A PDOP risk assessment modeling method and system based on low-altitude environment perception

Abstract

This invention provides a PDOP risk assessment modeling method and system based on low-altitude environmental perception, comprising: calculating a dynamic threshold function and constructing a corrected set of visible satellites; constructing an actual observation matrix corresponding to the center point of each unit grid based on the candidate position of the UAV in three-dimensional space, the corrected set of visible satellites, and the position vectors of each navigation satellite at any time; calculating the actual PDOP value corresponding to the center point of each unit grid based on the diagonal elements; generating an actual three-dimensional distribution field of PDOP based on the actual observation matrix and the actual PDOP value; constructing a multipath error model; organically fusing the multipath error model and the actual three-dimensional distribution field of PDOP to obtain a comprehensive GNSS positioning risk index; and constructing a GNSS positioning accuracy risk assessment model by fusing three-dimensional environmental features to improve the accuracy and reliability of GNSS positioning accuracy assessment in complex low-altitude airspace.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation and low-altitude flight safety technology, and in particular to a PDOP risk assessment modeling method and system based on low-altitude environmental perception. Background Technology

[0002] In the large-scale low-altitude applications of UAVs, the problem of GNSS positioning accuracy degradation caused by complex building clusters is becoming increasingly prominent. Traditional GNSS positioning accuracy assessment methods (such as the PDOP model) are based on static calculations of satellite geometry. However, this method is difficult to meet the requirements of refined path planning in complex airspace. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a PDOP risk assessment modeling method and system based on low-altitude environmental perception. By integrating three-dimensional environmental features to construct a GNSS positioning accuracy risk assessment model, the accuracy and reliability of GNSS positioning accuracy assessment in complex low-altitude airspace can be improved.

[0004] In a first aspect, embodiments of the present invention provide a PDOP risk assessment modeling method based on low-altitude environment perception, the method comprising: Calculate the dynamic threshold function, and construct the corrected visible satellite set based on the dynamic threshold function; Acquire GNSS ephemeris data and candidate locations of UAVs in three-dimensional space; Based on the GNSS ephemeris data, obtain the position vector of each navigation satellite at any given time; Based on the candidate position of the UAV in the three-dimensional space, the corrected set of visible satellites, and the position vectors of each navigation satellite at any given time, an actual observation matrix corresponding to the center point of each unit grid is constructed; wherein, the actual observation matrix is ​​used to characterize the unit vector direction of each visible satellite relative to the receiver; Calculate the actual PDOP value corresponding to each unit grid center point based on the diagonal elements of the actual observation matrix; Based on the actual observation matrix and the actual PDOP value corresponding to each unit grid center point, generate the actual three-dimensional distribution field of PDOP; Construct a multipath error model; The multipath error model and the actual three-dimensional distribution field of the PDOP are organically integrated to obtain the GNSS integrated positioning risk index.

[0005] Furthermore, the dynamic threshold function is calculated, including: A ray tracing algorithm is used to simulate the propagation path of each satellite signal from its spatial location to the current location of the UAV; Based on the propagation path, terrain, and 3D building model, occlusion detection is performed to determine the visibility status of the connection between the UAV and the satellite. Based on the visibility state, calculate the occlusion probability at time t; The dynamic threshold function is calculated based on the occlusion probability at time t.

[0006] Furthermore, based on the occlusion probability at time t, the dynamic threshold function is calculated, including: The dynamic threshold function is calculated according to the following formula:

[0007] in, The dynamic threshold function is... Based on the basic signal strength threshold, As an environmentally sensitive parameter, The rate of change coefficient, Let be the occlusion probability at time t.

[0008] Furthermore, based on the visibility state, the occlusion probability at time t is calculated, including: The occlusion probability at time t is calculated using the following formula:

[0009]

[0010] in, Let be the occlusion probability at time t. The visibility state is defined as N, where N is the total number of visible satellites at time t.

[0011] Furthermore, constructing a modified visible satellite set based on the dynamic threshold function includes: When the received signal strength is greater than or equal to the dynamic threshold function, it is determined to be a valid satellite; The modified visual satellite set is constructed based on the valid satellites.

[0012] Furthermore, a multipath error model is constructed, including:

[0013] in, Spatial location The multipath error model at that location, For the number of building material types, For the first Multipath perturbation weighting coefficients for similar materials The reflection influence factor of the material in space is used to characterize the relationship between the incident direction and the material. The angle between surface normals.

[0014] Furthermore, the multipath error model and the actual three-dimensional distribution field of the PDOP are organically integrated to obtain the GNSS integrated positioning risk index, including: The GNSS integrated positioning risk index is calculated according to the following formula:

[0015] in, This refers to the GNSS integrated positioning risk index. This represents the actual three-dimensional distribution field of the PDOP. Spatial location The multipath error model at that location, This represents the standard error value of a GNSS system under free-space conditions.

[0016] Secondly, embodiments of the present invention provide a PDOP risk assessment and modeling system based on low-altitude environment perception, the system comprising: The modified visible satellite set construction module is used to calculate a dynamic threshold function and construct a modified visible satellite set based on the dynamic threshold function. The candidate location acquisition module is used to acquire GNSS ephemeris data and candidate locations of the UAV in three-dimensional space; The position vector acquisition module is used to acquire the position vector of each navigation satellite at any time based on the GNSS ephemeris data. The actual observation matrix construction module is used to construct the actual observation matrix corresponding to the center point of each unit grid based on the candidate position of the UAV in the three-dimensional space, the corrected set of visible satellites, and the position vector of each navigation satellite at any time; wherein, the actual observation matrix is ​​used to characterize the unit vector direction of each visible satellite relative to the receiver; The actual PDOP value calculation module is used to calculate the actual PDOP value corresponding to each unit grid center point based on the diagonal elements of the actual observation matrix. The generation module is used to generate the actual three-dimensional distribution field of PDOP based on the actual observation matrix and the actual PDOP value corresponding to each unit grid center point; The multipath error model building module is used to build multipath error models. The fusion module is used to organically fuse the multipath error model and the actual three-dimensional distribution field of the PDOP to obtain the GNSS integrated positioning risk index.

[0017] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the method described above.

[0018] Fourthly, embodiments of the present invention provide a computer-readable medium having processor-executable non-volatile program code that causes the processor to perform the method described above.

[0019] This invention provides a PDOP risk assessment modeling method and system based on low-altitude environmental perception, including: calculating a dynamic threshold function; constructing a corrected set of visible satellites based on the dynamic threshold function; acquiring GNSS ephemeris data and candidate positions of UAVs in three-dimensional space; acquiring the position vectors of each navigation satellite at any given time based on the GNSS ephemeris data; constructing an actual observation matrix corresponding to the center point of each unit grid based on the candidate positions of the UAVs in three-dimensional space, the corrected set of visible satellites, and the position vectors of each navigation satellite at any given time; wherein the actual observation matrix is ​​used to characterize the unit vector direction of each visible satellite relative to the receiver; calculating the actual PDOP value corresponding to the center point of each unit grid based on the diagonal elements of the actual observation matrix; generating an actual three-dimensional distribution field of PDOP based on the actual observation matrix and the actual PDOP value corresponding to the center point of each unit grid; constructing a multipath error model; organically fusing the multipath error model and the actual three-dimensional distribution field of PDOP to obtain a comprehensive GNSS positioning risk index; and constructing a GNSS positioning accuracy risk assessment model by fusing three-dimensional environmental features, which can improve the accuracy and reliability of GNSS positioning accuracy assessment in complex low-altitude airspace.

[0020] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a flowchart of the PDOP risk assessment modeling method based on low-altitude environment perception provided in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the PDOP risk assessment modeling system based on low-altitude environment perception provided in Embodiment 2 of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] This invention relates to the field of satellite navigation and low-altitude flight safety technology, and is applicable to scenarios such as UAV path planning and aircraft navigation enhancement systems.

[0026] In existing technologies, PDOP assessment methods rely on static calculations of satellite geometry, which are insufficient to meet the requirements of refined path planning in complex airspaces. Therefore, there is an urgent need to construct a high-precision PDOP risk assessment model that integrates environmental characteristics to ensure the navigation safety and path planning reliability of UAVs in densely built-up areas.

[0027] This invention extends this to three-dimensional complex airspace, proposing a PDOP risk assessment modeling method based on low-altitude environmental perception. This method breaks through the traditional two-dimensional planar PDOP computational framework, integrating building occlusion and multipath effects to construct a three-dimensional spatial model, meeting the high sensitivity of low-altitude UAVs to positioning errors. Furthermore, by utilizing dual-channel compensation of ray tracing and material reflection feature libraries, it achieves a quantitative assessment of positioning accuracy risks, ensuring the navigation safety and reliability of UAV path planning in densely built-up areas.

[0028] To facilitate understanding of this embodiment, the embodiments of the present invention will be described in detail below.

[0029] Example 1: Figure 1 The flowchart is provided for the PDOP risk assessment modeling method based on low-altitude environment perception in Embodiment 1 of the present invention.

[0030] Reference Figure 1 The method includes the following steps: Step S101: Calculate the dynamic threshold function; Step S102: Construct the corrected visual satellite set based on the dynamic threshold function; Step S103: Obtain GNSS ephemeris data and candidate locations of the UAV in three-dimensional space; Step S104: Obtain the position vector of each navigation satellite at any given time based on GNSS ephemeris data; Step S105: Based on the candidate position of the UAV in three-dimensional space, the corrected set of visible satellites, and the position vectors of each navigation satellite at any time, construct the actual observation matrix corresponding to the center point of each unit grid; wherein, the actual observation matrix is ​​used to characterize the unit vector direction of each visible satellite relative to the receiver; Here, a three-dimensional gridded model is constructed in the target airspace, and the model is divided into multiple unit grid center points according to the height layer and geographic coordinates; then, the actual observation matrix corresponding to each unit grid center point is constructed.

[0031] Step S106: Calculate the actual PDOP value corresponding to the center point of each unit grid based on the diagonal elements of the actual observation matrix; Step S107: Generate the actual three-dimensional distribution field of PDOP based on the actual observation matrix and actual PDOP value corresponding to the center point of each unit grid. Step S108: Construct a multipath error model; Step S109: The multipath error model and the actual three-dimensional distribution field of PDOP are organically integrated to obtain the GNSS integrated positioning risk index.

[0032] Here, by calculating the actual PDOP value corresponding to the center point of each unit grid, and combining it with building occlusion and multipath interference correction, the GNSS integrated positioning risk index is obtained.

[0033] Specifically, this application constructs a real three-dimensional distribution field of PDOP by integrating three-dimensional environmental features, which can improve the accuracy and reliability of GNSS positioning accuracy assessment in complex low-altitude airspace. Compared with static PDOP calculation that relies solely on satellite geometry, it can effectively quantify the risk of positioning accuracy attenuation in complex scenarios such as building obstruction and multipath interference, achieve high-precision risk assessment modeling with spatial continuity, provide key positioning safety assurance technology for UAV navigation, and lay the technical foundation for safe flight in low-altitude densely built-up areas.

[0034] Position vectors of navigation satellites at any given time are obtained based on high-precision GNSS ephemeris data. Combining the candidate positions of the UAV in three-dimensional space The system determines the set of visible satellites and constructs the original observation matrix G based on geometric relationships. The determination of visible satellites takes into account whether they are obstructed by terrain or buildings, which is achieved through geometric interaction detection between spatial line segments and obstructions.

[0035] The position of each visible satellite in the Earth-centered Earth-fixed coordinate system at the time of observation is calculated using ephemeris, and denoted as . .

[0036] For a position is drones and visual satellite clusters , Then the first The method for calculating the original observation matrix of the row is as follows: (1) in, For drones to the first The geometric distance between the satellites is calculated using formula (2): (2) Therefore, the complete original observation matrix G is shown in equation (3): (3) in, Candidate locations for the UAV in three-dimensional space. For a collection of visible satellites, The original observation matrix corresponding to the center point of each unit grid.

[0037] The constructed original observation matrix reflects the unit vector direction of each visible satellite relative to the receiver. The least squares method is then used to estimate the error covariance matrix of the receiver position, as shown in formula (4): (4) in, Let be the error covariance matrix. This is the original observation matrix. This is the transpose of the original observation matrix.

[0038] Among them, in the diagonal elements In this context, the PDOP value is defined as shown in formula (5): (5) in, The PDOP value corresponding to the center point of each unit grid. The variance of the positioning error in the x-coordinate direction. Let be the variance of the positioning error in the y-coordinate direction. Let be the variance of the positioning error in the z-coordinate direction.

[0039] To quantify the PDOP distribution across the entire airspace, the set of visible satellites is enumerated at the center point of each unit grid, and the corresponding original observation matrix and PDOP value are calculated, ultimately generating a three-dimensional PDOP distribution field covering the low-altitude airspace.

[0040] This distribution field intuitively reveals the impact of factors such as building obstruction and terrain occlusion on GNSS positioning accuracy, which can provide a quantitative basis for accuracy risk modeling in UAV path planning and effectively support low-altitude navigation safety assessment and path optimality analysis.

[0041] Furthermore, step S101 includes the following steps: Step S201: Using a ray tracing algorithm, simulate the signal of each satellite from its spatial position. To the current location of the drone The propagation path; Step S202: Based on the propagation path, terrain, and 3D building model, perform occlusion detection to determine the connection between the UAV and the satellite. The visibility status; refer to formula (8) for details. Step S203: Calculate the occlusion probability at time t based on the visibility status; Step S204: Calculate the dynamic threshold function based on the occlusion probability at time t.

[0042] Furthermore, step S204 includes: Calculate the dynamic threshold function according to formula (6): (6) in, For dynamic threshold function, The basic signal strength threshold (the minimum usable value for a GNSS receiver in free space). (as a benchmark for function output). As an environmentally sensitive parameter, it reflects the maximum possible... The increment needs to be set according to the experimental environment. Through field experiments, different candidate upper limits are set, and the average error and number of available satellites for each candidate upper limit are statistically analyzed to find the maximum value that guarantees a visible number of 5 satellites. value, The larger the value, the more conservatively potential errors are eliminated; The rate of change coefficient represents the response speed of the control function to the occlusion probability, which can be determined based on the half-saturation point of the response speed (i.e., the dynamic function equals...). (Time) By working backward, it can be deduced that the threshold should be set according to the scenario. In scenarios requiring fast response and extreme conservatism, the threshold should be increased even with slight occlusion. Higher tolerance, suitable for navigation safety-critical areas; when a smooth response and greater fault tolerance are required, Smaller size, suitable for areas with less obstruction; Let be the occlusion probability at time t.

[0043] Furthermore, step S203 includes: The occlusion probability at time t is calculated using formulas (7) and (8): (7) (8) in, Let be the occlusion probability at time t. The visibility status is represented by N, which is the total number of visible satellites at time t.

[0044] Specifically, based on the visibility state, a dynamic threshold function is introduced. The criteria for judging the availability of actual satellite signal strength are used to adjust the dynamic changes in signal quality at different times and under different environments, as shown in formula (6).

[0045] Furthermore, step S102 includes the following steps: Step S301: When the received signal strength is greater than or equal to the dynamic threshold function, it is determined to be a valid satellite; Step S302: Construct a corrected set of visible satellites based on the available satellites.

[0046] Specifically, this threshold can be dynamically adjusted based on ionospheric activity, building density, or historical signal strength statistics. This dynamic threshold is calculated... Make a judgment when the received signal strength is greater than or equal to At that time, satellites that are deemed truly valid are identified, and then a revised set of visible satellites is constructed. Used to replace the visual satellite set under traditional line-of-sight judgment , refer to formula (9): (9) in, To receive signal strength.

[0047] In other words, a satellite will only be included in the revised visible satellite set if it simultaneously meets both the conditions of "no obstruction" and "sufficient signal strength". This solves the problem of traditional methods relying solely on geometric visibility, and avoids errors introduced by satellites that are "visible but have weak signals".

[0048] Finally, the revised visual satellite set This is used to recalculate the actual observation matrix and the actual covariance matrix, thereby obtaining a more accurate actual PDOP value. The calculation process here is similar to that using a visual satellite set... The calculation process for the PDOP value is similar and can be found in formulas (1) to (5). This method significantly improves positioning performance in obstructed environments such as canyons and mountains, providing more reliable spatial positioning support for path planning and flight control.

[0049] Furthermore, step S108 includes: (10) in, Spatial location Multipath error model at the location, For the number of building material types, For the first The multipath perturbation weighting coefficient of a material reflects its average reflection perturbation intensity to GNSS signals (e.g., metal is greater than concrete, which is greater than wood, etc.). For the material in spatial position The reflection influence factor is used to characterize the relationship between the incident direction and the material. The angle between surface normals. Refer to formula (11): (11) Specifically, navigation errors are not only positively correlated with building density, but also constrained by factors such as building materials. For example, metal has a much higher reflectivity than concrete, and strong multipath propagation can occur even at low density. Therefore, it is necessary to further introduce the multipath interference effect of buildings, construct a multipath error model, and organically integrate it with the actual three-dimensional distribution field of PDOP to form a unified GNSS integrated positioning risk index.

[0050] This model can construct a multipath error distribution field covering the entire airspace based on the three-dimensional distribution of buildings, enabling fine-grained characterization of the impact of multipath errors in spatial locations.

[0051] In order to achieve a unified assessment with the actual three-dimensional distribution field of PDOP, this application further proposes a GNSS integrated positioning risk index, as shown in formula (12).

[0052] Furthermore, step S109 includes: Calculate the GNSS integrated positioning risk index according to formula (12): (12) in, For GNSS integrated positioning risk indicators, This represents the actual three-dimensional distribution field of PDOP. Spatial location Multipath error model at the location, This represents the standard error value of a GNSS system under free-space conditions.

[0053] This model normalizes the multipath error to achieve consistency with the PDOP dimension, thereby enabling the comprehensive index to have both geometric structure and non-geometric interference characteristics, and to perform fusion modeling and spatial characterization of GNSS positioning errors in low-altitude three-dimensional environments.

[0054] Finally, a risk assessment is conducted based on the GNSS integrated positioning risk index. This application achieves unified modeling and spatial representation of the risk of GNSS positioning accuracy attenuation in complex airspace, providing accurate and interpretable risk map support for subsequent tasks such as path planning, track design, and accuracy assurance zone delineation.

[0055] This application extends traditional satellite geometry analysis to a three-dimensional evaluation system that integrates building occlusion and multipath effects, enabling high-precision quantification of positioning accuracy degradation risks in canyon environments. By accurately measuring the risk level of each grid cell, it ensures navigation safety for UAV path planning and improves the reliability of low-altitude flight missions. This technology provides crucial support for the safe operation of UAVs in densely built-up areas and has broad application prospects in logistics delivery, inspection, and other fields.

[0056] Example 2: Figure 2 This is a schematic diagram of the PDOP risk assessment modeling system based on low-altitude environment perception provided in Embodiment 2 of the present invention.

[0057] Reference Figure 2 The system includes: The modified visible satellite set construction module is used to calculate the dynamic threshold function and construct the modified visible satellite set based on the dynamic threshold function; The candidate location acquisition module is used to acquire GNSS ephemeris data and candidate locations of the UAV in three-dimensional space; The position vector acquisition module is used to obtain the position vector of each navigation satellite at any given time based on GNSS ephemeris data; The actual observation matrix construction module is used to construct the actual observation matrix corresponding to the center point of each unit grid based on the candidate position of the UAV in three-dimensional space, the corrected set of visible satellites, and the position vector of each navigation satellite at any time; wherein, the actual observation matrix is ​​used to characterize the unit vector direction of each visible satellite relative to the receiver; The actual PDOP value calculation module is used to calculate the actual PDOP value corresponding to the center point of each unit grid based on the diagonal elements of the actual observation matrix. The generation module is used to generate the actual three-dimensional distribution field of PDOP based on the actual observation matrix and actual PDOP value corresponding to the center point of each unit grid. The multipath error model building module is used to build multipath error models. The fusion module is used to organically integrate the multipath error model and the actual three-dimensional distribution field of PDOP to obtain the GNSS integrated positioning risk index.

[0058] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the PDOP risk assessment modeling method based on low-altitude environment perception provided in the above embodiments.

[0059] This invention also provides a computer-readable medium having processor-executable non-volatile program code, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the PDOP risk assessment modeling method based on low-altitude environment perception described in the above embodiments.

[0060] The computer program product provided in this embodiment of the invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0061] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0062] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0063] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0064] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0065] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A PDOP risk assessment modeling method based on low-altitude environmental perception, characterized in that, The method includes: Calculate the dynamic threshold function, and construct the corrected visible satellite set based on the dynamic threshold function; Acquire GNSS ephemeris data and candidate locations of UAVs in three-dimensional space; Based on the GNSS ephemeris data, obtain the position vector of each navigation satellite at any given time; Based on the candidate position of the UAV in the three-dimensional space, the corrected set of visible satellites, and the position vectors of each navigation satellite at any given time, an actual observation matrix corresponding to the center point of each unit grid is constructed; wherein, the actual observation matrix is ​​used to characterize the unit vector direction of each visible satellite relative to the receiver; Calculate the actual PDOP value corresponding to each unit grid center point based on the diagonal elements of the actual observation matrix; Based on the actual observation matrix and the actual PDOP value corresponding to each unit grid center point, generate the actual three-dimensional distribution field of PDOP; Construct a multipath error model; The multipath error model and the actual three-dimensional distribution field of the PDOP are organically integrated to obtain the GNSS integrated positioning risk index.

2. The PDOP risk assessment modeling method based on low-altitude environmental perception according to claim 1, characterized in that, Calculating the dynamic threshold function includes: A ray tracing algorithm is used to simulate the propagation path of each satellite signal from its spatial location to the current location of the UAV; Based on the propagation path, terrain, and 3D building model, occlusion detection is performed to determine the visibility status of the connection between the UAV and the satellite. Based on the visibility state, calculate the occlusion probability at time t; The dynamic threshold function is calculated based on the occlusion probability at time t.

3. The PDOP risk assessment modeling method based on low-altitude environmental perception according to claim 2, characterized in that, The dynamic threshold function is calculated based on the occlusion probability at time t, including: The dynamic threshold function is calculated according to the following formula: in, The dynamic threshold function is... Based on the basic signal strength threshold, As an environmentally sensitive parameter, The rate of change coefficient, Let be the occlusion probability at time t.

4. The PDOP risk assessment modeling method based on low-altitude environmental perception according to claim 2, characterized in that, Based on the visibility state, the occlusion probability at time t is calculated, including: The occlusion probability at time t is calculated using the following formula: in, Let be the occlusion probability at time t. The visibility state is defined as N, where N is the total number of visible satellites at time t.

5. The PDOP risk assessment modeling method based on low-altitude environmental perception according to claim 1, characterized in that, The modified visible satellite set is constructed based on the dynamic threshold function, including: When the received signal strength is greater than or equal to the dynamic threshold function, it is determined to be a valid satellite; The modified visual satellite set is constructed based on the valid satellites.

6. The PDOP risk assessment modeling method based on low-altitude environmental perception according to claim 1, characterized in that, Constructing a multipath error model includes: in, Spatial location The multipath error model at that location, For the number of building material types, For the first Multipath perturbation weighting coefficients for similar materials The reflection influence factor of the material in space is used to characterize the relationship between the incident direction and the material. The angle between surface normals.

7. The PDOP risk assessment modeling method based on low-altitude environmental perception according to claim 1, characterized in that, By organically fusing the multipath error model and the actual three-dimensional distribution field of the PDOP, a comprehensive GNSS positioning risk index is obtained, including: The GNSS integrated positioning risk index is calculated according to the following formula: in, This refers to the GNSS integrated positioning risk index. This represents the actual three-dimensional distribution field of the PDOP. Spatial location The multipath error model at that location, This represents the standard error value of a GNSS system under free-space conditions.

8. A PDOP risk assessment and modeling system based on low-altitude environmental perception, characterized in that, The system includes: The modified visible satellite set construction module is used to calculate a dynamic threshold function and construct a modified visible satellite set based on the dynamic threshold function. The candidate location acquisition module is used to acquire GNSS ephemeris data and candidate locations of the UAV in three-dimensional space; The position vector acquisition module is used to acquire the position vector of each navigation satellite at any time based on the GNSS ephemeris data. The actual observation matrix construction module is used to construct the actual observation matrix corresponding to the center point of each unit grid based on the candidate position of the UAV in the three-dimensional space, the corrected set of visible satellites, and the position vector of each navigation satellite at any time; wherein, the actual observation matrix is ​​used to characterize the unit vector direction of each visible satellite relative to the receiver; The actual PDOP value calculation module is used to calculate the actual PDOP value corresponding to each unit grid center point based on the diagonal elements of the actual observation matrix. The generation module is used to generate the actual three-dimensional distribution field of PDOP based on the actual observation matrix and the actual PDOP value corresponding to each unit grid center point; The multipath error model building module is used to build multipath error models. The fusion module is used to organically fuse the multipath error model and the actual three-dimensional distribution field of the PDOP to obtain the GNSS integrated positioning risk index.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the method described in any one of claims 1 to 7.

10. A computer-readable medium having processor-executable non-volatile program code, characterized in that, The program code causes the processor to execute the method according to any one of claims 1 to 7.

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