Low-altitude public air route gnss signal fragile section identification and rapid evaluation method and system

Abstract

The application belongs to the field of low-altitude flight and provides a low-altitude public air route GNSS signal fragile section identification and rapid evaluation method and system, wherein the method comprises two stages of offline pre-computation and online interpolation query, the offline pre-computation stage comprises the steps of obtaining original data, constructing a four-dimensional space-time grid, pre-computing each grid node, generating a feature library and periodically automatically updating the offline pre-computation library; the online interpolation query stage comprises the steps of inputting air route data, querying a four-dimensional interpolation index based on the feature library and evaluating integrity, automatically identifying and marking a continuous signal fragile section, outputting and visualizing simulation results and actively optimizing decisions; the application can solve the problems of lacking automatic identification and evaluation capabilities of fragile sections and active optimization of identification results.

Description

Technical Field

[0001] This invention relates to the field of low-altitude flight, and in particular to a method and system for identifying and rapidly assessing vulnerable segments of GNSS signals along low-altitude public airways. Background Technology

[0002] With the rapid development of the low-altitude economy, beyond-line-of-sight (BLOS) operational scenarios such as drone logistics delivery, urban air traffic, and urban inspection are increasingly common. Global Navigation Satellite Systems (GNSS) are the core infrastructure for navigation and positioning of these drones. However, low-altitude airspace lies between the ground and building clusters, making satellite signals highly susceptible to obstruction by terrain undulations, buildings, and other man-made and natural features. This results in insufficient available satellites, deterioration of the geometrical factor of precision (GDOP), and in severe cases, positioning failure, directly threatening flight safety. Currently, there are existing technologies for satellite visibility simulation based on three-dimensional geospatial data models (such as DSM, DEM + building vectors) and dynamic ephemeris. GNSS integrity-related research has been extensive in the general aviation field, but the classic civil aviation integrity mechanisms cannot yet be directly applied to low-altitude drones, especially those flying in complex scenarios.

[0003] To meet the practical needs of routine and large-scale operation of low-altitude drones, existing technologies still have three key gaps that have not been systematically addressed: (1) Existing technologies do not have the ability to quantify the risk of low-altitude integrity (such as protection level), lack the identification mechanism and automatic identification mark of "continuous signal degradation section" or "continuous signal vulnerability section" along the route, and have not established any mechanism for continuous point-by-point judgment and automatic filing along the route, which means that operators still need to manually judge the risk section, resulting in serious inefficiency and reliability.

[0004] (2) There is a lack of a batch rapid evaluation architecture for large-scale route networks and dynamic replanning. The point-by-point simulation scheme in the existing technology needs to be recalculated from scratch every time a new route is encountered, and there is a lack of a mechanism for reusing offline pre-calculation results. In addition, although the regional pre-calculation scheme in the existing technology has a pre-calculation form, it is based on historical data rather than future ephemeris and lacks periodic automatic updates based on predicted ephemeris. Since the static labels based on historical data cannot reflect the real constellation configuration at a specific future time, if they are directly used for pre-departure decisions, they will introduce a non-negligible timeliness bias, making it difficult to support batch rapid evaluation and dynamic replanning based on the latest ephemeris. Therefore, there is an urgent need in the field for a batch evaluation architecture that can be based on the latest ephemeris and achieve second-level response in the online stage, but the existing technology has not yet provided such a solution.

[0005] (3) Lack of proactive optimization decision-making based on the identification results of vulnerable segments: Existing technologies do not automatically generate avoidance schemes based on the identified risk segments. When a segment of a planned route is found to have poor signal, the operator still needs to rely on manual experience to adjust the time window or try to deviate the route. This passive trial and error mode is not only inefficient, but also makes it difficult to guarantee whether the new route after the deviation has completely eliminated the vulnerable segment. It may fall into a cycle of "multiple test flights and repeated verifications", which significantly increases operating costs and safety risks.

[0006] Against the backdrop of the aforementioned technologies, this invention proposes a systematic solution to address the lack of capabilities in automatic identification of vulnerable segments, proactive optimization of identification results, and rapid evaluation. Summary of the Invention

[0007] In view of this, the purpose of this invention is to provide a method and system for identifying and rapidly assessing vulnerable segments of GNSS signals along low-altitude public airways. The technical solution of this invention addresses the problems of insufficient automatic identification and assessment capabilities for vulnerable segments, as well as the proactive optimization of identification results.

[0008] On one hand, this invention provides a method for identifying and rapidly assessing vulnerable segments of GNSS signals along low-altitude public airways. The method includes two stages: offline pre-calculation and online interpolation query. The offline pre-calculation stage includes: S1. Acquisition of raw data: This includes determining the latitude and longitude boundaries and altitude ranges of the target airspace to be pre-assessed; acquiring ephemeris data of multi-constellation satellites covering the target airspace and the planned operating time window; and acquiring a three-dimensional geospatial data model within the target airspace; S2. Construction of a four-dimensional spatiotemporal grid: Discretizing the target airspace into a grid in four-dimensional space, constructing a three-dimensional spatial grid and corresponding time slices. The division criteria are adjusted according to grid accuracy requirements and computational resources; S3. Pre-calculation per grid node and generation of a feature library: For each spatial grid node and its corresponding time slice, candidate satellites are initially screened based on satellite ephemeris, and visibility calculation performance indicators are determined using DSM (Discrete Sight Mode) visibility analysis. These performance indicators are stored as feature vectors in a feature library with spatial and temporal indexes for retrieval in the subsequent online stage; S4. The offline pre-calculation library is automatically updated periodically: the update frequency is set according to the release cycle of the ephemeris products used, and steps S1~S3 are automatically re-executed after each new ephemeris release to automatically generate an updated feature library; the online interpolation query stage includes: S5: Input of route data: input the spatiotemporal point sequence of the low-altitude public route to be evaluated. ,in = Let i be the three-dimensional coordinates of the i-th waypoint. Let i be the planned arrival time at the point, where i is a positive integer greater than or equal to 1; S6. Four-dimensional interpolation index query and integrity assessment based on feature library: For each waypoint, based on its three-dimensional coordinates, retrieve K (using four-dimensional interpolation, K is 8) spatially adjacent grid nodes surrounding the point in the offline feature library, and retrieve the P (P is a positive integer greater than or equal to 2) adjacent time slices in the time dimension according to the planned time, and then... The performance index of the waypoint at the corresponding time is obtained by interpolating the feature vectors; the interpolation result is used for coarse-grained trend prediction or preliminary screening. After neighborhood consistency verification, the integrity of the waypoint is evaluated based on the online protection level model; S7. Automatic identification and marking of continuous signal vulnerable segments: For each waypoint, those that meet the vulnerability judgment conditions are judged as signal vulnerable points; when more than M (M is a positive integer greater than or equal to 2) waypoints are judged as vulnerable points, the waypoint segment is marked as a continuous signal vulnerable segment and its key information is recorded.

[0009] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes step S8. Simulation result output and visualization: Based on the identification and marking results of step S7, output a route signal quality color map and a risk report, the report listing key information of each vulnerable segment and a release conclusion based on the integrity assessment results; Step S9: Proactive optimization decision: For the identified continuous vulnerable signal segments, automatically generate an avoidance plan.

[0010] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein step S3 specifically includes: S3.1 Initial screening of candidate satellites: Based on the satellite ephemeris of the time slice, calculate the positions of all satellites at the grid node, calculate the azimuth and elevation angles of each satellite relative to the node, exclude satellites with elevation angles lower than a preset cutoff angle, and obtain a set of candidate satellites, wherein the preset cutoff angle is a configurable input parameter; S3.2 Obstruction judgment based on DSM: For each satellite in the candidate satellite set, with the three-dimensional coordinates of the grid node as the observation origin, perform a line-of-sight analysis in the three-dimensional geospatial data model: along the horizontal projection line of the satellite line of sight, sample at a set step size, and compare the model elevation at the sampling point with the line-of-sight height at the same location point by point; if there is a sampling point where the DSM elevation is higher than the line-of-sight height, then the satellite is determined to be obstructed by ground features and marked as "invisible"; otherwise, it is marked as "visible"; S3.3 Performance index generation: Based on the set of satellites determined to be "visible", calculate the following indexes of the grid node in the time slice: for each grid node, calculate the number of visible satellites. and DOP value, where the DOP value includes: geometric precision factor. Horizontal precision factor Position accuracy factor and vertical accuracy factor S3.4 Feature Vector Storage: The feature vector obtained in step S3.3 is stored... The performance index values ​​of GDOP, PDOP, HDOP, and VDOP, as well as the satellite identifier, station center azimuth, elevation angle, and visibility marker of each visible satellite, are used as the feature vector of the spatial grid node in the time slice. They are stored in a structured format in the four-dimensional spatiotemporal signal feature library to establish a three-dimensional spatial index and a time index according to the time slice number for subsequent rapid retrieval.

[0011] In addition to the aspects described above and any possible implementations, a further implementation is provided in which the DOP value calculation in step S3.3 is performed using the station-centered azimuth angles of each visible satellite. and elevation angle ,in Construct the observation geometry matrix H, where H is A dimensional matrix, the elements of its j-th row are: The first three columns correspond to the direction cosines of the three position components in the local horizontal coordinate system, and the fourth column is the receiver clock error coefficient, calculated using the weighting coefficient matrix. The diagonal elements of Q , , , These correspond to the variance amplification factors for the eastward, northward, and celestial position components and the clock error component, respectively. , , , .

[0012] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the retrieval and interpolation calculation in step S6 specifically involves: based on the three-dimensional coordinates of the waypoint, retrieving eight spatially adjacent grid nodes from the offline feature library, and obtaining the feature vectors of each node at the corresponding time; based on the planned time of the waypoint, retrieving two adjacent time slices in the time dimension, and using four-dimensional linear interpolation on the 16 feature vectors (eight spatial nodes × two time slices) retrieved, calculating the interpolation results of each feature vector of the waypoint at the corresponding time, wherein the feature vectors include... The DOP values ​​are GDOP, PDOP, HDOP, and VDOP. Neighborhood consistency verification includes: when the waypoint is located in a region with smooth satellite visibility, the DOP value is obtained by four-dimensional linear interpolation; when the waypoint is located in a region with abrupt changes in satellite visibility, the DOP value is calculated by reconstructing the observation matrix based on the visible satellite geometric parameters of the neighboring nodes, or the maximum value of the DOP value of the neighboring nodes is used as a conservative estimate.

[0013] As described above, and in accordance with any possible implementation, a further implementation is provided, wherein the online protection level model in step S6 is: , ;in It is a horizontal protection level. It is a vertical protection level. and This represents the quantile factor, determined by the probability of target integrity risk. Indicates the standard deviation of the user's equivalent ranging error; Integrity assessment in S6: If a waypoint > or > If so, the waypoint is marked as an integrity failure point. For horizontal alarm limits, The vertical alarm limit is defined by the user according to the specific operating scenario and airworthiness requirements, and serves as a configurable input parameter during the online evaluation phase.

[0014] As described above and in any possible implementation, a further implementation is provided, wherein the judgment condition for the signal vulnerability point in step S7 is: the waypoint is determined to be a signal vulnerability point if any of the following conditions are met: (1) the number of visible satellites ,in , Configurable threshold; 2) , The value is greater than or equal to 6; 3) Integrity exceeding the limit judgment: that is > or > .

[0015] In addition to the aspects described above and any possible implementation methods, a further implementation method is provided, wherein the automatic generation of avoidance schemes in step S9 includes flight time window optimization and route spatial offset optimization, as follows: Flight time window optimization: Within the allowable takeoff time range, the timestamps of each waypoint are adjusted according to a set step size. For each adjustment scheme, the four-dimensional interpolation query of step S6 is re-executed, and the vulnerable segment identification of step S7 is re-executed. The time scheme with the fewest vulnerable segments or the best comprehensive indicators is selected, and a recommended takeoff time window is output. The comprehensive indicators include the number of visible satellites, PDOP, HDOP, VDOP, HPL, VPL, as well as the number and total length of vulnerable segments. Route spatial offset optimization: For waypoints within vulnerable segments, the offset is enumerated in the horizontal or vertical direction. For each offset scheme, the four-dimensional interpolation query of step S6 is re-executed, and the vulnerable segment identification of step S7 is re-executed. The optimized scheme that eliminates vulnerable segments and meets airspace safety restrictions is selected, and a route adjustment suggestion is output.

[0016] In addition to the aspects described above and any possible implementations, a further implementation is provided in which the key information for each vulnerable segment in step S7 includes: the starting waypoint number and the ending waypoint number, the starting and ending geographical coordinates, the estimated flight duration, and the minimum number of visible satellites within the segment. Maximum value Peak and Peak value.

[0017] On the other hand, the present invention also provides a system for identifying and rapidly assessing vulnerable segments of GNSS signals in low-altitude public airways, the system comprising two subsystems: offline pre-calculation and online interpolation query.

[0018] Technical effects of the present invention: (1) The batch rapid evaluation capability brought by the dual-stage architecture of “offline pre-computation + online interpolation”: By transferring the computationally intensive point-by-point occlusion judgment and index generation to the offline stage, the online evaluation only requires lightweight four-dimensional interpolation query and protection-level calculation to obtain all signals and integrity indicators, supporting second-level response evaluation and dynamic replanning of large-scale low-altitude air route networks at the city level.

[0019] (2) Four-dimensional signal precision prediction capability: Based on the existing ephemeris + three-dimensional terrain simulation, it further addresses the pre-flight planning needs of low-altitude UAVs by integrating high-resolution three-dimensional geospatial data models and dynamic multi-constellation ephemeris, which can accurately predict the satellite obstruction status at any time and any waypoint in the next few hours to days, providing a precise data foundation for downstream analysis.

[0020] (3) Continuous validity based on the latest ephemeris: The automatic timed update mechanism that synchronizes the offline feature library with the ephemeris release cycle ensures that the online assessment is based on the latest satellite orbit status at any time, overcoming the inherent timeliness bias of the existing historical static data analysis scheme, and providing truly reliable data support for pre-flight release decisions.

[0021] (4) Pre-flight integrity quantification and vulnerable segment identification capability: For the first time, protection level (HPL / VPL) calculation is introduced in low-altitude route pre-simulation, which improves the evaluation dimension from "accuracy" to "integrity risk"; and a unique automatic identification and marking mechanism for "continuous signal vulnerable segments" is created. The multi-indicator joint judgment of "number of visible satellites + PDOP + protection level exceeding limit" is adopted to accurately extract and file information on the segments with continuous signal degradation in the route, providing a quantitative safety decision basis that existing simulation methods cannot provide for the release of UAV beyond visual range flight missions.

[0022] (5) Proactive optimization decision support: Based on simulation results, flight time window recommendations and route space offset suggestions are automatically generated, enabling operators to avoid signal blind spots before takeoff, replacing the traditional passive trial and error mode, and significantly reducing test flight risks and costs. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a block diagram of the evaluation architecture for the offline pre-computation stage and the online interpolation query stage of the present invention; Figure 2 This is a flowchart of the execution steps of the offline pre-computation stage of the present invention; Figure 3 This is a flowchart illustrating the execution steps of the online interpolation query stage of the present invention. Detailed Implementation

[0025] To facilitate understanding of the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0026] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0027] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0028] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0029] This invention provides a method for identifying and rapidly assessing vulnerable segments of GNSS signals along low-altitude public airways, as well as a system for implementing this method.

[0030] like Figure 1As shown, this invention constructs a rapid evaluation architecture of "offline pre-computation + online interpolation query". In the offline pre-computation stage, the target airspace is pre-computed point-by-point according to spatial grids and time slices, establishing a four-dimensional spatiotemporal signal feature library, which is automatically updated according to the ephemeris release cycle. In the online interpolation query stage, various indicators are directly obtained from the actual input waypoints through interpolation queries within the library, without the need to re-execute point-by-point simulation, thus achieving a second-level response time for large-scale route network batch evaluation. The two stages are detailed below: I. Offline Pre-computation Stage This stage includes four main steps: data acquisition, four-dimensional spatiotemporal grid construction, grid-by-grid node pre-computation and feature library generation, and periodic automatic updates of the offline pre-computation library. Figure 1 as well as Figure 2 As shown.

[0031] Step S1: Data Acquisition S1.1 Determination of Target Airspace Range: Determine the airspace coverage area that needs to be pre-assessed, including the horizontal range (longitude and latitude boundaries) and the vertical range (altitude range). This range should cover all possible planned low-altitude public airways and their buffer zones.

[0032] S1.2 Satellite Ephemeris Acquisition: Acquire ephemeris data for multiple constellations of satellites covering the target airspace and planned operating time window. Ephemeris sources can be broadcast ephemeris, IGS ultra-fast precise ephemeris (including 24-hour predicted orbits), or long-term predicted ephemeris (valid for 7-14 days), which can be used to accurately calculate the station center azimuth and elevation angles of each satellite at any given time.

[0033] S1.3 Acquisition of 3D Geospatial Data Model: Acquire a 3D geospatial data model within the target airspace, including terrain elevation and height information of artificial features such as buildings. This model aims to accurately describe the 3D spatial distribution of the land surface and features, with a horizontal resolution better than 10 meters and a height accuracy better than meters. The 3D geospatial data model can be a combination of any one or more of the following data sources: a Digital Surface Model (DSM) generated from airborne LiDAR point clouds, a 3D reality model generated by oblique photogrammetry, an overlay model based on the fusion of a Digital Elevation Model (DEM) and building vector data, or a standard-compliant 3D city model, etc.

[0034] Step S2: Construction of a four-dimensional spatiotemporal grid This step discretizes the target spatial domain into a grid in four-dimensional space: S2.1 Spatial grid division: The horizontal plane of the target airspace is divided into regular grids (preferably 10m×10m, which can be adjusted according to accuracy requirements and computing resources); the vertical direction is layered according to height (preferably 10m intervals, which can be adjusted according to accuracy requirements and computing resources) to cover the planned operating height range (e.g., true height 30m to 300m), thereby constructing a three-dimensional (longitude, latitude and height) spatial grid.

[0035] S2.2 Time Slice Division: Divide a day into time slices according to the simulation accuracy requirements (preferably 10-minute intervals, which can be adjusted according to accuracy requirements and computing resources) to cover the planned operation period.

[0036] Step S3: Pre-calculation of each grid node and generation of the feature library For the four-dimensional spatiotemporal grid constructed in step S2, perform the following calculations for each spatial grid node and its corresponding time slice: S3.1 Initial Screening of Candidate Satellites: Based on the satellite ephemeris of this time slice, calculate the positions of all satellites at this grid node, calculate the azimuth and elevation angles of each satellite relative to this node, and exclude satellites with elevation angles lower than a preset cutoff angle (e.g., 5°) to obtain the candidate satellite set, i.e., the elevation angle. Only satellites with a cutoff angle can be selected for the candidate satellite set. The preset cutoff angle is a configurable input parameter in the offline pre-calculation stage; in other embodiments, other angles can be selected.

[0037] S3.2 DSM-based Occlusion Judgment: For each satellite in the candidate satellite set, using the 3D coordinates of the grid node as the observation origin, a line-of-sight analysis is performed in the 3D geospatial data model along the satellite's line-of-sight direction (determined by azimuth and elevation angles). Specifically, sampling is performed along the horizontal projection line of the satellite's line-of-sight direction at a set step size (comparable to the model resolution). The model elevation at each sampling point is compared with the line-of-sight height at the same location. If a sampling point has a DSM elevation higher than the line-of-sight height, the satellite is determined to be occluded by ground features and marked as "invisible"; otherwise, it is marked as "visible". In other embodiments, besides the line-of-sight sampling method, a grid DEM sector line-of-sight analysis method or a TIN triangulation-based intersection calculation method can be used.

[0038] S3.3 Performance Metric Generation: Based on the set of satellites determined to be "visible," calculate the following metrics for this grid node in this time slice: Calculate for each grid node: (1) Number of visible satellites: The number of satellites marked as "visible". .

[0039] (2) Calculation of DOP (Dilution of Precision) value: using the station center azimuth of each visible satellite. and elevation angle ,in Construct the observation geometry matrix H. H is... A dimensional matrix, the elements of its j-th row are:

[0040] The first three columns correspond to the direction cosines of the three positional components (east, north, and sky) in the local horizontal coordinate system, and the fourth column is the receiver clock error coefficient (constant 1), which represents the common impact of the receiver clock error on all satellite observations in the pseudorange observation equation.

[0041] Calculate the weight coefficient matrix:

[0042] The weight coefficient matrix Q is a 4×4 symmetric matrix, whose diagonal elements , , , These correspond to the variance amplification factors for the eastward, northward, and celestial position components and the clock error component, respectively. From this, the following precision factors are extracted: Geometric precision factor: Position accuracy factor: Horizontal precision factor: Vertical accuracy factor: The DOP value reflects the degree to which the spatial geometry of satellites amplifies positioning errors. The lower the DOP value, the better the satellite geometry (satellite distribution structure) and the higher the potential for positioning accuracy.

[0043] S3.4 Feature Vector Storage: All performance indicators (including the number of visible satellites, GDOP, PDOP, HDOP, VDOP) calculated in step S3.3, as well as the satellite identifier, station center azimuth, elevation angle, and visibility marker of each visible satellite, are used as the feature vector of the spatial grid node in the time slice. This feature vector is stored in the four-dimensional spatiotemporal signal feature library in a structured format, and a spatial index (longitude, latitude, altitude) and a time index (time slice number) are established to facilitate rapid retrieval in the future.

[0044] Step S4: Periodic automatic updates of the offline pre-computation library Because satellite ephemeris has a limited validity period, the spatiotemporal signal feature library obtained through offline pre-calculation is strictly valid only within the validity period of the ephemeris used. Therefore, this invention establishes a timed automatic update mechanism synchronized with the ephemeris release cycle to ensure the continuous validity of the feature library: S4.1 Set the update frequency according to the release cycle of the ephemeris product used. When using IGS ultra-fast ephemeris on the same day, its forecast part covers the next 24 hours, and IGS releases 4 updates per day (UTC 3:00, 9:00, 15:00, 21:00). After each new ephemeris release, the offline pre-calculation program can be automatically triggered to re-execute steps S1~S3 and generate an updated feature library. S4.2 If using a long-term extended ephemeris (valid for 7-14 days), an update can be performed weekly, and steps S1-S3 can be re-executed to generate an updated feature library; The S4.3 update process is completed automatically through a server-side scheduled task, requiring no manual intervention, ensuring that the system can provide effective prediction results based on the latest ephemeris at any time.

[0045] II. Online Interpolation Query Stage The goal of this stage is to, before flight mission release, for any planned route with actual input, rely entirely on the four-dimensional spatiotemporal signal feature library built in the offline pre-computation stage, without performing any point-by-point occlusion judgment or performance index generation calculation. It only obtains the original performance indexes of each waypoint through four-dimensional interpolation queries of the feature library, and calculates the protection level in this stage, thereby completing vulnerable segment identification and optimization decisions. All operations of the online evaluation in this invention are lightweight mathematical interpolation and index queries, combined with lightweight protection level calculations (involving only multiplication and addition operations), achieving a second-level response time. This stage specifically includes the following steps, such as... Figure 1 as well as Figure 3 As shown: Step S5: Input route data Input the spatiotemporal point sequence of the low-altitude public airway to be evaluated. ,in = Let be the three-dimensional coordinates of the i-th waypoint, where longitude, latitude, high, This refers to the planned arrival time at this point (based on Beijing time). Route data can be sourced from flight planning systems, UAV mission planning systems, or manually entered.

[0046] Step S6: Four-dimensional interpolation index query and integrity assessment based on feature library. For each waypoint, perform the following query operation (this operation is entirely index retrieval and mathematical interpolation, without involving any simulation calculation): S6.1 Spatial Neighbor Node Retrieval: Based on the three-dimensional coordinates of the waypoint, retrieve the 8 spatially adjacent grid nodes (the vertices of the cube surrounding the point) in the offline feature library, and obtain the feature vector of each node at the corresponding time.

[0047] S6.2 Time Adjacent Slice Retrieval: Based on the planned time of the waypoint, retrieve two adjacent time slices in the time dimension (one time slice before and one time slice after the planned time).

[0048] S6.3 Four-dimensional interpolation calculation and neighborhood consistency verification: For the 16 feature vectors (8 spatial nodes × 2 time slices) retrieved, four-dimensional linear interpolation is used to calculate the interpolation results of various indicators (each feature vector) of the waypoint at the corresponding time, including but not limited to: visible satellite count, PDOP, HDOP, and VDOP values. In other embodiments, four-dimensional interpolation can also use other mathematical interpolation methods such as inverse distance weighted interpolation and cubic spline interpolation. The interpolation results are only used for coarse-grained trend prediction or preliminary screening. In the integrity protection level calculation, the above DOP interpolation results are not directly used. Instead, the DOP value of the waypoint is determined by one of the following methods based on whether the waypoint is located in the satellite visibility abrupt change region: (1) Interpolation results are used in smooth areas: When waypoints are located in satellite visibility smooth areas, the DOP value obtained by four-dimensional linear interpolation is used directly; (2) Conservative estimation for transition zones: When a waypoint is located in a region of abrupt change in satellite visibility, one of the following methods shall be used: Method 1: Visible Satellite Set Fusion Reconstruction Method: Extract the visible satellite identifier, station center azimuth, elevation angle, and visibility marker (obtained in step 3.2) from the feature vectors of the 16 neighboring nodes; for the same satellite, calculate its visibility ratio among the 16 neighboring nodes. If the visibility ratio exceeds a preset threshold, the satellite is included in the visible satellite set of the waypoint; if the same satellite is visible in multiple neighboring nodes, its azimuth and elevation angles are taken from the values ​​corresponding to the nearest neighboring node to the waypoint, or from the weighted average of the corresponding values ​​of each visible neighboring node; based on the visible satellite set of the waypoint obtained after fusion and its azimuth and elevation angles, reconstruct the observation geometry matrix H of the waypoint according to the same observation geometry matrix construction method in step S3.3, and recalculate the DOP value; if the number of visible satellites of the waypoint obtained after fusion is less than 4, the waypoint is directly determined to be a signal vulnerable point, and the DOP value of the waypoint is taken as the preset maximum value or the upper bound of the DOP envelope of the neighboring nodes; Method 2: DOP envelope upper bound method: Take the upper bound of the spatial envelope of the DOP values ​​of the 16 neighboring nodes, that is, take the maximum value of PDOP, HDOP and VDOP in the 16 feature vectors as the conservative estimate of PDOP, HDOP and VDOP of the waypoint respectively.

[0049] The criteria for determining the satellite visibility mutation region are: the range of the number of visible satellites in the neighboring nodes exceeds the first threshold (e.g., ≥3 satellites), or the PDOP range exceeds the second threshold (e.g., ≥3 satellites).

[0050] S6.4 Online protection level calculation and over-limit judgment: Based on the HDOP and VDOP values ​​determined after neighborhood consistency verification in step S6.3, the online phase protection level is calculated. The HDOP and VDOP values ​​are determined according to the area where the waypoint is located: when the waypoint is located in a region with smooth satellite visibility, the DOP value obtained through four-dimensional linear interpolation is used; when the waypoint is located in a region with abrupt changes in satellite visibility, the DOP value calculated based on the observation matrix reconstructed from the geometric parameters of visible satellites in the neighborhood nodes is used, or the maximum value of the DOP values ​​of the neighborhood nodes is used as a conservative estimate. The Protection Level (PL) is the core quantitative indicator of integrity, meaning that under a given target integrity risk probability, the probability that the actual positioning error exceeds this protection level does not exceed the target risk value. Wherein: Horizontal protection level ( Horizontal Protection Level and Vertical Protection Level The formula for calculating the Vertical Protection Level is:

[0051]

[0052] The meanings and determination methods of each parameter are as follows: 1) (Standard deviation of user equivalent ranging error) It integrates the equivalent standard deviation contributions of various error sources in GNSS positioning, including: residual errors of satellite clock and ephemeris, residual errors after correction of ionospheric delay model, residual errors after correction of tropospheric delay model, and receiver thermal noise and multipath effects. The elevation angle weighted model can be used to determine this, or a fixed empirical constant can be taken. For engineering simplification, a typical medium-precision receiver under normal conditions... The distance can be 0.6 to 1.5 meters. This parameter is a configurable input for the simulation system, and users can adjust it according to the specific receiver model and operating environment.

[0053] 2) and (Quantile factor) and Determined by the probability of integrity risk. Integrity risk typically characterizes the probability of Hazardously Misleading Information (HMI) occurring. This is defined as the probability that the actual positioning error exceeds the protection level but no alarm is issued. Under the assumption that the error follows a zero-mean Gaussian distribution, this corresponds to the integrity risk. The quantile factor is This factor is also a configurable parameter, which users can adjust according to the integrity risk level required for specific operations.

[0054] The above protection level model is a simplified protection level model, which belongs to lightweight protection level calculation (only involving multiplication and addition operations) and can achieve a response time in seconds.

[0055] (3) Determination of integrity exceeding the limit The calculated and Compared with preset alarm limits (horizontal alarm limits) Horizontal AlertLimit and Vertical Alert Limit This is compared to the Vertical Alert Limit. Alert limits are defined by the user based on specific operating scenarios and airworthiness requirements, serving as configurable input parameters during the online evaluation phase. Currently, there is no unified international standard for the integrity alarm limits of low-altitude UAVs operating beyond visual range. In practical applications, existing aviation standards (such as the Class I Vertical Guidance Approach APV-I standard in RTCADO-229D) can be referenced. , (This can be used as a reference, or set independently based on the risk assessment results of the specific flight mission. If a certain waypoint...) > or > If so, the waypoint will be marked as "out of integrity".

[0056] Through step S6.4, the protection level of each waypoint is calculated before flight using offline predicted satellite geometry and configuration error parameters. This level is then compared with the warning limit to determine whether the waypoint meets the integrity requirements, thus providing a basis for "safety and availability" for route planning and flight decisions. During the protection level calculation process, when a waypoint is located in a region of abrupt changes in satellite visibility, one of the following methods is used: reconstructing the observation geometry matrix based on the common visible satellite set of neighboring nodes and recalculating the DOP; or taking the maximum DOP value of the neighboring nodes as a conservative estimate of the waypoint's DOP. The criteria for determining a region of abrupt changes in satellite visibility are: the range of the number of visible satellites among neighboring nodes exceeds a first threshold, or the range of PDOP exceeds a second threshold.

[0057] This invention calculates errors based on visible satellite geometric configuration information obtained from offline feature library interpolation and online configured error statistics parameters, without relying on real-time pseudorange observation data, and is suitable for pre-flight simulation prediction scenarios. It should be noted that in the field of real-time airborne integrity monitoring, standards such as SBAS and GBAS use satellite-by-satellite error covariance synthesis formulas, but these formulas rely on real-time differential correction information and are not suitable for pre-flight simulation.

[0058] Step S7: Automatic identification and marking of vulnerable segments of continuous signals After obtaining the number of visible satellites, PDOP, HPL, and VPL values ​​for each waypoint in step S6, vulnerable segments are identified and marked according to the following rules: S7.1 Single-point vulnerability determination criteria: A waypoint is determined to be a signal vulnerability point if any of the following conditions are met (the threshold can be adjusted according to operational requirements): 1) Number of visible satellites ,in , Configurable threshold; 2) (The satellite's geometry is poor; the threshold can be adjusted as needed.) 3) Exceeding the limit of integrity ( > or > ).

[0059] S7.2 Marking of consecutive vulnerable segments: Set a threshold M for the number of consecutive points. (If M=3, it can be adjusted according to waypoint spacing and flight speed). When more than M waypoints are identified as vulnerable points, the continuous waypoint segment is marked as a "continuous signal vulnerable segment".

[0060] S7.3 Vulnerable Segment Information Recording: For each continuous vulnerable signal segment, record the following information: starting and ending waypoint numbers, starting and ending geographic coordinates, estimated flight duration, and minimum number of visible satellites within the segment. Maximum value Peak and Extreme values ​​of key indicators such as peak values.

[0061] The threshold parameters used in the above single-point vulnerability determination (visible satellite number threshold, PDOP threshold, alarm limit, etc.) are all configurable input parameters in the online evaluation stage. Users can flexibly adjust them according to different operating scenarios, airworthiness requirements and risk assessment results, so that the present invention has a wide range of scenario adaptability.

[0062] Step S8: Simulation Result Output and Visualization Based on the identification and labeling results of step S7, the following output is executed: S8.1 Route Signal Quality Color Map: With the flight distance as the horizontal axis, the color gradient represents the indicators of each waypoint, highlighting vulnerable segments.

[0063] S8.2 Risk Report: List detailed information on each vulnerable segment and the release conclusion based on integrity criteria.

[0064] Step S9: Proactive Optimization Decision For identified continuous vulnerable segments of the signal, an avoidance plan is automatically generated: S9.1 Flight Time Window Optimization: Within the allowable takeoff time range, adjust the timestamps of each waypoint by a set step size (e.g., 10 minutes) (overall translation). For each adjustment scheme, re-execute the four-dimensional interpolation query in step S6 (Note: only the interpolation query needs to be re-executed, no simulation calculation needs to be re-executed). Re-execute the vulnerable segment identification in step S7, filter out the time scheme with the best comprehensive indicators, and output the recommended takeoff time window. The comprehensive indicators include the number of visible satellites, PDOP, HDOP, VDOP, HPL, VPL, as well as the number and total length of vulnerable segments.

[0065] Among the optimal time options based on the above screening criteria, the option with the fewest vulnerable segments or the shortest total length is selected first. If there are multiple options without vulnerable segments, the minimum number of visible satellites, maximum PDOP value, maximum HPL, and maximum VPL value of the entire route are further compared, and the option with the smallest value is selected as the recommended takeoff time window.

[0066] S9.2 Route Spatial Offset Optimization: For waypoints within vulnerable sections, enumerate the offset in the horizontal direction (east / north offset, step size can be set, such as 5m) or the vertical direction (altitude rise / fall, step size can be set, such as 5m). For each offset scheme, re-execute the four-dimensional interpolation query in step S6, re-execute the vulnerable section identification in step S7, filter out the optimization schemes that eliminate vulnerable sections and meet airspace safety restrictions, and output route adjustment suggestions.

[0067] All optimized enumeration schemes in step S9 rely solely on repeated interpolation queries of the offline feature library and online lightweight protection-level calculations, without requiring re-execution of point-by-point simulation calculations. This allows the operator to quickly traverse a large number of temporal / spatial alternatives before takeoff, selecting the optimal solution and completely eliminating the inefficient traditional "manual trial and error + repeated simulation verification" model.

[0068] In summary, the method of this invention, based on a two-stage evaluation architecture, performs a four-dimensional (longitude, latitude, altitude, and time) signal simulation evaluation of the planned flight route several hours to several days before the flight mission. Its core technical idea is to use a high-precision three-dimensional geospatial data model to determine satellite line-of-sight geometric occlusion, combine this with dynamic multi-constellation ephemeris calculations to determine the visible satellite configuration at each waypoint, and integrate the generation of the number of visible satellites, DOP value, and integrity parameters (protection level). It automatically identifies and marks continuous vulnerable signal segments and provides optimization suggestions for flight time windows and route spatial offsets accordingly.

[0069] The following uses a low-altitude logistics distribution route in the core area of ​​a city as an example to illustrate the specific implementation of the present invention. [Specific Implementation Example 1] I. Implementation Example of Offline Pre-computation Phase (1) Data preparation and grid construction For the city's routine operation area (covering an area of ​​approximately 20km × 20km in the city's core area), the system pre-constructed a four-dimensional spatiotemporal signal feature database. The spatial grid uses a 10m × 10m horizontal interval, and is layered vertically at 10m intervals, covering a true height range of 30m to 300m (a total of 28 layers); the time slices use a 10-minute interval, covering 84 time slices daily from 06:00 to 20:00. The total number of three-dimensional spatial grid nodes is approximately (2000 × 2000 × 28) = 112 million, with 84 time slices and a total of approximately 9.4 billion feature vectors.

[0071] Ephemeris data: The forecast portion of the IGS ultra-fast, precise ephemeris (covering the next 24 hours).

[0072] 3D geospatial data model: Acquire a high-precision 3D real-world urban model produced by oblique photogrammetry, which includes detailed structural elevation information such as buildings. The model has a horizontal resolution better than 10 meters and a height accuracy better than meters.

[0073] (2) Pre-calculation of each grid node For each spatial grid node, the calculations in steps S3.1-S3.3 are performed at each time slice. Taking the calculation of a grid node located in a densely built-up area with a true height of 80m at the 10:00 time slice as an example: for the grid-by-grid node simulation calculation in the offline pre-calculation stage, the system is configured with an elevation angle cutoff of 5°; the positions of all GNSS satellites are calculated according to the ephemeris, and after excluding satellites with an elevation angle lower than 5°, 23 candidate satellites are obtained; Line-of-sight obstruction was determined for each satellite based on DSM. Among them, the signals of 11 satellites were blocked by surrounding buildings, while the signals of 12 satellites were visible. An observation matrix was constructed based on the azimuth / elevation angles of 12 visible satellites, and PDOP=2.1, HDOP=1.6, and VDOP=1.4 were calculated. (3) Feature library storage All the above indicators (12 visible satellites, GDOP=2.5, PDOP=2.1, HDOP=1.6, VDOP=1.4) are used as the feature vector of the grid node in the time slice and stored in the four-dimensional spatiotemporal signal feature library.

[0074] (4) Periodic update mechanism This database is built upon IGS ultra-fast ephemeris data and is valid for 24 hours. The system is configured with a daily scheduled task (UTC 3:30, i.e., after the first update of the day IGS is released) to automatically download the latest ephemeris data, re-execute all pre-calculations, and refresh the feature database. During the intervals between daily updates, the data in the database remains valid and reflects the latest satellite orbital status.

[0075] II. Implementation Example of Online Assessment Phase (1) Route and data preparation A planned low-altitude public logistics route in the city's core area was selected for online rapid evaluation. The route is approximately 2.3 kilometers long, with 15 waypoints (P1~P15), and the distance between adjacent waypoints is approximately 150 meters. The designed flight altitude is 80 meters above ground level, and the cruising speed is 18 km / h. The planned departure time is 10:00:00 Beijing time on July 15, 2026.

[0076] Simulation parameters are set as follows: visible satellite vulnerability threshold < 4, PDOP vulnerability threshold > 6, integrity risk. (Corresponding quantile factor is) The horizontal alarm limit HAL is 40m, the vertical alarm limit VAL is 50m, and the continuous vulnerable point threshold M is 3. All of these parameters can be configured online, allowing users to adjust them flexibly according to different operating scenarios.

[0077] (2) Querying four-dimensional interpolation indexes and calculating protection level The system performs online evaluation according to step S6, extracting preliminary indicators from the offline feature library for each waypoint through four-dimensional interpolation, and determining the final indicators after neighborhood consistency verification. Taking waypoint P6 as an example (located in a densely built-up area, longitude 116.3972°E, latitude 39.9076°N, altitude 80m, time 10:02:30): The system retrieves eight spatially adjacent grid nodes and two adjacent time slices (10:00 and 10:10). Four-dimensional linear interpolation is performed on 16 feature vectors to obtain preliminary interpolation results for the waypoint: 4 visible satellites, PDOP=5.8, HDOP=4.9, and VDOP=3.0. After neighborhood consistency verification, the range of visible satellites in the neighborhood of point P6 is 1 (<3), and the range of PDOP is 2.1 (<3). Therefore, point P6 is determined to be in a smooth satellite visibility region. This is combined with the online settings... And quantile factors, the protection level is calculated directly using the HDOP and VDOP values ​​obtained from the above interpolation: =5.33 × 4.9 × 0.8 = 20.9m =5.33 × 3.0 × 0.8 = 12.8m Repeat the above process to obtain the index data for all 15 waypoints.

[0078] (3) Identification and marking of vulnerable segments of continuous signals Based on the waypoint indicators obtained through interpolation queries, vulnerable segments are identified according to step S7: In the segment 0.8km to 1.2km from the starting point (waypoints P6 to P11, traversing dense clusters of high-rise commercial buildings), the number of visible satellites drops sharply from an average of 15 in the previous segment to a minimum of 3 (at P8). During this segment, PDOP jumps from 2.5 to 24.0 (at P8), HDOP deteriorates from 2.0 to 21.3, and protection level calculations show that HPL increases from 9m to 91m (>HAL 40m), and VPL increases from 5m to 64m (>VAL 50m).

[0079] Based on the online assessment parameters, the single-point assessment results are as follows: P6 (non-vulnerable, 4 visible satellites, PDOP=5.8≤6, HPL=20.9m≤40m, VPL=12.8m≤50m), P7 (vulnerable, PDOP=12.5>6), P8 (vulnerable, 3<4 visible satellites, PDOP=24.0>6, HPL=91m>40m, VPL=64m>50m), P9 (vulnerable, PDOP=15.2>6), P10 (vulnerable, PDOP=8.7>6), P11 (non-vulnerable). If more than M=3 waypoints are consecutively identified as vulnerable points, the system automatically marks P7 to P10 (4 waypoints in total, flying continuously for approximately 100 seconds) as a "continuous signal vulnerability segment" and marks them with a red warning.

[0080] Vulnerable segment information record: starting waypoint P7 (116.3973°E, 39.9074°N, 80m), ending waypoint P10 (116.3980°E, 39.9070°N, 80m), estimated flight duration 100 seconds, number of visible satellites within the segment 3-4, PDOP peak 24.0 (P8), HPL peak 91m (P8), VPL peak 64m (P8).

[0081] [Specific Implementation Example 2] Optimized Application Implementation Example (1) Flight time window optimization The system enumerates 19 alternative time schemes in 10-minute increments within ±1.5 hours before and after the planned takeoff time of 10:00 (i.e., from 08:30 to 11:30). For each scheme, only the four-dimensional interpolation query, neighborhood consistency check, and protection level calculation in step S6 need to be re-executed (no re-simulation is required), and then the vulnerable segment identification in step S7 needs to be re-executed.

[0082] The assessment results showed that if the takeoff time was advanced by 30 minutes to 9:30, the constellation configuration of the original vulnerable segment changed significantly. Interpolation results showed that the number of visible satellites in this segment recovered to 7, PDOP decreased to 2.8, and HPL / VPL decreased to 28m and 35m respectively, completely eliminating the vulnerability segment. If the takeoff time was delayed to 10:30, the number of visible satellites in this segment further decreased to 2, and the vulnerable segment was extended to P6-P11.

[0083] After comprehensively comparing all 19 options, the system recommends the optimal takeoff time window as 9:25-9:40. Within this time window, all the original vulnerable segments are eliminated, the maximum PDOP of the entire route is ≤3.0, and all protection levels are qualified.

[0084] (2) Optimization of route spatial offset If the takeoff time is not adjusted (remaining at 10:00), the system enumerates horizontal offset schemes for waypoints (P7-P10) within the vulnerable segment: northward offset ±50m (5m step, 21 steps in total), eastward offset ±50m (5m step, 21 steps in total), resulting in 441 possible combinations. Each offset scheme only requires re-execution of step S6 (four-dimensional interpolation query, neighborhood consistency check, and protection level calculation) and step S7 (vulnerable segment identification), without re-execution of any simulation calculations.

[0085] The assessment revealed that shifting the entire P7 to P10 range northward by 25 meters eliminated all obstructions, restoring the number of visible satellites to 8, reducing PDOP to 2.5, HPL to 18m (<40m), and VPL to 12m (<50m), achieving full protection compliance. The assessment also verified that the minimum horizontal distance between the waypoints and the building complex after the shift was 18 meters, meeting the city's safe operating distance requirements for UAVs (≥10 meters).

[0086] The system outputs a suggested route adjustment: the latitude coordinates of each waypoint from P7 to P10 should be increased by approximately 0.000225° (corresponding to a northward shift of 25m), while the coordinates of the remaining waypoints should remain unchanged.

[0087] Based on the above-described Specific Implementation Examples 1 and 2, a batch online assessment was conducted on all 78 approved low-altitude public air routes operating routinely in the city. Each air route underwent complete interpolation query, protection level calculation, vulnerable segment identification, and report generation according to steps S6-S8. The results of the batch assessment capability verification are as follows: Evaluation Results: Of the 78 routes, 63 were evaluated as "Recommended for Release" (no vulnerable segments or acceptable vulnerable segments), 12 were evaluated as "Requiring Adjustment of Time / Route" (existing vulnerable segments that can be optimized), and 3 were evaluated as "Not Recommended for Execution" (existing severe vulnerable segments that cannot be eliminated by time / space offsets). The total evaluation time for all 78 routes was approximately 35 seconds, averaging approximately 0.45 seconds per route. For the 12 routes evaluated as "Requiring Adjustment of Time / Route," the system further executed the optimization decision in step S9, which took an additional 3-5 seconds per route.

[0088] If the traditional point-by-point real-time simulation method is used, the simulation time will be greatly increased, which fully verifies the significant advantages of the "offline pre-computation + online interpolation query" architecture in the batch evaluation of large-scale route networks. This mechanism ensures that the simulation system can provide rapid evaluation based on the latest ephemeris at any time, meeting the near real-time simulation requirements of batch evaluation of multiple routes and temporary route changes.

[0089] The beneficial effects of the technical solution of this invention are as follows: (1) Four-dimensional signal precision prediction capability: Based on the existing ephemeris + three-dimensional terrain simulation, it further addresses the pre-flight planning needs of low-altitude UAVs by integrating high-resolution three-dimensional geospatial data models and dynamic multi-constellation ephemeris, which can accurately predict the satellite obstruction status at any time and any waypoint in the next few hours to days, providing a precise data foundation for downstream analysis.

[0090] (2) Pre-flight integrity quantification and vulnerable segment identification capability: For the first time, protection level (HPL / VPL) calculation is introduced in low-altitude route pre-simulation, which improves the evaluation dimension from "accuracy" to "integrity risk"; and a unique automatic identification and marking mechanism for "continuous signal vulnerable segments" is created. The mechanism adopts a multi-indicator joint judgment of "number of visible satellites + PDOP + protection level exceeding limit" to accurately extract and file information on the segments with continuous signal degradation in the route, providing a quantitative safety decision basis that existing simulation methods cannot provide for the release of UAV beyond visual range flight missions.

[0091] (3) Proactive optimization decision support: Based on simulation results, flight time window recommendations and route space offset suggestions are automatically generated, enabling operators to avoid signal blind spots before takeoff, replacing the traditional passive trial and error mode, and significantly reducing test flight risks and costs.

[0092] (4) The batch rapid evaluation capability brought by the dual-stage architecture of “offline pre-computation + online interpolation”: By transferring the computationally intensive point-by-point occlusion judgment and index generation to the offline stage, the online evaluation only requires lightweight four-dimensional interpolation query and protection-level calculation to obtain all signals and integrity indicators, supporting second-level response evaluation and dynamic replanning of large-scale low-altitude air route networks at the city level.

[0093] (5) Continuous validity based on the latest ephemeris: The automatic timed update mechanism that synchronizes the offline feature library with the ephemeris release cycle ensures that the online assessment is based on the latest satellite orbit status at any time, overcoming the inherent timeliness bias of the existing historical static data analysis scheme, and providing truly reliable data support for pre-flight release decisions.

[0094] The present invention also provides a system for identifying and rapidly assessing vulnerable segments of GNSS signals in low-altitude public airways. The system includes two subsystems: an offline pre-calculation subsystem and an online interpolation query subsystem. The offline pre-calculation subsystem is used to perform the aforementioned steps S1-S4, and the online interpolation query subsystem is used to perform the aforementioned steps S5-S9.

[0095] The foregoing has provided a detailed description of a method for identifying and rapidly assessing vulnerable segments of GNSS signals along low-altitude public airways, a system for implementing the method, and its embodiments. The descriptions of the embodiments above are merely illustrative of the method and its core concepts; furthermore, those skilled in the art will recognize that variations in specific implementation methods and application scope may occur based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

[0096] Certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that hardware manufacturers may use different names to refer to the same component. This specification and claims do not distinguish components based on differences in name, but rather on differences in function. The terms "comprising" and "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising / including but not limited to". "Approximately" means that within an acceptable margin of error, those skilled in the art can solve the technical problem and substantially achieve the technical effect within a certain margin of error. The following descriptions in the specification are preferred embodiments for carrying out this application; however, these descriptions are for the purpose of illustrating the general principles of this application and are not intended to limit the scope of this application. The scope of protection of this application shall be determined by the appended claims.

[0097] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes said element.

[0098] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0099] The foregoing description illustrates and describes several preferred embodiments of this application. However, as previously stated, it should be understood that this application is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the application concept described herein through the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of this application should be within the protection scope of the appended claims.

Claims

1. A method for identifying and rapidly assessing vulnerable segments of GNSS signals along low-altitude public airways, characterized in that, The method comprises two stages: offline pre-calculation and online interpolation query, wherein the offline pre-calculation stage includes: S1. Acquisition of raw data: This includes determining the latitude and longitude boundaries and altitude range of the target airspace coverage to be pre-assessed; acquiring ephemeris data of multi-constellation satellites covering the target airspace and the planned operation time window; and acquiring a three-dimensional geospatial data model within the target airspace. S2. Four-dimensional spatiotemporal grid construction: The target spatial domain is discretized into a grid in four-dimensional space, and a three-dimensional spatial grid and corresponding time slices are constructed. The criteria for the division are adjusted according to the grid accuracy requirements and computing resources. S3. Pre-calculation and feature library generation for each grid node: For each spatial grid node and its corresponding time slice, candidate satellites are initially screened based on satellite ephemeris, and visibility calculation performance indicators are determined using DSM visibility analysis. These performance indicators are then stored as feature vectors in a feature library with spatial and temporal indexes for retrieval and use in subsequent online stages. S4. Periodic automatic update of offline pre-calculation library: Set the update frequency according to the release cycle of the ephemeris product used, and automatically re-execute steps S1~S3 after each new ephemeris release to automatically generate the updated feature library; The online interpolation query stage includes: S5: Input of route data: Input the spatiotemporal point sequence of the low-altitude public route to be evaluated. ,in = Let i be the three-dimensional coordinates of the i-th waypoint. Let i be the planned time to reach that point, where i is a positive integer greater than or equal to 1; S6. Four-dimensional interpolation index query and integrity assessment based on feature library: For each waypoint, based on its three-dimensional coordinates, retrieve the K spatially adjacent grid nodes surrounding the point in the offline feature library, where K=8, and retrieve the P adjacent time slices before and after the planned time. And thus to The performance index of the waypoint at the corresponding time is obtained by interpolating the feature vectors; the interpolation result is used for coarse-grained trend prediction or preliminary screening, and after neighborhood consistency verification, the integrity of the waypoint is evaluated based on the online protection level model. S7. Automatic Identification and Marking of Continuous Signal Vulnerability Segments: For each waypoint, those meeting the vulnerability criteria are identified as signal vulnerability points; when more than M waypoints are consecutively identified as vulnerability points, among them... The route segment was marked as a continuous signal-vulnerable segment, and its key information was recorded.

2. The method according to claim 1, characterized in that, The method further includes: Step S8. Simulation Result Output and Visualization: Based on the identification and marking results of step S7, output a route signal quality color map and a risk report. The report lists key information of each vulnerable section and a release conclusion based on the integrity assessment results. Step S9: Proactive optimization decision: Automatically generate avoidance solutions for the identified vulnerable segments of continuous signals.

3. The method according to claim 2, characterized in that, Step S3 specifically includes: S3.1 Initial screening of candidate satellites: Based on the satellite ephemeris of the time slice, calculate the position of all satellites at the grid node, calculate the azimuth and elevation angle of each satellite relative to the node, exclude satellites with elevation angles lower than the preset cutoff angle, and obtain a set of candidate satellites. The preset cutoff angle is a configurable input parameter. S3.2 DSM-based occlusion judgment: For each satellite in the candidate satellite set, with the three-dimensional coordinates of the grid node as the observation origin, a line-of-sight analysis is performed in the three-dimensional geospatial data model: along the horizontal projection line of the satellite's line of sight, sampling is performed at a set step size, and the model elevation at the sampling point is compared with the line-of-sight height at the same location point by point; if there is a sampling point where the DSM elevation is higher than the line-of-sight height, the satellite is determined to be occluded by ground features and marked as "invisible"; otherwise, it is marked as "visible". S3.3 Performance Metric Generation: Based on the set of satellites determined to be "visible," calculate the following metrics for the grid node in this time slice: For each grid node, calculate the number of visible satellites. and DOP value, where the DOP value includes: geometric precision factor. Horizontal precision factor Position accuracy factor and vertical accuracy factor ; S3.4 Feature Vector Storage: The feature vector obtained in step S3.3 is stored... The performance index values ​​of GDOP, PDOP, HDOP, and VDOP, as well as the satellite identifier, station center azimuth, elevation angle, and visibility marker of each visible satellite, are used as the feature vector of the spatial grid node in the time slice. They are stored in a structured format in the four-dimensional spatiotemporal signal feature library to establish a three-dimensional spatial index and a time index according to the time slice number for subsequent rapid retrieval.

4. The method according to claim 3, characterized in that, The DOP value calculation in step S3.3 is performed using the station center azimuth of each visible satellite. and elevation angle ,in Construct the observation geometry matrix H, where H is A dimensional matrix, the elements of its j-th row are: The first three columns correspond to the direction cosines of the three position components in the local horizontal coordinate system, and the fourth column is the receiver clock error coefficient, which is used to calculate the weighting coefficient matrix. The diagonal elements of Q , , , These correspond to the variance amplification factors for the eastward, northward, and celestial position components and the clock error component, respectively. , , , .

5. The method according to claim 3, characterized in that, The retrieval and interpolation calculation in step S6 specifically involves: based on the three-dimensional coordinates of the waypoint, retrieving eight spatially adjacent grid nodes from the offline feature library to obtain the feature vectors of each node at the corresponding time; based on the planned time of the waypoint, retrieving two adjacent time slices in the time dimension; and using four-dimensional linear interpolation to calculate the interpolation results of each feature vector of the waypoint at the corresponding time for the 16 feature vectors (eight spatial nodes × two time slices). The feature vectors include... The DOP values ​​are GDOP, PDOP, HDOP, and VDOP. Neighborhood consistency verification includes: when the waypoint is located in a region with smooth satellite visibility, the DOP value is obtained by four-dimensional linear interpolation; when the waypoint is located in a region with abrupt changes in satellite visibility, the DOP value is calculated by reconstructing the observation matrix based on the visible satellite geometric parameters of the neighboring nodes, or the maximum value of the DOP value of the neighboring nodes is used as a conservative estimate.

6. The method according to claim 3, characterized in that, The online protection level model in step S6: in It is a horizontal protection level. It is a vertical protection level. and This represents the quantile factor, determined by the probability of target integrity risk. This represents the standard deviation of the user's equivalent ranging error; Integrity assessment in S6: If a waypoint > or > If so, the waypoint is marked as an integrity failure point. For horizontal alarm limits, The vertical alarm limit is defined by the user according to the specific operating scenario and airworthiness requirements, and serves as a configurable input parameter during the online evaluation phase.

7. The method according to claim 6, characterized in that, The criteria for determining a signal vulnerability point in step S7 are: if any of the following conditions are met, the waypoint is determined to be a signal vulnerability point: Number of visible satellites ,in , Configurable threshold; Where N takes a value greater than or equal to 6; Integrity exceeding limit judgment: that is > or > .

8. The method according to claim 3, characterized in that, Step S9 automatically generates an avoidance plan, including flight time window optimization and route spatial offset optimization, as detailed below: Flight time window optimization: Within the allowable takeoff time range, adjust the timestamps of each waypoint by a set step size, re-execute the four-dimensional interpolation query in step S6 for each adjustment scheme, re-execute the vulnerable segment identification in step S7, filter out the time scheme with the best comprehensive indicators, and output the recommended takeoff time window. The comprehensive indicators include the number of visible satellites, PDOP, HDOP, VDOP, HPL, VPL, as well as the number and total length of vulnerable segments. Route spatial offset optimization: For waypoints within vulnerable segments, enumerate the offsets in the horizontal or vertical directions, re-execute the four-dimensional interpolation query in step S6 for each offset scheme, re-execute the vulnerable segment identification in step S7, filter out the optimization schemes that eliminate vulnerable segments and meet airspace safety restrictions, and output route adjustment suggestions.

9. The method according to claim 3, characterized in that, Step S7 key information for each vulnerable segment includes: starting and ending waypoint numbers, starting and ending geographic coordinates, estimated flight duration, and minimum number of visible satellites within the segment. Maximum value Peak and Peak value.

10. A system for identifying and rapidly assessing vulnerable segments of GNSS signals along low-altitude public airways, characterized in that, The system includes two subsystems: an offline pre-calculation subsystem and an online interpolation query subsystem. The offline pre-calculation subsystem is used to execute steps S1-S4 of any one of claims 1-9, and the online interpolation query subsystem is used to execute steps S5-S9 of any one of claims 2-9.

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