or feature points of these 3D test grid cells. The ground station software interface will display a real-time overlay image of the UAV's position and the 3D test grid, ensuring that the actual flight path precisely matches the predefined test grid spatial framework. The real-time dynamic positioning data provided by the truth system is denoted as... superscript Identifier Each field test target is numbered. The data logging system must ensure the timestamps from the monitoring system are accurate. With timestamps from the truth system Strict alignment, meeting the conditions , The allowable synchronization error threshold is typically set to 1 millisecond. All ground truth data acquired in the field. Both the monitoring data and the data itself are automatically or manually associated with the grid cell number that is being flown over.

[0045] S27. Regardless of whether the raw data is collected from a laboratory simulation environment or a typical urban field environment, it will be stored according to a unified data structure. This data structure is required to include fields such as the data source environment label, the corresponding 3D test grid cell number, timestamp, truth information, and original monitoring information. All data is stored according to the unique identifier of its respective grid cell. Indexing and storing data establishes a precise correspondence between data points and their three-dimensional spatial locations. Subsequent performance metric calculations and analysis algorithms all use the three-dimensional test grid cells as the basic processing unit, calling the corresponding data processing modules based on the environment labels of the input data, but outputting unified performance metric results. This completes the connection and integration from two independently constructed test environments to a performance evaluation system based on a unified three-dimensional test grid.

[0046] The test environment associated with the 3D test grid refers to a set of technical conditions, hardware facilities, software platforms, and physical locations specifically designed and constructed for the performance testing and evaluation of the low-altitude public airway surveillance network. This environment uses the 3D test grid as the spatial benchmark and evaluation framework, ensuring that all testing activities, data acquisition, and performance analysis are conducted based on this grid system. It includes a controlled and reproducible laboratory simulation environment and a typical urban field environment with realistic complexity, both of which jointly support a comprehensive and objective verification of the surveillance network's performance.

[0047] Among them, the laboratory simulation environment is a test platform that simulates the real operation scenarios of low-altitude public airways and complex urban environments under controlled indoor conditions through technologies such as computer simulation, hardware-in-the-loop, and signal simulation. It can accurately reproduce the regional characteristics of various urban environment types defined by the three-dimensional test grid and inject simulated aircraft targets and sensor signals for repeatable, efficient, and low-risk preliminary functional verification and performance evaluation testing of the low-altitude public airway surveillance network.

[0048] The typical urban field environment refers to a test site comprised of representative physical areas selected within real urban low-altitude airspace. This environment realistically features densely built-up areas, high-rise canyon areas, open transition zones, and complex electromagnetic regions traversed by low-altitude public airways. It is equipped with a high-precision ground truth measurement system, a time synchronization system, and data recording equipment. In this environment, real unmanned aerial vehicles (UAVs) are used as test targets, flying along a path planned by a three-dimensional test grid to evaluate the continuous monitoring performance and reliability of the low-altitude public airway surveillance network in a real, complex urban environment.

[0049] S3. In laboratory simulation environment and typical urban field environment, control the test target to conduct simulated flight or actual flight along the route covered by the three-dimensional test grid, and simultaneously collect the truth data provided by the corresponding truth system and the monitoring data output by the route monitoring network. S30. Develop a flight test plan based on a 3D test grid. This plan uses a digital 3D test grid file as the core input to plan the flight path of the test target. The plan explicitly requires that the flight trajectory of the test target must traverse all 3D test grid cells to be evaluated, especially the center point or characteristic location point of the grid cell. The flight path design covers typical operating modes such as straight cruise, horizontal turns, vertical climbs and descents, and convergence flights of multiple targets at route intersections, ensuring that flight activities can fully demonstrate the performance of the airway surveillance network in different spatial locations and dynamic scenarios.

[0050] S31. Start and initialize the semi-physical simulation platform of the laboratory simulation environment, load the 3D test mesh data and the corresponding urban environment model, and in the simulation software, set the number of simulated test targets and inject a flight trajectory generated according to the test plan for each target. Let the total number of simulated test targets in the laboratory simulation environment be... , No. The simulated true trajectory of the target at time t The state is .in, Indicates the first The goal is at any time longitude, Indicates latitude, Indicates altitude, Indicates speed, superscript Identify the target index, superscript This represents the vector transpose. The trajectory generator inside the simulation platform continuously generates... This serves as the data source for the truth system corresponding to the laboratory simulation environment. Simultaneously, the signal simulator relies on these truth states... and the environment type of the grid cell where the target is located. The system simulates and generates various surveillance source signals in real time, including radar echoes, 5G enhanced mobile communication (GSM) integrated sensing signals, broadcast automatic correlation surveillance (ALS) signals, and remote identification (RADI) signals. These simulated signals are then input into the sensors or data interfaces of the airway surveillance network under test. After processing these input signals, the airway surveillance network outputs fused surveillance data. The data acquisition system uses a high-precision synchronous clock as a reference to synchronously record the true data stream from the trajectory generator. And surveillance data streams from the airway surveillance network. Each data record is accompanied by a precise timestamp and is numbered with the simulated 3D test grid cell. Establish a connection.

[0051] S32. In the selected field test area, according to the flight test plan, operate a real UAV as the test target. The UAV is equipped with a high-precision real-time dynamic positioning receiver. This receiver receives correction signals from a ground differential reference station and calculates the UAV's high-precision position information in real time. Let the... Real-time dynamic positioning data of each field test target at time... The output is .in, Indicates longitude. Indicates latitude, Indicates height, superscript Identify the index of the target in the field. This constitutes the ground truth system data corresponding to a typical urban field environment. The UAV's flight control system guides the UAV to fly sequentially over the three-dimensional test grid cells according to a predetermined flight path. Meanwhile, various real sensors deployed in the field's airway surveillance network detect, identify, and track the test targets in the air, and output surveillance data through fusion processing. The field data acquisition station simultaneously receives the ground truth data transmitted from the UAV via a high-precision time synchronization network. And surveillance data output from the airway surveillance network. To ensure time comparability, the acquisition system continuously monitors and corrects the timestamps of each data source, ensuring that the timestamps from the airway surveillance network are consistent. With timestamps from the truth system satisfy The constraints here This is a preset synchronization error threshold, typically set to 1 millisecond. All collected data is also correlated with the 3D test grid cell number currently being flown by the drone. Bind.

[0052] S33. Regardless of whether the raw data stream is acquired in the laboratory or in the field, it must be preprocessed. Preprocessing includes checking the integrity of the data packets, parsing the data format, and arranging the ground truth data and monitoring data along a unified timeline. For each data point, its associated information should include at least: a precise timestamp and the corresponding 3D test grid cell number. Test target identifier, coordinates and status provided by the truth system or This includes monitoring information such as coordinates, speed, and identification information output by the airway surveillance network. This preprocessed and correlated data is archived in a structured database to prepare data for subsequent performance indicator calculations, ensuring that each data sample can be traced back to its specific environment, spatial location, and time of generation.

[0053] In this context, the truth system corresponding to the laboratory simulation environment refers to a module or device within the laboratory simulation testing platform used to generate or provide absolutely accurate spatiotemporal trajectory data of the test target in the simulated environment, serving as a benchmark reference. In implementation, it typically refers to a high-precision software trajectory generator capable of calculating the precise longitude, latitude, altitude, and speed of the test target at each simulation moment based on a preset flight plan. This data is then compared with the output results of the airway surveillance network in the simulated environment.

[0054] The true value system corresponding to a typical urban field environment refers to a physical measurement system used in real urban field tests to directly measure and acquire the real, high-precision spatiotemporal location information of the test target. It is usually composed of a real-time dynamic positioning receiver integrated on the test target UAV and a ground differential reference station, which can provide positioning data with centimeter-level accuracy as an objective benchmark for evaluating the performance of the airway surveillance network in a real environment.

[0055] The test target refers to the flying entity used to stimulate the network's response and serve as the data acquisition object during performance testing of the low-altitude public airway surveillance network. In a laboratory simulation environment, the test target is a computer-generated virtual aircraft; in a typical urban field environment, the test target is a real flying drone or other aircraft platform.

[0056] True value data refers to the test target state information that is deemed accurate and serves as the final comparison benchmark during performance testing and evaluation. It originates from the true value system corresponding to the laboratory simulation environment or the true value system corresponding to the typical urban field environment, and contains parameters such as the precise spatial coordinates and velocity of the test target at a specific point in time. It is the reference standard used to measure the accuracy of monitoring data in the calculation of all performance indicators.

[0057] The surveillance data output by the airway surveillance network refers to the status information of aerial targets released to the public after data fusion processing by various surveillance infrastructures deployed along low-altitude public airways, including radars, 5G mobile communication-enhanced integrated base stations, radio detection stations, optoelectronic detection equipment, remote identification receivers, automatic dependent surveillance broadcast (ADS-B) receivers, and BeiDou receivers, as well as low-altitude surveillance platforms. This data typically includes estimated target position, velocity, identification code, and timestamps, and is the direct object for evaluating the performance of the surveillance network.

[0058] The airway surveillance network refers to a comprehensive system built upon surveillance infrastructure such as radar, 5G mobile communication enhancement technology, integrated sensing, radio detection, photoelectric detection, remote identification, automatic dependent surveillance broadcast (ADS-B), and BeiDou navigation satellite system, as well as a low-altitude surveillance platform. It is used for the real-time positioning, identification, and control of test and non-test targets within low-altitude public airways. It is a key infrastructure for the manageable and controllable operation of low-altitude public airways, and its performance needs to be verified through testing and evaluation methods.

[0059] S4. After aligning the collected ground truth data with the monitoring data in terms of time and space, calculate performance indicators including at least the monitoring probability, monitoring information update frequency, alarm latency, and monitoring positioning accuracy.

[0060] The process of aligning the collected ground truth data with the monitoring data in terms of time and space is as follows: S40. The raw data streams from the truth system and the airway surveillance network each have their own timestamps, and the reference and precision of these timestamps may differ. The goal of time alignment is to establish a correspondence between the two sets of data on a unified time axis. During processing, a high-precision clock source is used as the reference time axis, denoted as... For each true data point, its built-in timestamp is denoted as . For each monitored data point, its built-in timestamp is... Through calibration and interpolation algorithms, all ground truth data points are mapped to moments on the reference time axis. The aligned truth sequence is obtained. Simultaneously, all monitored data points are mapped to corresponding times on the same reference time axis. The aligned monitoring sequence is obtained. This process ensures that for any reference time point... Each has a pair of time-matched truth state and monitoring state data, which are used for subsequent comparison and calculation.

[0061] S41. Ground truth data and surveillance data may use different spatial reference coordinate systems. For example, ground truth data may use the WGS-84 geodetic coordinate system, while some surveillance sensor outputs may use a local rectangular coordinate system. The goal of spatial alignment is to unify all coordinates to the same geographic coordinate system. During processing, a unified target coordinate system needs to be explicitly specified, typically the WGS-84 latitude, longitude, and elevation coordinate system. The coordinates of all ground truth data points are then transformed to this target coordinate system. Simultaneously, the coordinates of all surveillance data points are also transformed to the same target coordinate system using a corresponding coordinate transformation model. After spatial alignment, both ground truth points and their corresponding surveillance points are represented as latitude, longitude, and altitude in the same coordinate system, making the calculation of their spatial deviations directly comparable.

[0062] S42. After time and space alignment, theoretically at each reference time point Each time point should have a pair of truth points and monitoring points. However, in reality, due to different sampling rates or packet loss, data on one side may be missing. Therefore, data pairing and interpolation are necessary. For each time point with truth data but missing corresponding monitoring data, the monitoring status at that moment can be estimated using methods such as linear interpolation based on monitoring data from neighboring time points, and vice versa. This ultimately generates a complete data pair sequence. ,in It is the total number of points after pairing. Indicates time Aligned truth data, including longitude ,latitude and height ; This represents aligned monitoring data at the same time, including longitude. ,latitude and height This pairing sequence forms the basis for calculating all performance metrics.

[0063] In the real data and surveillance data after time and space alignment, according to the set evaluation rules, the ratio of the effective surveillance data output by the airway surveillance network to the expected surveillance data is statistically analyzed, and this ratio is used as the surveillance probability.

[0064] The established evaluation rules specifically define the statistical methods and judgment criteria used to calculate the surveillance probability, mainly including the following two: 1) Evaluation Rule 1: Based on the proportional statistics of the number of data points, this rule uses each data point output by the true value system as the counting benchmark for the expected amount of monitored data. During calculation, it is first determined whether each monitoring point meets the preset positioning accuracy threshold. For the test target, the horizontal error threshold... The vertical error threshold is 10 meters. The value is 15 meters. For each data pair with timestamp alignment, calculate the horizontal error between the monitored data and the ground truth data. and vertical error The horizontal error is calculated using the semi-versus formula: in, The average radius of the Earth This represents the difference between the true value latitude and the monitored latitude. This represents the difference between the true longitude and the monitored longitude. and For latitude and longitude in the true data, and To monitor latitude and longitude in the data.

[0065] The formula for calculating perpendicularity error is: ,in, Indicates the height in the true data. This indicates the height in the monitored data.

[0066] If a monitoring point simultaneously satisfies and If the condition is met, then the point is determined to be a valid monitoring point. The total number of valid monitoring points that meet the criteria throughout the entire evaluation sequence is counted and denoted as [the number of points]. The expected amount of monitored data is equal to the total number of true value points. According to this rule, the probability of monitoring... The calculation formula is: .

[0067] 2) Evaluation Rule Two: Coverage Statistics Based on Fixed Time Intervals. This rule uses a continuous time dimension as the counting benchmark for the expected amount of surveillance data. It is used to evaluate the ability of an airway surveillance network to continuously provide effective data within a specified time window, and is applicable to scenarios where the update frequency of surveillance information differs from the update frequency of the truth system. During calculation, the entire test time axis is first divided into a series of continuous and non-overlapping fixed time intervals, and the length of each time interval is denoted as... (For example, 1 second). The total number of time intervals is denoted as... For each time interval, check whether there exists at least one location within that time window that satisfies the aforementioned preset positioning accuracy threshold (i.e., simultaneously satisfies...). and ) Valid monitoring points. If they exist, the time interval is considered a valid coverage interval. Count the number of all valid coverage intervals, denoted as . The expected amount of monitored data is the total number of time intervals. According to this rule, the monitoring probability... The calculation formula is: .

[0068] The calculations under both rules can be performed independently using a single three-dimensional test grid cell as the spatial unit, thereby enabling a localized and refined evaluation of the performance of the airway surveillance network.

[0069] It should be noted that when the test objective focuses on evaluating the instantaneous response and positioning accuracy of the airway surveillance network to each sampling moment of the ground truth system, evaluation rule one should be used. This rule applies when the ideal update frequency of the airway surveillance network is not lower than the update frequency of the ground truth system, or when the tester is primarily concerned with the system's ability to accurately capture data at each discrete ground truth sampling point. Evaluation rule one directly quantifies the system's ability to successfully output accurate data at the time it should be reported, sensitively reflecting issues such as single-point data loss or positioning errors, and is a direct way to evaluate the system's basic positioning accuracy and data integrity. When the test objective focuses on evaluating the airway surveillance network's ability to continuously provide effective surveillance information at a specified time granularity, especially when its data update cycle differs from that of the ground truth system, evaluation rule two should be used. This rule is particularly important when the minimum update cycle of the airway surveillance network (e.g., 1 second for a 5G enhanced mobile communication integrated sensing base station) is significantly longer than the update cycle of the ground truth system (e.g., 0.1 seconds for a real-time dynamic positioning system). Evaluation Rule 2 avoids underestimating system performance due to inherent differences in update rates, instead assessing whether the system can provide effective data in each of its own output cycles. This aligns better with the actual needs of operations management, focusing on whether the system can provide at least one reliable target position per second or within each specified time window, thereby supporting continuous track tracking and safety monitoring.

[0070] Among them, in the time- and space-aligned ground truth data and surveillance data, the number of times the airway surveillance network updates the valid surveillance information output for the same test target per unit time is counted, and this number of updates is used as the surveillance information update frequency. Specifically: The surveillance information update frequency is used to evaluate the continuity and real-time performance of airway surveillance network (ESN) data output. The calculation requires analyzing continuous surveillance data streams targeting the same test target, extracting all surveillance data points belonging to the same test target from the aligned and paired surveillance data sequences, and arranging them chronologically. Valid surveillance information refers to complete data records output by the ESN that include valid timestamps and target status (e.g., position). Statistics are compiled per unit time. The number of times the airway surveillance network updates valid surveillance information for the same target within a timeframe (e.g., 1 second or 1 minute) is denoted as [the number of times the network updates the information]. Monitoring information update frequency The calculation formula is: For example, if the system outputs two valid monitoring points within a 1-second time window, the update frequency is 2 Hz. During calculation, a stable tracking segment is typically selected to avoid periods of target loss or immediate acquisition, and the distribution of the update frequency is analyzed using a grid-based approach.

[0071] Specifically, in the time- and space-aligned ground truth data and monitoring data, the horizontal and vertical errors between the monitoring data and ground truth data are calculated respectively, which are used as the monitoring positioning accuracy. Surveillance positioning accuracy is used to quantify the accuracy of the position information output by the airway surveillance network. It is achieved by calculating the spatial error of each data pair, including the horizontal error. The calculation uses the semi-versus formula described above. Perpendicular error The calculation uses the formula for the absolute value of the altitude difference described above. For the entire evaluation segment or a specific three-dimensional test grid cell, the statistical characteristics of the horizontal and vertical errors of all data pairs are typically calculated, such as the mean, root mean square error, and 95th percentile error. These statistics collectively reflect the positioning accuracy performance of the airway surveillance network in both the horizontal and vertical directions.

[0072] The number of monitoring points that meet the preset positioning accuracy threshold refers to the total number of monitoring data points output by the airway surveillance network, after time and space alignment, whose horizontal position deviation and vertical height deviation from the corresponding ground truth data points do not exceed the limits specified in the technical specifications or test outline during performance evaluation calculations. This number is the numerator in the formula for calculating the monitoring probability and directly reflects the availability of the airway surveillance network's output results.

[0073] Specifically, when the test target triggers a preset abnormal event, the first timestamp of the event and the second timestamp of the alarm information output by the airway surveillance network are recorded. The alarm delay is calculated based on the time difference between the first and second timestamps. The specific implementation process is as follows: 1) Before the test begins, a series of logical rules and thresholds for triggering alarms are pre-defined in the Low-Altitude Public Airway Integrated Management Service Platform. These rules and thresholds collectively define the preset abnormal events. For example, Rule 1: When the flight trajectory of an object (test target or other non-test target, etc.) exceeds the boundary of the designated electronic fence or low-altitude public airway; Rule 2: When a test target intrudes into a pre-defined no-fly zone, danger zone, or restricted zone; Rule 3: When the three-dimensional spatial distance between two test targets is less than the set minimum safe distance, such as 150 meters; Rule 4: When the lateral deviation between the actual flight track of the test target and the pre-declared flight plan track is greater than a set value, such as 150 meters; Rule 5: When the vertical deviation between the actual flight altitude of the test target and the planned flight altitude is greater than a set value, such as 60 meters; Rule 6: When a test target exhibits reverse flight behavior in a one-way airway. All these rules and their threshold parameters must be clearly defined in the test outline and configured in the rule engine of the integrated management service platform before the test.

[0074] 2) To accurately measure alarm latency, it is essential to ensure that the test target's ground truth system, airway surveillance network sensors, integrated management service platform, and data recording equipment are all synchronized to a unified, high-precision time source. Typically, a time server using a Precision Time Protocol (PTP) or Network Time Protocol (NAT) is employed to control the time synchronization error of the entire test system to within 1 millisecond. An independent end-to-end data recording system is deployed, equipped with multiple high-precision input channels for synchronously monitoring and recording target trajectory data from the ground truth system, raw surveillance data streams from various sensors in the airway surveillance network, and all alarm information logs output from the integrated management service platform's network interface. All recorded data packets must be timestamped with microsecond-level precision from the same time base.

[0075] 3) In a laboratory simulation environment or a typical urban field environment, control the test target to fly along a predetermined test route. During specific phases of flight, use flight control commands to intentionally cause the test target's behavior to violate a pre-defined rule, thereby triggering a pre-set abnormal event. For example, in a laboratory simulation, modify the simulated target's trajectory to make it fly outside the electronic fence boundary; in the field, control a real drone via a ground station to make it deviate from the planned route by more than 150 meters. This triggering process is planned and controlled.

[0076] 4) When a preset abnormal event is triggered, the data acquisition system responsible for recording the test target's true data and flight status needs to immediately generate an event marker. This marker contains two key pieces of information: the type of event and the precise time the event occurred. The time of the event, i.e., the "first timestamp," should be determined based on the actual state changes of the test target. The most accurate method is for the truth system to continuously determine whether the target's state meets the abnormal conditions. Assume the truth system at time... The target state is determined to be normal, and the next sampling time is... If the target state meets the abnormal conditions, then the time of the event can be determined. Determined as Alternatively, it can be determined through interpolation as the precise moment when the state just crosses the threshold. It is recorded in real time to the data stream.

[0077] Meanwhile, the end-to-end data recording system continuously monitors the network traffic output by the integrated management service platform. Upon detecting an anomaly, the platform, after internal data processing, rule matching, and alarm generation, broadcasts a structured alarm message through a designated application programming interface or message queue. This alarm message typically includes the alarm type, target identifier, event location, severity level, and timestamp. When the data recording system captures this complete alarm message network packet, it immediately assigns it a received timestamp based on the same time base, denoted as [timestamp name missing]. .this This is known as the "second timestamp".

[0078] 5) Obtain and Then, first calculate the original time difference. However, this difference includes the time required for the alarm information to be transmitted to the recording system interface via the internal network after it is generated on the platform, i.e., network transmission latency. To accurately measure the platform's processing and response speed, this latency needs to be compensated for. (Network transmission latency) The alarm latency can be estimated by measuring the round-trip time of the network path from the platform's alarm output interface to the recording system multiple times before and after the test and taking the average. The calculation formula is: ,in, Indicates the total alarm delay. Indicates the precise time when the triggering event occurred. This indicates the precise time when the data recording system received the platform's alarm message. This represents the estimated network transmission latency. For the test target, the calculated... It must meet the requirement of being less than or equal to 5 seconds.

[0079] 6) After completing the triggering and delay calculation of an abnormal event, the data from the entire process needs to be correlated and verified. Verification includes: confirming that the triggered event type matches the type in the platform alarm information; verifying that the target identifier in the alarm information matches the target identifier of the triggered event; and checking... and Check if the timestamp comes from the synchronized time base. After verification, archive the complete record of this test, including: the type of abnormal event, the triggering location (associated 3D test grid cell number), and the first timestamp. Second timestamp Estimated network latency Calculated alarm delay And the results of determining whether the threshold requirements are met. These data will serve as a key basis for evaluating the anomaly detection and response capabilities of the airway surveillance network.

[0080] Pre-defined abnormal events refer to specific flight activities or states that, once they occur during low-altitude public airway operations, indicate a deviation from safe or planned operations and require immediate detection and alerting from the monitoring system. These events are based on safety rules and operating procedures, and typical examples include aircraft flying out of designated airspace, intruding into no-fly zones, maintaining a distance from other aircraft below safety standards, and significant deviations from the planned flight path. In performance testing, these events are intentionally triggered to verify the anomaly detection and alerting capabilities of the airway surveillance network.

[0081] Alarm latency refers to the time elapsed from the precise moment a pre-defined abnormal event actually occurs to the moment the Low-Altitude Public Airway Integrated Management Service Platform generates and outputs the corresponding alarm information. It quantifies the processing and response speed of the airway surveillance network from detecting an anomaly to outputting alarm information, and is a key indicator for evaluating the system's real-time performance and timeliness. A shorter alarm latency means the system can alert to potential risks more quickly, allowing more time for safety control decisions.

[0082] This invention designs performance indicators for a low-altitude public airway surveillance network. These indicators are used to comprehensively evaluate the network's ability to detect, track, locate, and perceive anomalies in complex urban low-altitude environments. The designed performance indicators include at least surveillance probability, surveillance information update frequency, surveillance positioning accuracy, and alarm latency, aiming to provide objective and quantitative evaluation criteria for the planning, construction, operation management, and technical optimization of the airway surveillance network. Specifically: 1) Design a surveillance probability performance index to evaluate the ability of a route surveillance network to successfully capture and continuously output effective surveillance data. The surveillance probability is calculated based on defined evaluation rules, which specify how to statistically determine the ratio of effective surveillance data to expected surveillance data from temporally and spatially aligned ground truth data and surveillance data. Specifically, two calculation rules are included: Rule 1 is based on the proportion of data points, calculating the percentage of effective surveillance points meeting a preset positioning accuracy threshold out of the total number of ground truth points; Rule 2 is based on coverage statistics at fixed time intervals, calculating the percentage of windows containing at least one effective surveillance point within consecutively divided time windows out of the total number of windows. Ground truth data is provided by the corresponding ground truth system and serves as the sole benchmark for evaluation.

[0083] 2) Design a performance index for the monitoring information update frequency to evaluate the continuity and real-time performance of the monitoring data output by the airway surveillance network to the same test target. This index is calculated by counting the number of times the airway surveillance network updates the valid monitoring information to the same test target per unit time. Valid monitoring information refers to data records that include a valid timestamp and complete target status. During testing, it is necessary to ensure that all monitoring data includes accurately synchronized timestamps to accurately calculate the update rate and thus evaluate the continuous tracking capability of the monitoring link.

[0084] 3) Design monitoring and positioning accuracy performance indicators to evaluate the deviation between the output coordinate information of the airway surveillance network and the true data, including two dimensions: horizontal error and vertical error. The test adopts the true value comparison method, which calculates the values ​​based on the time- and space-aligned data sequences. The horizontal error is calculated using the semi-versus formula to calculate the great circle distance between the true point and the monitored point; the vertical error is obtained by calculating the absolute difference in their heights. The calculation results are used to quantify the positioning accuracy of the airway surveillance network in the horizontal and vertical directions.

[0085] 4) Design an alarm latency performance metric to evaluate the speed at which the airway surveillance network detects and responds to preset anomalies. Preset anomalies include test targets triggering route deviations, altitude deviations, reverse flight, intrusions into restricted airspace, and distances from other targets falling below the safe separation. This metric is obtained by recording the first timestamp of the anomaly occurrence and the second timestamp of the alarm information output by the airway surveillance network, calculating the time difference between the two, and compensating for network transmission delays when necessary. Alarm latency directly reflects the timeliness of the system's detection and reporting of security risks.

[0086] S5. Based on the calculated performance indicators, evaluate the performance of the airway surveillance network in the complex low-altitude environment of cities where low-altitude public airways are located.

[0087] S50. The evaluation process unfolds using a 3D test grid as the basic spatial unit. The performance indicators of each data pair calculated previously, including monitoring probability, monitoring information update frequency, horizontal error of monitoring positioning accuracy, vertical error of monitoring positioning accuracy, and alarm latency, are aggregated based on the unique ID of their associated 3D test grid unit. For each 3D test grid unit, the indicators calculated from all sampling points within it are spatially aggregated and statistically analyzed. For example, for monitoring probability, the calculation result within a 3D test grid unit is a summary value; for monitoring positioning accuracy, the horizontal and vertical error sequences of all sampling points within the unit are collected; for alarm latency, the latency calculation results corresponding to all preset abnormal events triggered within the unit are collected. Let the set of sampling point indices belonging to 3D test grid unit G be I_G, then the average level error of this 3D test grid unit can be calculated as: ,in, This represents the number of sampling points in set I_G. Similarly, the average vertical error of this cell can be calculated. Monitoring probability within the unit Average monitoring information update frequency and average alarm latency This step regularizes the discrete trajectory point performance data onto three-dimensional test grid cells with well-defined spatial boundaries, forming a spatialized performance dataset.

[0088] S51. Each 3D test grid cell is labeled with its corresponding urban environment type during construction, such as densely populated urban area, high-rise canyon area, open transition area, or complex electromagnetic area. During evaluation, it is necessary to extract the index data of all 3D test grid cells within the same urban environment type area for comprehensive analysis. For example, analyzing the distribution of monitoring probabilities for all 3D test grid cells within a densely populated urban area. The overall average monitoring probability and its standard deviation for that urban environment type area can be calculated: in, This represents the total number of three-dimensional test grid cells in urban environment type areas that are densely populated urban areas. This indicates the environment type to which the 3D test grid cell G belongs. The same analytical method is applied to the monitoring information update frequency, monitoring positioning accuracy, and alarm latency. Through this analysis, the performance differences of the airway surveillance network in complex low-altitude urban environments with varying characteristics can be clearly revealed. For example, it can be clearly shown that the system has high and stable positioning accuracy in open transition zones, while the monitoring information update frequency may decrease in high-rise building and canyon areas, and alarm latency may increase in complex electromagnetic areas.

[0089] S52. To visually represent the evaluation results, the aggregated 3D test grid cell performance data needs to be mapped back to 3D geographic space. Using a Geographic Information System (GIS) or specialized visualization software, each 3D test grid cell is color-coded according to its performance index value. For example, a gradient from green to red represents monitoring probability from high to low, different shades of blue represent different levels of positioning error, and a gradient from yellow to purple represents alarm latency from short to long. Horizontal errors can be... Vertical error and alarm delay The rendered images are displayed in layers or side-by-side. Simultaneously, the flight path centerline, city building outlines, and the boundaries of different urban environment types are overlaid on the map. This visualization clearly shows the spatial distribution of performance bottlenecks along the flight path; for example, it can identify which specific turning point or high-rise area experiences a significant decrease in system monitoring probability, excessive positioning errors, or slow alarm response.

[0090] S53. Define minimum threshold requirements for each performance indicator. The evaluation process requires a compliance assessment of each indicator for every 3D test grid cell, every urban environment type, and even the entire flight path. For surveillance probability, the test objective requires a surveillance probability greater than or equal to 99%. During the assessment, check the surveillance probability of each 3D test grid cell. Does it meet the requirements? For the monitoring information update frequency, the test target requires an update frequency greater than or equal to 1 time per second. During the judgment, check the average update frequency of each 3D test mesh unit during the stable tracking segment. Whether the standards are met. For monitoring and positioning accuracy, the test target requires a horizontal error of less than or equal to 10 meters and a vertical error of less than or equal to 15 meters. During judgment, check whether the errors of all sampling points within each 3D test grid cell meet the threshold, or whether the 95th percentile error meets the requirement. For alarm latency, the test target requires an alarm latency of less than or equal to 5 seconds. During judgment, check whether the calculated alarm latency corresponding to each preset abnormal event triggered within each 3D test grid cell meets the threshold. The system must also meet the threshold requirements for interface consistency, system reliability, and electromagnetic compatibility. The evaluation report must clearly list the judgment results for each indicator.

[0091] S54. Based on the individual pass / fail criteria, a comprehensive evaluation is required, which must incorporate the validity criteria for the 3D test grid. For example, it is stipulated that within a single altitude layer, the number of failed 3D test grid cells deemed not to meet any core performance indicator threshold should be less than or equal to 10, and all failed 3D test grid cells cannot be continuously distributed for more than two grid lengths along the flight path extension direction. During the evaluation, it is necessary to count the number of failed 3D test grid cells at each altitude layer that do not meet any core indicator threshold, such as monitoring probability, monitoring information update frequency, monitoring positioning accuracy, or alarm latency, and their distribution continuity. If the number of failed 3D test grid cells at a certain altitude layer exceeds 10 or there are more than 3 consecutive failed 3D test grid cells, the monitoring capability of that altitude layer is deemed unqualified. Simultaneously, it is necessary to check whether all 3D test grid cells at key nodes are valid grids. Key nodes include the 3D test grid cells corresponding to flight path intersections, turning points, and access / departure points. By cross-analyzing the spatial distribution of failed 3D test grid cells, the associated urban environmental types, and the specific non-compliant performance indicators, the root cause of system performance degradation can be diagnosed. For example, if the failed 3D test grid cells are concentrated in complex electromagnetic regions, and the main manifestations are interrupted monitoring information update frequency and excessive alarm delay, then the problem may stem from insufficient electromagnetic interference resistance of the system or excessive data processing link delay.

[0092] S55. The final performance evaluation results must be presented in a standardized report format. The report should include: a test overview, test environment and conditions, definition of the 3D test grid, data acquisition and processing methods, detailed calculation results and spatial distribution maps of various performance indicators, performance profile analysis divided by urban environmental type, summary of compliance judgments based on standard thresholds, comprehensive conclusions on overall system performance, a list of failed 3D test grid cells and root cause diagnosis analysis, and improvement suggestions. All conclusions in the report must be based on calculated performance indicator data such as monitoring probability, monitoring information update frequency, monitoring positioning accuracy, and alarm latency, as well as visualization charts and standard clauses, to ensure the traceability, reproducibility, and objectivity of the evaluation process and conclusions.

[0093] In the above embodiments, for the cooperative objective, i.e., the test objective, the performance evaluation of the airway surveillance network must meet the following specific requirements: the surveillance probability should be greater than or equal to 99%; the surveillance information update frequency should be greater than or equal to 1 time per second; in terms of surveillance positioning accuracy, the horizontal error should be less than or equal to 10 meters, and the vertical error should be less than or equal to 15 meters; the alarm delay should be less than or equal to 5 seconds. These requirements constitute a quantitative benchmark for the basic performance of the airway surveillance network in complex low-altitude urban environments. Meanwhile, in practical applications, the above specific requirements can be adjusted in a targeted manner according to the industry standards and technical specifications on which the specific test is based, the urban environmental scenario category of the actual airway operation, and the specific assessment requirements specified in the detailed test outline prepared by the test task party. For example, for airway sections in urban core areas with extremely high building heights, a stricter vertical error threshold can be specified; for logistics airways with high real-time requirements, a higher surveillance information update frequency can be specified; or for systems using new technologies, supplementary indicators can be introduced into the approved technical specifications. This adjustment ensures that the performance evaluation both follows general benchmarks and accurately matches the actual technical conditions and safe operation requirements of specific projects.

[0094] The above technical solution also includes the determination of the effectiveness of the three-dimensional test grid. Specifically, if the number of failed grid cells that fail to meet the threshold requirements of any core performance indicator within a single altitude layer does not exceed 10, and all failed grid cells do not have a distribution of more than two consecutive grid lengths in the route extension direction, then the grid at that altitude layer is deemed to be valid. If the grid cell containing a critical route node is determined to be a failed grid, then the monitoring capability of the route segment associated with that node is directly deemed unqualified. Critical nodes include route junctions, turning points, and access / departure points. The specific implementation process is as follows: 1) The input to the evaluation process is the evaluation results of all 3D test mesh elements whose performance indicators have been calculated and stored in the database. For each mesh element, its unique identifier is... The calculated performance metrics include, but are not limited to, monitoring probability. Positioning accuracy horizontal error Positioning accuracy vertical error Update frequency Additionally, a predefined list of critical nodes is required, which explicitly lists one or more 3D test mesh cells corresponding to all intersections, turns, and access / departure points along the flight path. This list serves as the basis for determining critical nodes.

[0095] 2) Traverse each 3D test mesh cell in the database. Performance metrics data. Thresholds for each core performance metric are used as the judgment criteria. For example, for the test target, the monitoring probability threshold... Horizontal positioning error threshold Vertical positioning error threshold Update frequency threshold If any core metric of a grid cell fails to meet its corresponding threshold requirement, the grid cell is considered a failed grid cell. A status flag is assigned to all failed grid cells, for example... And record the specific indicators that did not meet the standards.

[0096] 3) The 3D test mesh has clear layers in the vertical direction, such as height layers. The determination process needs to be performed separately for each altitude level. For each altitude level... Select the set of cells belonging to this layer from all grid cells. Then, all mesh cells in the failed state are identified from this set, forming the failed mesh set for this layer. Count the number of elements in this set, denoted as . That is, the total number of failed mesh cells within that height layer.

[0097] 4) To check whether the failed grid cells are continuously distributed, it is necessary to reconstruct the sequential position of each grid cell in the flight path extension direction based on the grid cell number or spatial coordinates. For a certain altitude layer... Sort all grid cells (including active and inactive) at this layer according to the projected distance of their center points along the flight path centerline, resulting in an ordered sequence. Check if grid cells marked 'inactive' appear consecutively in this sequence. Define "consecutive distribution length" as the number of grid cells contained in the largest consecutive segment formed by adjacent inactive grid cells in the ordered sequence. For example, in a grid sequence arranged in flight path order, if grid cells 5, 6, and 7 are all inactive, then there exists an inactive segment with a consecutive distribution length of 3. This step requires calculating for each altitude layer. Maximum continuous failure length .

[0098] 5) For each height level The first rule is applied for judgment. This rule contains two conditions that must be met simultaneously: Condition 1, the number of failed mesh cells. Condition 2, maximum successive failure distribution length . use and Make a judgment. If both conditions are met, then the height level is determined. The mesh validity is "qualified". If any condition is not met, the layer at that height is deemed "qualified". The mesh validity is "unqualified". The judgment result is recorded as follows: .

[0099] 6) Read the prepared list of critical nodes. For each critical node mesh cell listed in the list... Query the marked status If any critical node grid cell is in the 'failed' state, the surveillance capability of the entire route segment associated with that critical node is directly deemed 'unqualified'. The scope of the associated route segment needs to be predefined; for example, it could be the route interval to which all grid cells within a certain distance before and after the critical node belong. This determination is independent of the altitude layer determination, has the highest priority, and once it occurs, it overrides the validity conclusions of other grid cells in that route segment.

[0100] 7) Summarize the validity determination results for each altitude level. The report should clearly list the number of failed grid cells at each altitude level, and the resulting critical route segment determinations. Maximum continuous failure length The results include the qualification conclusions of each level; the inspection results of all key node grids and their impact on airway segments; and finally, an overall evaluation of the effectiveness of the three-dimensional test grid within the entire low-altitude public airway test range, clearly indicating which altitude layers or airway segments failed the test, providing accurate spatial positioning basis for subsequent system rectification or optimization.

[0101] The technical solution of the present invention will be further described through the following embodiments, specifically including the following contents: (1) Detailed implementation process of test environment, conditions and mesh construction: The test environment construction, condition setting and three-dimensional test mesh division in the embodiments of this invention are the basis for subsequent testing and evaluation, and are carried out according to logical steps: 1) To ensure the accuracy and comparability of test results, the laboratory simulation environment must be constructed to closely match the operational characteristics of urban low-altitude public airways. The laboratory simulation environment needs to be equipped with a signal simulator, a truth positioning system, and a data processing module. This environment should be able to simulate high-density aircraft operation scenarios and complex electromagnetic environments, and should have the function of injecting simulated flight trajectories into the airway surveillance network and recording its output surveillance data. Specifically, the signal simulator is used to generate various surveillance source signals, including simulated radar, 5G enhanced mobile communication technology (GSM) integrated sensing, radio detection, photoelectric detection, remote identification, automatic dependent surveillance broadcast (ADS-B), and BeiDou receivers; the truth positioning system in the simulation environment refers to a high-precision software trajectory generator that provides the reference trajectory for simulated targets; the data processing module is responsible for scenario-driven operations, data synchronization, and recording.

[0102] 2) The typical urban field verification environment should be selected in a real urban space, and the selected area must be a typical urban scenario traversed by the low-altitude public airway. This area needs to be equipped with a Global Navigation Satellite System (GNSS) differential reference station or a measurement system with equivalent precision to provide centimeter-level accuracy ground truth data. Simultaneously, this environment must have electromagnetic environment monitoring capabilities to record background noise and interference intensity. The scope of the typical urban field environment should cover the start and end points, turning sections, intersections, and boundary areas of the airway to reflect multi-target operations and urban interference characteristics, thereby verifying the continuous monitoring performance and reliability of the airway surveillance network in complex low-altitude environments. The selected typical urban field area must include multiple preset urban environment types, specifically including densely populated urban areas characterized by significant signal obstruction and multipath reflection interference; high-rise canyon areas characterized by severe obstruction of GNSS signals and ground communication signals; open transition areas, serving as a comparative benchmark environment for evaluating the background performance of the surveillance system; and complex electromagnetic areas characterized by interference from 5G mobile communication enhancement technology bands and concentrated interference from wireless fidelity technology. The comprehensive coverage of these environmental types ensures the adequacy of the testing and the representativeness of the evaluation results.

[0103] 3) The test scenarios should cover at least the following typical operating conditions, and nighttime test scenarios may be added if necessary: ​​densely built-up areas in urban low-altitude regions; low-altitude airway intersections and turning sections; electronic fence boundaries and sensitive airspace edges; altitude-layered coverage areas, i.e., airspace from 100 meters to 600 meters; normal weather conditions such as clear skies, and interfering weather conditions such as rainfall. The flight paths of the test targets need to be designed to cover a variety of typical maneuvers, including straight lines, turns, climbs, descents, and intersections.

[0104] 4) Performance testing of the low-altitude public airway surveillance network must meet a series of prerequisites: the true position of the test target must be provided by a real-time dynamic positioning system, a differential global navigation satellite system, or other equivalent high-precision measurement system; the collected surveillance data must be clock-synchronized with the true data, and the spatial coordinate system and sampling rate must be matched; the data recording and storage system throughout the testing process must support complete data backtracking, recalculation, and consistency verification. Furthermore, the test should be organized and implemented by a third-party testing organization or authorized technical unit with low-altitude system evaluation capabilities. A detailed test outline must be prepared before the test, clearly defining the test airspace, test routes, data acquisition frequency, test grid division scheme, target type, meteorological conditions, data accuracy requirements, and adjudication rules. The testing party must be equipped with at least one real-time dynamic positioning true data system, a clock synchronization system, and a full-link data recording system. The clock synchronization system must adopt a network time protocol or a precise time protocol, and its synchronization accuracy must reach or exceed 1 millisecond to ensure the accuracy of timestamp alignment.

[0105] 5) To achieve spatial coverage assessment and comparability determination of low-altitude public airway surveillance capabilities, a three-dimensional airway-centered gridded test framework is adopted. The construction principle is to establish a three-dimensional test grid covering the horizontal and vertical directions of the airway, based on the centerline of the test airway. The grid division must cover the effective surveillance area of ​​the entire airway, before and after turning points, intersection sections, take-off and landing extension areas, and risk-sensitive areas, achieving full coverage of critical sections of airway operation and coverage of non-critical sections at sampling intervals. The specific grid division method is as follows: the baseline size of the horizontal grid is 100 meters by 100 meters. In complex urban areas with obstructions, i.e., densely populated urban areas and high-rise canyon areas, the grid can be densified to 50 meters by 50 meters, but this must be declared in the test plan and the densification rules must be consistent; the vertical height layering starts at 100 meters and ends at 600 meters, with layering at 100-meter intervals, i.e., the height layer set is... Meters. All divided route grids must be uniformly numbered according to the format "Route Number - Segment Number - Altitude Layer - Grid Sequence Number", and their unique identifier is represented by... This ensures the uniqueness, traceability, and reproducibility of each grid cell.

[0106] 6) During the test, the 3D test grid should cover 95% or more of the flight path length, and there should be no more than three consecutive grid sections not covered by the flight test. For flight path segments such as curves, intersections, crossings of sensitive airspace, vertical crossings of flight paths, or sudden altitude changes, the sampling density of the grid for these segments should be doubled compared to the baseline requirement. For a single grid cell, at least three valid independent samples should be taken. Trajectories of the same target repeatedly flying over the same grid are counted as only one independent sample. The time interval between each independent sample should be greater than or equal to 30 seconds to avoid data correlation bias.

[0107] 7) For each test grid, at least the following information should be recorded for each sampling: coordinates of the four corner points of the grid, coordinates of the center point, and the altitude layer to which it belongs; the time stamp range of the start and end of the sampling, and the meteorological conditions during the sampling period; the type of test target, the number of targets, and the unique identifier of the flight trajectory; the true value data source information and the original monitoring data message; the calculation results of each performance index, the deviation value from the true value, and the judgment conclusion on whether the sampling meets the index requirements.

[0108] 8) The altitude of all flight tests must be strictly controlled within the range of 120 meters to 600 meters; an emergency landing zone must be set up in advance, safety observers must be arranged, and it must be ensured that the UAV can automatically return to base if the control link is lost; all test airspaces must be equipped with electronic fences and physically isolated from the surrounding airspace; before the test, the flight registration form, flight plan and valid flight insurance certificate must be pushed to the UAV traffic management system in real time.

[0109] (2) Specific implementation of the performance testing method for the low-altitude public airway surveillance network. The specific implementation process is as follows: 1) The main objects of the performance test of the low-altitude public airway surveillance network include the following: test targets, mainly including aircraft with remote identification and automatic dependent surveillance broadcast capabilities, as well as aircraft that can actively report their status to the low-altitude surveillance platform; surveillance fusion platform, which includes core capabilities such as data reception, multi-source information fusion tracking, anomaly alarm and standardized data output.

[0110] 2) A complete test of the airway surveillance network should include laboratory simulation verification and field testing in typical cities. The specific implementation process is as follows: 1) Laboratory Simulation Testing: Using a constructed semi-physical simulation platform, flight scenarios and test targets under various urban environments are simulated to complete the preliminary testing and verification of the basic performance of the airway surveillance network. 2) Field Verification Testing: In selected typical urban field environments, real UAVs are used as test targets to conduct realistic tests on the performance of the low-altitude public airway surveillance network in complex actual environments. Data output from high-precision differential positioning equipment is used as the ground truth system benchmark, and monitoring data and environmental parameter data output by the airway surveillance network are collected simultaneously. Subsequently, timestamp alignment between the ground truth data and monitoring data, spatial coordinate system unification, and outlier data removal are completed. Then, based on the specified performance index thresholds and statistical algorithms, performance calculations and result judgments are completed, and a formal test report is finally issued.

[0111] (3) Testing and Calculation Methods for Core Performance Indicators: The performance testing index system for the low-altitude public airway surveillance network aims to comprehensively evaluate its detection, tracking, positioning, and anomaly detection capabilities. Specifically, it includes the testing of the following key indicators: 1) Surveillance probability is used to evaluate the ability of a route surveillance network to successfully capture and continuously output effective surveillance data in the presence of a target. The calculation of surveillance probability is based on the established evaluation rules. The test steps include: a) generating a test route according to the approved or preset route, which must include straight-ahead, turning, merging, and airspace boundary flight segments; b) the UAV flies to each altitude layer according to the set route, and begins recording data after entering a stable level flight state; c) collecting real-time dynamic positioning ground truth data and surveillance data output by the route surveillance network under a synchronized time reference; d) completing spatiotemporal alignment, including timestamp alignment, coordinate system unification, data interpolation matching, and outlier removal; e) calculating the surveillance probability according to the selected evaluation rules.

[0112] If evaluation rule one (based on the proportion of data points) is adopted, then it is determined whether each monitoring point meets the positioning accuracy threshold, and the number of all valid monitoring points that meet the accuracy requirements is counted. Total number of points in the true value trajectory The calculation formula is: If evaluation rule two (coverage statistics based on fixed time intervals) is adopted, then the entire test timeline will be divided into sections of length [length missing]. For continuous time windows, count the number of windows that contain at least one valid monitoring point. With total number of windows The calculation formula is: .

[0113] f) Generate trajectory comparison maps, hit point visualization maps, and missed detection section annotation maps, and calculate the monitoring probability; g) Calculate the monitoring probability of each unit according to the 3D test grid, and evaluate it according to the grid validity judgment rules. The monitoring probability of the test target is required to be greater than or equal to 99%.

[0114] 2) The monitoring information update frequency is used to evaluate the system's continuous tracking capability and real-time performance. The test steps include: a) the test target flies along a preset route and maintains stable cruising; b) synchronously acquiring the true trajectory and the updated data from the monitoring system; c) statistically analyzing the data within a unit of time. Within the network, the number of times the effective surveillance information output by the airway surveillance network for the same test target is updated. d) Output update frequency distribution map and update interruption record.

[0115] The formula for calculating the monitoring information update frequency is as follows: ,in, Indicates the monitoring update frequency, in times per second or times per minute; Indicates the duration of observation Number of times the monitoring data is refreshed within the system; This indicates the observation duration. The monitoring information update frequency for the test target must be greater than or equal to 1 time per second. No update for 3 consecutive seconds is considered a link interruption.

[0116] 3) Monitoring and positioning accuracy is used to evaluate the deviation between the coordinate information output by the airway surveillance network and the true trajectory, and is divided into horizontal error and vertical error. The test steps include: a) collecting real-time dynamic positioning coordinates and coordinates output by the monitoring system; b) performing spatiotemporal alignment and interpolation processing to ensure that the data points correspond one-to-one; c) calculating the horizontal error using the half-versus formula and calculating the vertical error using the height difference; d) outputting an error scatter plot, calculating the root mean square error and the 95% confidence interval.

[0117] Among them, horizontal error Calculate using the semi-versus formula: Vertical error The calculation formula is: ,in, Indicates the average radius of the Earth; , These are the differences in latitude and longitude (in radians), respectively. , These represent the output height of the monitoring system and the true height, respectively. The horizontal error requirement for the test target is less than or equal to 10 meters, and the vertical error requirement is less than or equal to 15 meters.

[0118] 4) Alarm latency is used to evaluate the speed at which the monitoring system triggers alarms after an abnormal event occurs. The test steps include: a) pre-setting abnormal alarm rules and trigger thresholds in the Low Altitude Public Airway Integrated Management Service Platform; b) controlling the test target to trigger the preset abnormal event and accurately marking the first timestamp of the event. c) Record the second timestamp of the alarm information output by the platform. d) The computing platform handles latency and compensates for network transmission latency. The formula for calculating alarm delay is: ,in, Indicates the total alarm delay; Indicates the precise time when the triggering event occurred; Indicates the platform alarm output time; This indicates network transmission latency. The alarm latency requirement for the test target is less than or equal to 5 seconds.

[0119] 5) Interface consistency is used to verify that the format, fields, protocol syntax, and timing of the data output by the airway surveillance network conform to the specifications. The test steps include: a) collecting complete raw data link packets output by the airway surveillance network; b) parsing the protocol structure and meaning of each field in the data packets; c) checking field completeness, syntax correctness, timing consistency, and calculating the packet loss rate; d) outputting detailed consistency verification results. The core performance indicators for interface consistency are: data format conformance rate greater than or equal to 99.9%, timestamp deviation less than or equal to 100 milliseconds, and data packet loss rate less than or equal to 0.1%.

[0120] 6) System reliability is assessed by evaluating the operational stability and fault recovery capability of the surveillance system through mean time between failures (MTBF) and mean time to repair (MTBR). The test steps include: a) deploying the airway surveillance network system and bringing it into continuous operation; b) recording the occurrence times of all faults and the times the system recovers during operation; c) calculating the fault intervals and repair times within the total operating time. The system reliability requirements are: MTBF greater than or equal to 500 hours, and MTBR less than or equal to 2 hours.

[0121] 7) Electromagnetic compatibility (EMC) testing is used to confirm that the system can operate stably in complex urban electromagnetic environments without generating excessive interference. The testing steps include: a) conducting radiated emission testing according to national standards; b) conducting electromagnetic immunity testing according to national standards; c) recording interference values ​​and system stability performance during the testing process. The EMC requirements are: radiated emissions must meet the Class A limits of national standard GB / T 9254.1, and immunity must meet the Level 3 immunity requirements specified in national standard GB / T 17626.3.

[0122] The airway surveillance network testing and evaluation method provided in this invention has the overall technical effect of constructing a complete, systematic testing and evaluation system that closely resembles actual operating scenarios. This fundamentally improves the comprehensiveness, authenticity, and reliability of performance verification. Specifically: 1) By constructing a three-dimensional test grid based on the centerline of low-altitude public airways, a standardized and quantifiable spatial reference framework was established for the entire evaluation process. This grid not only achieves a precise mapping of the airway spatial structure but also mandates that the test cover various pre-defined urban environmental types, including densely populated urban areas, high-rise canyon areas, open transition zones, and complex electromagnetic zones. This design ensures that the performance evaluation is not targeted at ideal or single environments but rather systematically examines the adaptability of the airway surveillance network under various typical and complex urban low-altitude challenges, giving the evaluation conclusions practical guiding significance.

[0123] 2) This method innovatively constructs a correlated test environment that simultaneously includes a laboratory simulation environment and a typical urban field environment. This design integrates the controllability and repeatability of laboratory testing with the realism and complexity of field testing. A large number of fundamental and boundary tests can be efficiently completed in the laboratory environment; in the typical urban field environment, the actual performance of the system under real urban occlusion, multipath interference, and electromagnetic noise can be verified. Both are correlated and compared based on the same three-dimensional test grid, achieving a seamless connection from theoretical verification to practical evaluation, overcoming the shortcomings of traditional methods that emphasize laboratory testing while neglecting field verification.

[0124] 3) This method clarifies the standardized operation of the entire process from data acquisition and processing to indicator quantification. By controlling the test target to fly along the grid and simultaneously collecting ground truth and monitoring data, the synchronization and comparability of data sources are ensured. Furthermore, the data undergoes strict temporal and spatial alignment, and based on this, several key performance indicators such as monitoring probability, monitoring information update frequency, monitoring positioning accuracy, and alarm latency are calculated, ensuring that the evaluation results are based on accurate and objective data analysis. The added calculation of alarm latency further extends the evaluation dimension to the real-time response capability of security management.

[0125] 4) Based on the performance indicators obtained from the above systematic testing and precise calculations, the performance of the airway surveillance network in the complex low-altitude urban environment is evaluated, and the conclusions are comprehensive, objective, and reliable. This method can not only accurately identify the performance shortcomings of the low-altitude public airway surveillance network and its specific environment and spatial location, but also provide solid data support and decision-making basis for the planning, construction, technical optimization, operation management, and standard setting of the low-altitude public airway surveillance network, effectively ensuring the safety and efficiency of low-altitude public airway operation.

[0126] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation. The scheme after adjusting the order is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0127] like Figure 2 As shown, an embodiment of the present invention provides a route surveillance network testing and evaluation system 200, which includes a test grid construction module 201, a test environment construction module 202, a control acquisition module 203, an alignment calculation module 204, and an evaluation module 205. Test mesh construction module 201 is used to: construct a three-dimensional test mesh covering low-altitude public airways; The test environment construction module 202 is used to: build associated test environments based on a three-dimensional test grid, including laboratory simulation environments and typical urban field environments; The control acquisition module 203 is used to: control the test target to perform simulated flight or actual flight along the route covered by the three-dimensional test grid in laboratory simulation environment and typical urban field environment, and simultaneously acquire the truth data provided by the corresponding truth system and the monitoring data output by the route monitoring network. The alignment calculation module 204 is used to: align the collected true data with the monitoring data in time and space, and then calculate performance indicators including at least the monitoring probability, monitoring information update frequency, alarm latency and monitoring positioning accuracy. Evaluation module 205 is used to: evaluate the performance of the airway surveillance network in the complex low-altitude environment of cities where low-altitude public airways are located, based on the calculated performance indicators.

[0128] In the above technical solution, the three-dimensional test grid is defined based on the centerline of the low-altitude public airway and is divided into grid cells in the horizontal and vertical directions.

[0129] In the above technical solution, the area covered by the constructed three-dimensional test grid includes a variety of preset urban environment types, including dense urban areas, high-rise canyon areas, open transition areas, and complex electromagnetic areas.

[0130] In the above technical solution, the alignment calculation module 204 is specifically used for: In the real data and the surveillance data after time and space alignment, according to the set evaluation rules, the ratio of the effective surveillance data output by the airway surveillance network to the expected surveillance data is calculated, and this ratio is used as the surveillance probability. In the real data and the monitoring data after time and space alignment, the number of times the effective monitoring information output by the airway surveillance network for the same test target is updated within a unit time, and the number of updates is used as the monitoring information update frequency. In the real data and the monitoring data after time and space alignment, the horizontal error and vertical error between the monitoring data and the real data are calculated respectively, and used as the monitoring positioning accuracy; When the test target triggers a preset abnormal event, the first timestamp of the event and the second timestamp of the alarm information output by the airway surveillance network are recorded, and the alarm delay is calculated based on the time difference between the first timestamp and the second timestamp.

[0131] It should be noted that the beneficial effects of the airway surveillance network testing and evaluation system 200 provided in the above embodiments are the same as those of the airway surveillance network testing and evaluation method described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0132] An electronic device according to an embodiment of the present invention 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 any of the above-mentioned airway surveillance network testing and evaluation methods.

[0133] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-described airway surveillance network testing and evaluation methods.

[0134] 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.

[0135] 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 and evaluating airway surveillance networks, characterized in that, include: Construct a three-dimensional test grid covering low-altitude public air routes; Based on the three-dimensional test grid, an associated test environment is constructed, which includes a laboratory simulation environment and a typical urban field environment; In the laboratory simulation environment and the typical urban field environment, the test target is controlled to perform simulated flight or actual flight along the route covered by the three-dimensional test grid, and the truth data provided by the corresponding truth system and the monitoring data output by the route monitoring network are collected simultaneously. After aligning the collected true data with the monitoring data in time and space, calculate performance indicators including at least monitoring probability, monitoring information update frequency, alarm latency and monitoring positioning accuracy. Based on the calculated performance indicators, the performance of the airway surveillance network in the complex low-altitude environment of the city where the low-altitude public airway is located is evaluated.

2. The method for testing and evaluating airway surveillance networks according to claim 1, characterized in that, The three-dimensional test grid is defined based on the centerline of the low-altitude public airway and is divided into grid cells in the horizontal and vertical directions.

3. The method for testing and evaluating airway surveillance networks according to claim 2, characterized in that, The area covered by the constructed three-dimensional test grid includes a variety of preset urban environment types, including dense urban areas, high-rise canyon areas, open transition areas, and complex electromagnetic areas.

4. A method for testing and evaluating airway surveillance networks according to any one of claims 1 to 3, characterized in that, The calculation includes performance metrics such as surveillance probability, surveillance information update frequency, alarm latency, and surveillance positioning accuracy, including: In the real data and the surveillance data after time and space alignment, according to the set evaluation rules, the ratio of the effective surveillance data output by the airway surveillance network to the expected surveillance data is calculated, and this ratio is used as the surveillance probability. In the real data and the monitoring data after time and space alignment, the number of times the effective monitoring information output by the airway surveillance network for the same test target is updated within a unit time, and the number of updates is used as the monitoring information update frequency. In the real data and the monitoring data after time and space alignment, the horizontal error and vertical error between the monitoring data and the real data are calculated respectively, and used as the monitoring positioning accuracy; When the test target triggers a preset abnormal event, the first timestamp of the event and the second timestamp of the alarm information output by the airway surveillance network are recorded, and the alarm delay is calculated based on the time difference between the first timestamp and the second timestamp.

5. A test and evaluation system for airway surveillance networks, characterized in that, It includes a test grid construction module, a test environment construction module, a control acquisition module, an alignment calculation module, and an evaluation module; The test grid construction module is used to: construct a three-dimensional test grid covering low-altitude public airways; The test environment construction module is used to: construct an associated test environment based on the three-dimensional test grid, the test environment including a laboratory simulation environment and a typical urban field environment; The control and acquisition module is used to: control the test target to perform simulated flight or actual flight along the flight path covered by the three-dimensional test grid in the laboratory simulation environment and the typical urban field environment, and simultaneously acquire the truth data provided by the corresponding truth system and the monitoring data output by the flight path monitoring network. The alignment calculation module is used to: align the collected true data with the monitoring data in time and space, and then calculate performance indicators including at least monitoring probability, monitoring information update frequency, alarm delay and monitoring positioning accuracy. The evaluation module is used to: evaluate the performance of the airway surveillance network in the complex low-altitude environment of the city where the low-altitude public airway is located, based on the calculated performance indicators.

6. The airway surveillance network testing and evaluation system according to claim 5, characterized in that, The three-dimensional test grid is defined based on the centerline of the low-altitude public airway and is divided into grid cells in the horizontal and vertical directions.

7. The airway surveillance network testing and evaluation system according to claim 6, characterized in that, The area covered by the constructed three-dimensional test grid includes a variety of preset urban environment types, including dense urban areas, high-rise canyon areas, open transition areas, and complex electromagnetic areas.

8. A route surveillance network testing and evaluation system according to any one of claims 5 to 7, characterized in that, The alignment calculation module is specifically used for: In the real data and the surveillance data after time and space alignment, according to the set evaluation rules, the ratio of the effective surveillance data output by the airway surveillance network to the expected surveillance data is calculated, and this ratio is used as the surveillance probability. In the real data and the monitoring data after time and space alignment, the number of times the effective monitoring information output by the airway surveillance network for the same test target is updated within a unit time, and the number of updates is used as the monitoring information update frequency. In the real data and the monitoring data after time and space alignment, the horizontal error and vertical error between the monitoring data and the real data are calculated respectively, and used as the monitoring positioning accuracy; When the test target triggers a preset abnormal event, the first timestamp of the event and the second timestamp of the alarm information output by the airway surveillance network are recorded, and the alarm delay is calculated based on the time difference between the first timestamp and the second timestamp.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the airway surveillance network testing and evaluation method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the airway surveillance network testing and evaluation method according to any one of claims 1 to 4.

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