GNSS signal enhancement method and device based on UAV signal relay

Abstract

The present disclosure provides a GNSS signal enhancement method and device based on unmanned aerial vehicle signal relay, relating to the technical field of dam slope monitoring. The method comprises: determining a signal to be enhanced area in the dam slope area; scheduling a first unmanned aerial vehicle cluster to hover and stay in each signal to be enhanced area to form a signal enhancement network; screening a signal blind area based on the signal-to-noise ratio monitored by each network node; scheduling a second unmanned aerial vehicle cluster to the dynamic blind-filling position corresponding to the signal blind area to form a signal blind-filling network; based on the signal enhancement network and the signal blind-filling network, the received GNSS satellite signal is signal enhanced and directionally emitted to the GNSS signal receiver to realize dam slope deformation monitoring. The technical solution can effectively enhance the GNSS satellite signal, improve the stability and accuracy of the GNSS signal receiver in monitoring the deformation of the dam slope area in bad weather.

Description

Technical Field

[0001] The present disclosure relates to the technical field of dam slope monitoring, and in particular to a GNSS signal enhancement method and device based on drone signal relay. Background Art

[0002] In the fields of geological disaster monitoring and dam safety and maintenance, the Global Navigation Satellite System (GNSS) has been widely used in scenarios such as dam slope deformation monitoring and dam structural stability assessment due to its high precision, continuity, and full automation. However, due to the complex terrain of dam slopes, particularly in canyons, steep slopes, or heavily obstructed areas, GNSS satellite signal attenuation is severe, even leading to signal blind spots, which affect the accuracy and continuity of positioning data.

[0003] In relevant technical solutions, the signal blocking problem is usually alleviated by deploying more ground-based GNSS signal receivers in the dam slope area or adjusting the angle of the receiving antenna. However, these methods are limited by factors such as terrain characteristics and construction conditions, and are difficult to operate continuously and effectively in areas with complex terrain and severe signal attenuation. In addition, some technical solutions attempt to introduce signal relay equipment set at fixed positions on the ground. However, due to structural defects such as fixed deployment positions and limited signal coverage range, their signal coverage capabilities are insufficient under dynamic environmental changes, and they cannot flexibly respond to real-time changes in the spatial distribution of signal quality.

[0004] Therefore, there is an urgent need to provide a solution for enhancing GNSS satellite signal coverage with flexible and adjustable positions, dynamically controllable coverage range, and highly adaptable deployment methods, so as to meet the needs of GNSS signal enhancement and relay under complex terrain conditions.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0006] The purpose of the embodiments of the present disclosure is to provide a GNSS signal enhancement method and device based on drone signal relay, which can effectively enhance GNSS satellite signals and improve the stability and accuracy of GNSS signal receivers in deformation monitoring of dam slope areas under severe weather conditions.

[0007] According to a first aspect of an embodiment of the present disclosure, a GNSS signal enhancement method based on drone signal relay is provided, comprising:

[0008] Acquire pre-collected GNSS signal characteristic data and terrain characteristic data corresponding to the dam slope area, and determine a plurality of signal enhancement areas based on the GNSS signal characteristic data and the terrain characteristic data;

[0009] Dispatching the first drone cluster to hover and stay at a preset hovering height in each of the signal enhancement areas to form a signal enhancement network, wherein each enhanced drone in the first drone cluster constitutes a network node of the signal enhancement network;

[0010] Determining a signal-to-noise ratio (SNR) of GNSS satellite signals received by each network node in the signal enhancement network; if the SNR of a target network node is less than or equal to a preset SNR threshold, determining a signal enhancement area corresponding to the target network node as a signal blind spot;

[0011] Dispatching a second drone cluster to a dynamic blind spot filling position corresponding to the signal blind spot to form a signal blind spot filling network, wherein each blind spot filling drone in the second drone cluster constitutes a network node of the signal blind spot filling network, and the dynamic blind spot filling position is determined based on the real-time updated distribution of the enhanced drones in each of the signal blind spots;

[0012] Based on the constructed signal enhancement network and the signal blind spot network, the received GNSS satellite signal is enhanced and directionally transmitted to the GNSS signal receivers installed at each slope deformation monitoring point in the dam slope area to achieve signal enhancement of the GNSS signal receiver.

[0013] In an exemplary embodiment of the present disclosure, determining a plurality of signal enhancement areas based on the GNSS signal characteristic data and the terrain characteristic data includes:

[0014] Divide the dam slope area according to the signal coverage parameters of the enhanced UAV to determine multiple monitoring sub-areas;

[0015] Determining the GNSS signal strength of each monitoring sub-area according to the GNSS signal characteristic data, and determining the terrain shielding degree of each monitoring sub-area according to the terrain characteristic data;

[0016] Determining a signal quality score for each monitoring sub-area based on the GNSS signal strength and the terrain obstruction degree;

[0017] The monitoring sub-areas whose signal quality scores are less than or equal to a preset score threshold are marked as the signal to-be-enhanced areas, thereby obtaining a plurality of signal to-be-enhanced areas.

[0018] In an exemplary embodiment of the present disclosure, the dam slope area is divided according to the signal coverage parameters of the enhanced drone to determine multiple monitoring sub-areas, including:

[0019] Determining the signal enhancement coverage radius of the enhanced UAV, and determining the signal coverage area on the dam slope area according to the preset hovering height and the signal enhancement coverage radius;

[0020] Determining a plurality of weak signal distribution aggregation points in the dam slope area based on stored historical signal quality distribution data, wherein each of the weak signal distribution aggregation points corresponds to a GNSS signal receiver;

[0021] With the goal of covering all the weak signal distribution points with the minimum signal coverage area, target signal coverage areas at multiple different locations are determined, and the target signal coverage areas are used as the monitoring sub-areas.

[0022] In an exemplary embodiment of the present disclosure,

[0023] The determining target signal coverage areas at a plurality of different locations and using the target signal coverage areas as the monitoring sub-areas includes:

[0024] The target signal coverage areas whose center points are spaced apart by a distance less than or equal to a preset distance are merged to obtain multiple monitoring sub-areas.

[0025] In an exemplary embodiment of the present disclosure,

[0026] The first drone cluster is dispatched to hover at a preset hovering height in each of the signal enhancement areas to form a signal enhancement network, including:

[0027] Based on the preset hovering height, the space above the area to be enhanced is divided into spatial grids, and the signal transmission stability of the enhanced drone at each spatial grid is determined based on the drone parameters of the enhanced drone;

[0028] Select at least two hovering points according to the signal transmission stability;

[0029] In real time, based on the received GNSS signal quality feedback from each enhanced UAV in the first UAV cluster, the hovering point of each enhanced UAV in the corresponding signal to be enhanced area is adjusted to ensure the quality of the enhanced GNSS satellite signal received by all GNSS signal receivers within the coverage range of the signal enhancement network.

[0030] In an exemplary embodiment of the present disclosure, the dispatching of the second drone cluster to the dynamic blind spot position corresponding to the signal blind spot to form a signal blind spot network includes:

[0031] Determine the blind spot deployment point based on the current hovering position of the enhancement drone in the first drone cluster corresponding to each of the signal blind spots, and in combination with the signal enhancement coverage radius of the blind spot filling drone and the preset blind spot filling height;

[0032] Each blind spot filling UAV in the second UAV cluster is dispatched to fly to the blind spot filling deployment point, and hovers at a preset blind spot filling height higher than the corresponding enhanced UAV to build the signal blind spot filling network.

[0033] In an exemplary embodiment of the present disclosure, when there is only one enhancement drone within the signal coverage space formed by the signal enhancement coverage radius of the blind spot filling drone, determining the blind spot filling deployment point includes:

[0034] Determine multiple candidate deployment points so that the current hovering position of the enhancement drone corresponding to the signal blind spot is within the signal enhancement coverage radius of the blind spot-filling drone;

[0035] At least one blind spot filling deployment point is selected from the plurality of candidate deployment points using the preset blind spot filling height as a restriction condition.

[0036] In an exemplary embodiment of the present disclosure, when there are two or more enhancement drones within the signal coverage space formed by the signal enhancement coverage radius of the blind spot filling drone, determining the blind spot filling deployment point includes:

[0037] Determining an offset ratio with respect to each of the enhanced drones based on a signal-to-noise ratio of a GNSS satellite signal received by the enhanced drone, wherein the smaller the signal-to-noise ratio of the enhanced drone, the larger the offset ratio, and the closer the blind spot deployment point will be to the enhanced drone;

[0038] Determining a plurality of candidate deployment points such that the current hovering position of each of the enhancement drones is within the signal enhancement coverage radius of the blind spot filling drone;

[0039] At least one blind spot filling deployment point is selected from the plurality of candidate deployment points using the offset ratio and the preset blind spot filling height as restriction conditions.

[0040] In an exemplary embodiment of the present disclosure, the enhanced drone or the blind spot filling drone includes:

[0041] A flight control module is used to control the UAV to fly to the deployment position corresponding to the signal enhancement area or the signal blind area, and to hover stably at a set hovering height;

[0042] A GNSS signal receiving module, configured to receive GNSS satellite signals from multiple GNSS satellite systems;

[0043] a signal enhancement and relay module, electrically connected to the GNSS signal receiving module, for amplifying and filtering the received GNSS satellite signals, and directionally transmitting the enhanced GNSS satellite signals to a GNSS signal receiver on the ground through a relay transmitting antenna;

[0044] A signal quality sensing module is used to detect the signal-to-noise ratio, number of visible satellites or multipath interference characteristics of the GNSS signal received by the UAV in real time, and send the signal quality data to the ground control center;

[0045] A communication module is used for communication and interaction between the UAV and other UAV nodes and the ground control system to achieve collaborative construction and topology optimization of signal enhancement networks or signal blind spot filling networks;

[0046] The energy management module is used to manage the power supply system of the UAV and send a replacement request to the ground control center or activate a spare UAV node to fill the position when the power supply of the power supply system is lower than a preset power threshold.

[0047] According to a second aspect of an embodiment of the present disclosure, a GNSS signal enhancement device based on drone signal relay is provided, comprising:

[0048] an area determination module, configured to obtain pre-collected GNSS signal characteristic data and terrain characteristic data corresponding to the dam slope area, and determine a plurality of signal enhancement areas based on the GNSS signal characteristic data and the terrain characteristic data;

[0049] A signal enhancement network construction module is used to dispatch the first drone cluster to hover and reside at a preset hovering height in each of the signal enhancement areas to form a signal enhancement network, wherein each enhanced drone in the first drone cluster constitutes a network node of the signal enhancement network;

[0050] a signal blind spot screening module, configured to determine the signal-to-noise ratio of the GNSS satellite signals received by each network node in the signal enhancement network; and if the signal-to-noise ratio of the target network node is less than or equal to a preset signal-to-noise ratio threshold, determining the signal enhancement area corresponding to the target network node as a signal blind spot;

[0051] A signal blind spot network construction module is used to dispatch a second UAV cluster to a dynamic blind spot location corresponding to the signal blind spot to form a signal blind spot network. Each blind spot filling UAV in the second UAV cluster constitutes a network node of the signal blind spot network. The dynamic blind spot filling location is determined based on the real-time updated distribution of the enhanced UAVs in each of the signal blind spots.

[0052] The signal enhancement module is used to enhance the received GNSS satellite signal based on the constructed signal enhancement network and the signal blind spot network, and transmit it directionally to the GNSS signal receivers set at each slope deformation monitoring point in the dam slope area to achieve signal enhancement for the GNSS signal receiver.

[0053] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects:

[0054] The GNSS signal enhancement method based on drone signal relay provided in the example embodiment of the present disclosure, on the one hand, determines multiple signal enhancement areas in the dam slope area through GNSS signal feature data and terrain feature data, and combines the hierarchical scheduling deployment of the first drone cluster and the second drone cluster. It can effectively avoid the problems of spatial attenuation, uneven occlusion, dynamic changes in blind spot distribution, etc. of GNSS signals in complex terrain in related technologies. Without relying on fixed base stations, it can realize spatial reconstruction and link reinforcement of areas with weak signal coverage, improve the stability and signal quality of GNSS satellite signals received by GNSS signal receivers, and thus ensure the accuracy of slope deformation monitoring results; on the other hand, through the signal enhancement network composed of the enhanced drones in the first drone cluster, the signal enhancement basis for the dam slope area is realized. On this basis, through the signal enhancement network composed of the enhanced drones in the first drone cluster, the signal enhancement basis for the dam slope area is realized. By enhancing the real-time signal-to-noise ratio collected by each network node in the network, identifying signal blind spots, and then dispatching the second drone cluster to build a signal blind spot network, it is possible to add blind spot enhancement paths with less aerial obstruction in areas with severe signal obstruction or signal attenuation in the dam slope area, forming a multi-node, multi-level spatial link reinforcement architecture. The dynamic generation of blind spot deployment positions in this structure is combined with the actual hovering position and distribution pattern of the enhanced drones to avoid the situation where the fixed deployment structure cannot cope with the expansion of blind spots or changes in spatial obstruction, resulting in the inability to compensate for signals. This further improves the signal transmission strength while taking into account the flexibility of the system structure and the robustness of the signal enhancement network, thereby providing GNSS signal services with stable coverage and timely response for GNSS signal receivers, especially ensuring the stability and accuracy of GNSS signal receivers in deformation monitoring of dam slope areas in severe weather.

[0055] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0057] Figure 1 A flow chart of a GNSS signal enhancement method based on drone signal relay in an embodiment of the present disclosure is shown.

[0058] Figure 2 A schematic diagram of a process for screening and determining a signal area to be enhanced in an embodiment of the present disclosure is shown.

[0059] Figure 3 A schematic diagram of a process for determining a hovering point of an enhanced drone in an area to be signal enhanced in an embodiment of the present disclosure is shown.

[0060] Figure 4 A schematic diagram of a process for determining a blind spot deployment point of a blind spot filling drone in a signal blind spot is shown in an embodiment of the present disclosure.

[0061] Figure 5 A schematic diagram of an application scenario of GNSS signal enhancement based on drone signal relay in an embodiment of the present disclosure is shown.

[0062] Figure 6 A schematic diagram of another application scenario of GNSS signal enhancement based on drone signal relay in an embodiment of the present disclosure is shown.

[0063] Figure 7 A schematic diagram of a GNSS signal enhancement device based on drone signal relay in an embodiment of the present disclosure is shown.

[0064] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts. DETAILED DESCRIPTION

[0065] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. As used in this specification and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0066] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, such information should not be limited to these terms. These terms are merely used to distinguish information of the same type from one another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information without departing from the scope of this specification. Depending on the context, the term "if" as used herein may be interpreted as "when," "when," or "in response to determining."

[0067] The embodiment of the present disclosure first provides a GNSS signal enhancement method based on drone signal relay, and the following describes the method by taking the server of the ground control center as an example. Figure 1 The flowchart of a GNSS signal enhancement method based on drone signal relay in an embodiment of the present disclosure is schematically shown. Figure 1 As shown, the method may include steps S110 to S150:

[0068] Step S110, obtaining pre-collected GNSS signal characteristic data and terrain characteristic data corresponding to the dam slope area, and determining a plurality of signal enhancement areas based on the GNSS signal characteristic data and the terrain characteristic data;

[0069] Step S120: dispatching the first drone cluster to hover at a preset hovering height in each of the signal enhancement areas to form a signal enhancement network, wherein each enhanced drone in the first drone cluster constitutes a network node of the signal enhancement network;

[0070] Step S130: determining the signal-to-noise ratio of the GNSS satellite signals received by each network node in the signal enhancement network; if the signal-to-noise ratio of the target network node is less than or equal to a preset signal-to-noise ratio threshold, determining the signal enhancement area corresponding to the target network node as a signal blind spot;

[0071] Step S140: dispatching a second drone cluster to a dynamic blind spot filling position corresponding to the signal blind spot to form a signal blind spot filling network. Each blind spot filling drone in the second drone cluster constitutes a network node of the signal blind spot filling network. The dynamic blind spot filling position is determined based on the real-time updated distribution of the enhanced drones in each of the signal blind spots.

[0072] In step S150, based on the constructed signal enhancement network and the signal blind spot network, the received GNSS satellite signal is enhanced and directionally transmitted to the GNSS signal receivers installed at each slope deformation monitoring point in the dam slope area to achieve signal enhancement for the GNSS signal receiver.

[0073] According to the GNSS signal enhancement method based on drone signal relay disclosed in the present invention, on the one hand, multiple signal enhancement areas are determined in the dam slope area through GNSS signal feature data and terrain feature data, and combined with the hierarchical scheduling deployment of the first drone cluster and the second drone cluster, it can effectively avoid the problems of spatial attenuation, uneven occlusion, dynamic changes in blind spot distribution, etc. of GNSS signals in complex terrain in related technologies. Without relying on fixed base stations, it can realize spatial reconstruction and link reinforcement of areas with weak signal coverage, improve the stability and signal quality of GNSS satellite signals received by GNSS signal receivers, and thus ensure the accuracy of slope deformation monitoring results; on the other hand, through the signal enhancement network composed of the enhanced drones in the first drone cluster, the signal enhancement basis for the dam slope area is realized. On this basis, through signal enhancement The real-time signal-to-noise ratio collected by each network node in the network is used to identify signal blind spots, and then the second drone cluster is dispatched to build a signal blind spot network. This can add blind spot enhancement paths with less aerial obstruction in areas with severe signal obstruction or signal attenuation in the dam slope area, forming a multi-node, multi-level spatial link reinforcement architecture. The dynamic generation of blind spot deployment positions in this structure is combined with the actual hovering position and distribution pattern of the enhanced drones to avoid the situation where the fixed deployment structure cannot cope with the expansion of blind spots or changes in spatial obstruction, resulting in the inability to compensate for signals. This further improves the signal transmission strength while taking into account the flexibility of the system structure and the robustness of the signal enhancement network, thereby providing GNSS signal services with stable coverage and timely response for GNSS signal receivers, especially ensuring the stability and accuracy of GNSS signal receivers in deformation monitoring of dam slope areas in severe weather.

[0074] Below, the GNSS signal enhancement method based on drone signal relay in the embodiment of the present disclosure will be described in detail.

[0075] In step S110 , pre-collected GNSS signal characteristic data and terrain characteristic data corresponding to the dam slope area are acquired, and a plurality of signal enhancement areas are determined based on the GNSS signal characteristic data and the terrain characteristic data.

[0076] In an example embodiment of the present disclosure, GNSS signal characteristic data refers to measurement parameters related to GNSS signal propagation recorded by deployed GNSS receiving equipment, mobile collection terminals, or historical databases within the target area. For example, GNSS signal characteristic data may be the signal-to-noise ratio (SNR) of each sampling point, the number of visible satellites, the geometric dilution of precision (GDOP) of the satellite arrangement, the multipath interference amplitude, and the signal loss rate. Of course, this embodiment does not specifically limit the type of GNSS signal characteristic data. GNSS signal characteristic data may be real-time collection data or offline imported monitoring log files. It may be collected by a GNSS observation terminal equipped with multi-system and multi-band receiving capabilities, and these data may be organized according to a spatial grid structure or a measurement point coordinate system for fusion analysis with terrain feature data.

[0077] Terrain feature data refers to a set of spatial data that describes the three-dimensional spatial structure of the terrain within the target area. For example, terrain feature data can include a high-resolution digital elevation model (DEM), an oblique photography model, a lidar point cloud model, or a synthetic aperture radar interferogram (InSAR DEM). Terrain feature data can be used to extract indicators such as slope, aspect, obstruction angle, relative height difference, reflective surface distribution, and degree of concavity. These indicators will serve as terrain input factors for determining the degree of GNSS signal obstruction and the reliability of the signal transmission path. Optionally, the terrain feature data may also include surface cover type (such as bare rock, vegetation, structures) or reflection characteristic parameters for correcting and modeling the effects of multipath interference. In this embodiment, the GNSS signal feature data and the terrain feature data can be established in a corresponding relationship through a spatial alignment algorithm to form an input data matrix for subsequent area division.

[0078] Signal enhancement areas are defined as terrestrial regions that exhibit high signal attenuation or chronically poor signal quality during GNSS signal transmission. For example, these areas can be characterized by a significantly lower signal-to-noise ratio (SNR) than surrounding areas, a chronically insufficient number of visible satellites, or significant fluctuations in positioning error. The identification of signal enhancement areas can be based on the enhanced drone's signal coverage capability. Using the enhanced drone's signal coverage radius as a metric, a regular or adaptive spatial grid is created on the terrain model, with each grid cell representing a candidate monitoring sub-area. Key GNSS signal characteristic metrics (such as average SNR, GDOP, and number of visible satellites) can then be combined with input variables such as obstruction angle and aspect from terrain data to create a multi-factor scoring system. For example, a logistic weighting model, support vector machine model, or neural network regression model can be used to achieve regional scoring. The obstruction angle is the sum of the angles of obstacles within the typical elevation range of GNSS satellites, projected horizontally from a hypothetical height (e.g., 30 meters) above the monitoring sub-area. This angle quantifies the degree of terrain obstruction, while the signal-to-noise ratio (SNR) reflects actual signal strength and stability. By setting a scoring threshold, the monitoring sub-areas with scores lower than the threshold are marked as signal enhancement areas, and their corresponding spatial range, center point coordinates or priority levels are output for subsequent enhanced drone deployment links.

[0079] In step S120, the first drone cluster is dispatched to hover at a preset hovering height in each of the signal enhancement areas to form a signal enhancement network, and each enhanced drone in the first drone cluster constitutes a network node of the signal enhancement network.

[0080] In an example embodiment of the present disclosure, the first drone cluster refers to a swarm system composed of multiple enhanced flight platforms equipped with GNSS signal receiving and relaying devices. The enhanced drones in the first drone cluster can have autonomous flight, fixed-point hovering, positioning capability monitoring and air relay communication functions. The enhanced drone refers to a flight unit used to perform GNSS signal reception, enhancement and directional relay transmission tasks in the target space area. The enhanced drone can receive GNSS satellite signals, perform low-noise amplification and filtering on the signals, and transmit enhanced signals downward through the relay antenna for use by ground GNSS receivers. In an optional embodiment, the enhanced drone can be a multi-rotor aircraft. The multi-rotor aircraft has excellent hovering stability and vertical take-off and landing capabilities, and can carry core components such as high-gain directional antennas, GNSS signal modules, flight control systems and communication modules. Of course, a fixed-wing vertical take-off and landing composite aircraft can also be used as an alternative platform. This embodiment does not specifically limit this.

[0081] The preset hovering height refers to a vertical hovering height set according to factors such as terrain undulation, signal coverage radius and airspace restriction, above the signal-to-be-enhanced area. The preset hovering height can generally be set to 20 meters to 80 meters, and can be dynamically adjusted according to the steepness of the slope. The setting of the preset hovering height not only needs to meet the propagation conditions of the GNSS signal relay link, but also should ensure that the signal gain area covers the entire target area, and tries to avoid local shielding or reflection interference. The setting of the preset hovering height can make the signal enhancement coverage radius cover the main observation points of the target area under the given antenna pattern and transmission power, while meeting the power consumption and endurance limitations of the unmanned aerial vehicle flight platform. The setting of the preset hovering height also needs to be customized in combination with the specific application scenario, and the present embodiment is not limited thereto.

[0082] The signal enhancement network refers to a multi-node structure composed of multiple enhancement unmanned aerial vehicles in the first unmanned aerial vehicle cluster in the air according to a predetermined arrangement rule, so as to have spatial coverage continuity, inter-node cooperation capability and link reconfigurability. In order to construct the signal enhancement network, the hovering residence points in each signal-to-be-enhanced area can be determined first. The hovering residence point refers to the specific position coordinates of the enhancement unmanned aerial vehicle performing the enhancement task in space. The hovering residence point can take into account signal transmission stability, GNSS signal fluctuation condition in the area, terrain shielding condition and deployment distance between adjacent unmanned aerial vehicles.

[0083] Optionally, a three-dimensional space grid structure can be constructed above the signal-to-be-enhanced area, each space grid representing an optional hovering position, and the signal enhancement capability of each point is evaluated in combination with flight simulation data and channel model. In the evaluation process, the elevation angle distribution of the GNSS satellite signal received from the point, the range of possible shielding angles, the line-of-sight path length and attenuation factor between the unmanned aerial vehicle and the target area and other data can be evaluated to calculate the reliability score of the signal relay path, and the hovering residence points with higher reliability scores are selected, and the distance between the hovering residence points is preferably slightly greater than the signal coverage radius of the enhancement unmanned aerial vehicle, so as to avoid redundant deployment, while leaving a certain overlap redundancy area to enhance the system robustness.

[0084] After the hovering residence points are selected, the system can issue a scheduling instruction to the first unmanned aerial vehicle cluster, and each enhancement unmanned aerial vehicle flies to the specified position according to the task instruction path and hovers at the corresponding height. The flight control process can realize precise hovering based on the dual-redundancy mechanism of GPS positioning and laser radar ranging, and can also use an inertial measurement unit (IMU), a visual positioning module or an ultrasonic sensor to realize micro-motion correction, to ensure the stability of the position under the condition of micro-wind disturbance or air disturbance.

[0085] After the hovering residence is completed, each enhanced unmanned aerial vehicle starts the GNSS signal receiving and relay process in turn, that is, taking the space position where the enhanced unmanned aerial vehicle is located as a receiving antenna platform to receive satellite signals, and after the signals are amplified and filtered, the signals are transmitted downward by a directional relay module to cover the target ground GNSS receiver. The multiple enhanced unmanned aerial vehicle nodes can also form a relay subnetwork through a wireless communication link for link redundancy, signal synchronization and control command forwarding, to improve the fault tolerance and expansion capability of the overall enhanced network of the system. Of course, the above deployment process is only illustrative, and other ways can also be used to implement deployment in combination with real scenarios, and the present embodiment is not limited thereto.

[0086] In step S130, the signal-to-noise ratio of the GNSS satellite signals received by each network node in the signal enhancement network is determined, and if the signal-to-noise ratio of the target network node is less than or equal to a preset signal-to-noise ratio threshold, the signal enhancement area corresponding to the target network node is determined as a signal blind area.

[0087] In an example embodiment of the present disclosure, the network node refers to each enhanced unmanned aerial vehicle in a stable hovering state in the signal enhancement network, which undertakes the task of GNSS signal receiving and forwarding. Each enhanced unmanned aerial vehicle is a signal enhancement network node. In order to accurately evaluate the signal environment of the space position where the network node is located, each network node can be equipped with a GNSS signal quality sensing module for collecting real-time signal strength data. For example, the GNSS signal quality sensing module can collect signal-to-noise ratio (SNR), carrier-to-noise ratio of GNSS system receiving channel, number of visible satellites, and satellite arrangement quality index (such as GDOP or PDOP).

[0088] The signal-to-noise ratio is a key parameter for describing the proportion of GNSS signal strength and background noise, and is usually measured in decibels (dB). The measurement method can also be based on the demodulation result inside the GNSS receiving module or obtained by sampling through an external power detector. In order to ensure the stability of the evaluation, the signal-to-noise ratio can be time series smoothed by using a sliding window average or median filtering processing method to remove the influence of short-term fluctuations or multipath disturbances. The preset signal-to-noise ratio threshold is a reference value for judging whether the current signal environment meets the enhancement requirement. The preset signal-to-noise ratio threshold can be set according to the target monitoring accuracy, receiver sensitivity and historical experience data, for example, the preset signal-to-noise ratio threshold can be set to 30 dB, 35 dB or 40 dB. Of course, the preset signal-to-noise ratio threshold can also be dynamically adjusted in combination with the current task level or terrain risk level, and set according to the actual application scenario, and the present example embodiment does not specially limit this.

[0089] The signal quality feedback data from each enhanced unmanned aerial node can be received periodically, and compared with whether the signal-to-noise ratio of the current node is lower than or equal to the preset signal-to-noise ratio threshold. If the signal-to-noise ratio of a certain node is lower than the preset signal-to-noise ratio threshold for multiple sampling periods, or accompanied by abnormal indicators such as a decrease in the number of visible satellites and an increase in GDOP, it is determined that there is a spatial hole in the signal enhancement area where the network node is located, which cannot be normally covered by the first layer enhancement network, that is, a signal blind area. To prevent misjudgment, the signal-to-noise ratio difference between adjacent network nodes can also be used to verify the judgment result for spatial consistency, and if necessary, a spatial interpolation model can also be introduced to finely estimate the blind area boundary to form a spatially continuous blind area identification result.

[0090] The area identified as a signal blind area will be marked as a blind area compensation target area and used as one of the input data for subsequent blind area compensation deployment. The deployment strategy of the second unmanned aerial cluster based on the labeling result can be dynamically generated, and the blind area compensation signal link structure for covering these blind areas can be constructed, thereby effectively solving the signal coverage hole problem caused by insufficient coverage capability of the first layer node network or terrain mutation shielding, thereby improving the continuity and integrity of the overall signal enhancement network.

[0091] In step S140, the second unmanned aerial cluster is dispatched to the dynamic blind area compensation position corresponding to the signal blind area to form a signal blind area compensation network. Each blind area compensation unmanned aerial vehicle in the second unmanned aerial cluster constitutes a network node of the signal blind area compensation network, and the dynamic blind area compensation position is determined based on the distribution of the enhanced unmanned aerial vehicles in each signal blind area.

[0092] In an example embodiment of the present disclosure, the second unmanned aerial cluster refers to a set of flight platforms specially used to cover and enhance the signal blind area that the first layer enhancement network cannot effectively cover. The unmanned aerial vehicles in the second unmanned aerial cluster are blind area compensation unmanned aerial vehicles. The blind area compensation unmanned aerial vehicle refers to a flight device with GNSS signal receiving, enhancement, and directional forwarding capabilities. The function structure can be similar to that of the enhanced unmanned aerial vehicle, but there are differences in deployment strategy, hovering height, path scheduling, and target task positioning. The blind area compensation unmanned aerial vehicle can be set as a multi-rotor flight platform with high-power directional transmission module and high-fault-tolerant flight control capability. Of course, when the terrain allows or the blind area span is large, a vertical take-off and landing fixed-wing unmanned aerial vehicle with stronger endurance can also be selected.

[0093] The dynamic blind area compensation position refers to the hovering deployment point of the blind area compensation unmanned aerial vehicle in the airspace. The dynamic blind area compensation position is a spatial coordinate specially calculated and generated to compensate for the signal blind area, and is not a static preset regular deployment point. The determination of the dynamic blind area compensation position can consider the current hovering residence position of the first layer enhanced unmanned aerial vehicle, the boundary profile of the signal blind area, the terrain shielding condition, the coverage radius of the blind area compensation unmanned aerial vehicle, and the target priority of the blind area compensation task, etc.

[0094] In an optional embodiment, the spatial distribution of the signal blind area can be constructed by analyzing the positional relationship between the signal blind area and the adjacent first layer of enhanced nodes, and based on the direction of insufficient coverage of the known enhanced network nodes, the main direction vector of the coverage gap is calculated; then, combined with the standard signal coverage radius of the blind-filling UAV and the preset blind-filling height (which is generally set to be about 10-30 meters higher than the hovering height of the corresponding enhanced network node), a plurality of candidate deployment points are projected along the main direction vector in the blind area space. The selection of the deployment point not only requires to be located in the visual propagation path range of the signal blind area in the three-dimensional space, but also needs to meet the line-of-sight unobstructed GNSS signal link from the blind-filling node to the target area, and if necessary, the obstruction angle between each candidate point and the ground receiving point can be calculated by using the digital terrain model to exclude.

[0095] In the process of determining the blind-filling deployment point, the inter-node distance constraint can also be introduced, that is, the blind-filling UAV coverage area boundary and the adjacent enhanced node are required to maintain a certain minimum distance to avoid frequency interference or signal overlap. In addition, if there is only a single enhanced node in the signal blind area, the blind-filling deployment point is preferably set in the direction of the enhanced node at the coverage range boundary; if the blind area is formed by the coverage gap between two or more enhanced nodes, the position of the blind-filling deployment point should be adjusted by using the offset weighting strategy combined with the signal-to-noise ratio data received by each node, so that the blind-filling UAV is closer to the direction of the node with weaker signal, and the blind-filling network topology with the most balanced spatial link structure is constructed.

[0096] After the blind-filling deployment point is determined, the blind-filling UAV in the second UAV cluster can be guided to fly to the target deployment point by flight control instructions, and hover at the preset blind-filling height. The network node set formed by all blind-filling UAVs constitutes a signal blind-filling network. As an upper layer or auxiliary layer structure of the signal enhancement network, the signal blind-filling network has the functions of preliminary enhancement, amplification and re-directional transmission of the received satellite signals. This layered network arrangement of signal enhancement can effectively solve the problem of airspace blind area caused by terrain barriers, complex reflections or sparse enhanced network structure, and improve the spatial integrity and stability of the entire GNSS signal enhancement system.

[0097] In step S150, based on the constructed signal enhancement network and signal blind-filling network, the received GNSS satellite signals are signal-enhanced and directionally transmitted to the GNSS signal receivers arranged at each slope deformation monitoring point in the dam slope area, so as to realize signal enhancement of the GNSS signal receivers.

[0098] In an example embodiment of the present disclosure, a signal enhancement network and a signal blind spot compensation network work together to form a multi-level, dynamically adaptive airborne GNSS signal enhancement system. The airborne GNSS signal enhancement system has flexible deployment, adjustable spatial links, and controllable signal paths, and can meet the high-quality signal transmission requirements in the complex obstruction environment of the dam slope area.

[0099] Signal enhancement involves processing the raw GNSS satellite signals received by augmentation and blind-spot drones to achieve higher signal strength, lower noise interference, more stable phase continuity, and waveform characteristics more suitable for ground-based GNSS receivers. This signal enhancement process can be achieved through low-noise amplification—using a low-noise amplifier (LNA) module to amplify weak incoming GNSS signals with low power and low distortion, thereby improving the signal-to-noise ratio (SNR) of the received signal. Furthermore, intermediate frequency (IF) or baseband filtering is used to remove non-GNSS frequency band interference or multipath components through bandpass filtering, ensuring the output signal has excellent spectral purity and time synchronization.

[0100] After signal enhancement, the enhanced GNSS signal is redirected and transmitted to the ground via a relay transmitter module installed on the drone. This module is a transmitter device that integrates a high-gain directional antenna, a power control unit, and a transmit signal modulation component. It is used to radiate the processed GNSS signal downward at a specific beam angle and transmit power, ensuring that the signal covers the designated target area. Directional transmission can achieve spatial alignment through antenna pitch adjustment, phased array beam steering, or mechanical servo actuators. Alternatively, a narrow-beam, high-gain directional antenna combined with an automatic correction algorithm can achieve directional stability even with slight platform position disturbances.

[0101] The signal receiving object is the GNSS signal receiver deployed in the dam slope area. The GNSS signal receiver refers to a terminal device with high-precision positioning capability and can receive GNSS signals from an aerial relay signal source. It is usually a dual-frequency or multi-frequency GNSS receiving module. Combined with the deformation monitoring point installation method and data transmission interface, it can realize the pseudo-range, carrier phase or differential observation analysis of the received enhanced signal to generate deformation trend monitoring results.

[0102] During actual signal transmission, the boosting and blind-spotting drones can dynamically adjust their transmission power and angle based on parameters such as the density of GNSS signal receivers, the signal path environment, and antenna beamwidth, thereby enhancing the signal for multiple GNSS receivers. Furthermore, to ensure link transmission stability, the signal boosting and blind-spotting networks can share node status information, signal quality parameters, and location information via communication links, building a multi-layered aerial transit network with adaptive topology reconstruction and link backup capabilities.

[0103] By constructing a signal enhancement network and a signal blind spot compensation network, GNSS signals can be stably received in areas of the dam slope that are previously severely obstructed or poorly covered. This allows ground-based GNSS receivers to continuously obtain high-quality positioning solutions even in complex terrain, ensuring the continuity, accuracy, and timeliness of slope deformation monitoring data. This process offers non-invasive deployment, high spatial resolution coverage, and dynamic aerial scheduling capabilities, making it suitable for monitoring dams, landslides, canyons, and other areas prone to GNSS signal attenuation.

[0104] The contents of steps S110 to S150 are described in detail below.

[0105] In an exemplary embodiment of the present disclosure, Figure 2 The steps in the above are used to determine multiple signal enhancement areas based on GNSS signal feature data and terrain feature data. Figure 2 Specifically, it may include:

[0106] Step S210, dividing the dam slope area according to the signal coverage parameters of the enhanced UAV to determine a plurality of monitoring sub-areas;

[0107] Step S220, determining the GNSS signal strength of each monitoring sub-area according to the GNSS signal characteristic data, and determining the terrain shielding degree of each monitoring sub-area according to the terrain characteristic data;

[0108] Step S230, determining a signal quality score for each monitoring sub-area based on the GNSS signal strength and the terrain shielding degree;

[0109] Step S240 : Mark the monitoring sub-areas whose signal quality scores are less than or equal to a preset score threshold as the signal to-be-enhanced areas, thereby obtaining a plurality of signal to-be-enhanced areas.

[0110] The signal coverage parameter for an enhanced drone refers to the effective signal enhancement coverage radius at a specific hovering altitude, determined by the drone's antenna pattern, transmit power, flight stability, and terrain visibility. This radius can be expressed as a three-dimensional spherical cap with the drone's hovering point as the center and the signal reachable distance corresponding to a certain power limit as the radius. This coverage area can be calibrated using actual flight test data or field strength simulation models, taking into account multiple factors such as GNSS signal frequency, signal enhancement module gain characteristics, antenna pitch angle range, and preset hovering altitude.

[0111] In a specific implementation, the dam slope region can be divided into sub-regions using mesh division, and the size of the spatial mesh can be equal to or slightly smaller than the signal coverage diameter of the enhanced unmanned aerial vehicle, so as to ensure that each sub-region after division can be effectively covered by a single unmanned aerial vehicle without dead angles. Optionally, if the dam slope region presents obvious slope changes or spatial topography discontinuity features, contour-driven terrain adaptive partitioning can be used to divide the target region at different elevation levels as the cutting reference, thereby improving the terrain adaptability on the basis of ensuring consistent signal coverage.

[0112] After completing the monitoring sub-region division, the GNSS signal strength and terrain shielding degree of each monitoring sub-region can be further evaluated. The GNSS signal strength refers to the stable intensity performance of the GNSS signal received by the sampling points in the sub-region within a unit time, for example, the signal-to-noise ratio (SNR) can be used as the main evaluation index, and the number of visible satellites, signal interruption times, average carrier-to-noise ratio, etc. can be used as reference parameters. The signal-to-noise ratio data can be provided by the historical ground receiving equipment or collected by the unmanned aerial vehicle during the initial detection flight in the non-enhanced state. Optionally, a GNSS signal quality map can also be introduced to perform three-dimensional interpolation and spatial fitting on the historical flight data to form a signal strength distribution map of the target region.

[0113] The terrain shielding degree refers to the shielding degree of the GNSS signal propagation path caused by factors such as mountains, buildings, or vegetation in the monitoring sub-region, which can be quantified by calculating the shielding angle or sky view factor (SVF) in each direction based on the three-dimensional terrain model. The shielding angle refers to the angle range of the cone-shaped space vertically upward from the center point of the sub-region to the zenith, and the larger the shielding angle, the more serious the shielding. The sky view factor is the ratio of the visible sky area of a unit space point to the total ideal sky area, and the lower the sky view factor, the stronger the shielding. The above parameters can be simulated and solved based on the digital elevation model combined with the ray projection algorithm, or obtained by real-time scanning of the laser radar carried by the unmanned aerial vehicle to obtain point clouds and perform airspace inversion, which is not specially limited in this example embodiment.

[0114] After obtaining the GNSS signal strength and terrain shielding degree of each monitoring sub-region, the GNSS signal strength and terrain shielding degree can be fused and calculated to obtain the signal quality score of each monitoring sub-region. The signal quality score is a comprehensive evaluation index for representing the positioning reliability and enhancement demand degree of the region under the current GNSS network, which can be calculated using a weighted linear model, an analytic hierarchy process model, or a support vector machine regression model, without being limited to this. For example, the signal quality score can be determined by the following relationship:

[0115] ;

[0116] in, It can represent the signal quality score of the i-th monitoring sub-area, It can represent the average signal-to-noise ratio of the i-th monitoring sub-area, It can represent the maximum signal-to-noise ratio in the i-th monitoring sub-area, It can represent the average occlusion angle of the i-th monitoring sub-area, It can represent the maximum occlusion angle in the i-th monitoring sub-area, and It can represent an adjustable weight parameter, which respectively expresses the attention paid to signal strength and terrain influence.

[0117] After the signal quality score calculation is complete, the scores for all monitoring sub-areas can be compared with a preset scoring threshold. Monitoring sub-areas with signal quality scores below or equal to the preset scoring threshold are identified as areas with poor signal quality and requiring priority deployment of booster drones for signal enhancement. The scoring threshold can be dynamically set based on the application scenario. For example, in the main control area of ​​a dam slope, where safety monitoring requirements are high, a higher scoring standard can be used to ensure that there are no blind spots in critical areas. This example embodiment does not impose any specific restrictions on the setting of the scoring threshold.

[0118] By determining the signal quality score through GNSS signal feature data and terrain feature data, multiple signal enhancement areas can be identified, and a quantitative analysis of the signal environment under the complex spatial structure of the dam slope can be achieved. This can effectively reduce the number of drones required for signal relay enhancement and reduce operating costs.

[0119] In an exemplary embodiment of the present disclosure, the following steps may be performed to divide the dam slope area according to the enhanced drone signal coverage parameters to determine multiple monitoring sub-areas, which may specifically include:

[0120] The signal enhancement coverage radius of the enhanced drone can be determined, and the signal coverage area on the dam slope area can be determined based on the preset hovering height and signal enhancement coverage radius; multiple weak signal distribution aggregation points are determined in the dam slope area based on the stored historical signal quality distribution data, where each weak signal distribution aggregation point corresponds to a GNSS signal receiver; with the goal of covering all weak signal distribution aggregation points with the minimum signal coverage area, the target signal coverage areas at multiple different locations are determined, and the target signal coverage areas are used as monitoring sub-areas.

[0121] Among them, the signal enhancement coverage radius refers to the maximum horizontal projection distance that the enhanced drone can effectively cover the ground receiving equipment at a set hovering height, based on the transmission power, antenna directivity and channel model of its GNSS signal relay device. The signal enhancement coverage radius can be dynamically set by different enhanced drone flight platform parameters. For example, when the antenna is a high-gain directional type, the coverage area presents a narrow-angle cone structure, corresponding to a smaller but higher-intensity projection radius at a certain height; when the antenna is a wide-angle low-gain type, the coverage radius can be appropriately enlarged but the signal strength edge attenuates faster. The signal enhancement coverage radius at least meets the requirement that after the GNSS signal reaches the ground receiver on the enhanced transmission path, its signal-to-noise ratio is greater than the receiver's minimum recognizable threshold and the link bit error rate remains within a stable range.

[0122] After obtaining the enhanced signal coverage radius of the UAV, its spatial range of action on the dam slope area can be geometrically calculated in combination with the preset hovering height, thereby determining the signal coverage area on the terrain surface. This signal coverage area can be abstracted as a cone with the UAV's hovering point as the vertex and the downward direction as the axis. The intersection of these cones on the terrain projection surface is the effective signal coverage boundary. Due to the dam slope area's undulating terrain characteristics such as slope changes, this cone can be projected and intersected with the digital elevation model in actual calculations to form an irregular polygonal coverage area. The boundary is then buffered and expanded based on the principle of spatial proximity to improve continuous coverage capabilities.

[0123] The layout of multiple monitoring sub-areas in the entire dam slope area can be optimized. For example, all weak signal distribution clusters can be collected and driven as key observation locations where the signal is to be enhanced. Weak signal distribution clusters refer to a set of spatial points in the dam slope area where the GNSS signal has long-term low signal-to-noise ratio, insufficient number of visible satellites, or frequent signal interruptions. This point set can be based on historical positioning error heat map analysis, abnormal event monitoring records, or reverse extraction from drone cruise data. Each weak signal distribution cluster should have a unique spatial coordinate identifier and be associated with at least one GNSS observation record sequence.

[0124] In this embodiment, the goal can be to cover all weak signal distribution clusters with the least signal coverage area, and a multi-circle site selection model based on coverage optimization can be used to implement the deployment strategy. For example, a minimum circle coverage algorithm, a genetic algorithm, or a discrete grid coverage approximation method can be used to iteratively generate multiple candidate signal coverage area sets in the search space, and ultimately select a combination of monitoring sub-areas that completely includes the weak signal cluster points and uses the least number of coverage areas as the deployment result. In order to ensure that the deployment structure is consistent with the actual terrain conditions, the terrain adaptability can be judged after the coverage points are generated. For example, check whether there is enough airspace above the target hovering point of the enhanced drone and whether the minimum overhead occlusion requirements are met, and make fine adjustments to the deployment points if necessary.

[0125] The target signal coverage area finally determined serves as the monitoring sub-area, which not only meets the task requirements of covering weak signal aggregation points, but also has the characteristics of adjustable deployment redundancy, minimization of the number of nodes and maximization of enhanced coverage efficiency, making it easier to execute precise enhanced drone scheduling and signal enhancement network construction strategies in subsequent steps.

[0126] Optionally, when determining target signal coverage areas at multiple different locations and using the target signal coverage areas as monitoring sub-areas, the target signal coverage areas whose center points are spaced apart by a distance less than or equal to a preset distance can be fused and merged to obtain multiple monitoring sub-areas.

[0127] The center point refers to the geometric center of each signal coverage area or the spatially projected coordinates of the actual augmented drone deployment point, typically expressed in a geographic coordinate system (e.g., WGS84) or a projected coordinate system (e.g., UTM). To determine whether two target coverage areas should be merged, the Euclidean distance between their corresponding center points can be calculated and compared with a preset separation distance. The preset separation distance refers to the minimum recognizable spacing threshold set by the system based on the drone coverage radius, signal augmentation overlap tolerance, and deployment cost. Its value is typically slightly smaller than the signal interference boundary caused by the simultaneous deployment of two drones. For example, it can be set to 80% to 100% of the signal coverage radius. Of course, the specific setting can be customized based on actual application conditions, and this embodiment is not limited to this.

[0128] When the distance between the center points of any two target signal coverage areas is less than or equal to the preset interval distance, it can be considered that there is a possibility of spatial overlap or redundant coverage between the two target signal coverage areas. At this time, in order to reduce the number of drones deployed, avoid signal superposition interference and improve network coordination, the fusion merging mechanism can be triggered. Fusion merging refers to merging two or more target signal coverage areas into a monitoring sub-area, and expanding its boundaries to a joint contour that can completely cover all the original target points. In order to achieve efficient fusion, the geometric union algorithm or the convex hull generation algorithm can be used to process the boundary coordinates of multiple overlapping areas, and the center point of the new monitoring sub-area can be relocated with the goal of maximizing the joint coverage.

[0129] The fusion and merging process can also take into account both coverage integrity and deployment flexibility. In some slope areas, the terrain changes dramatically or the GNSS signal environment is complex, which may cause some weak signal aggregation points to be located at the boundary of two or more signal coverage areas. At this time, in order to ensure that all target points are within the effective range of the enhanced signal, the system should appropriately expand the outer boundary of the new sub-area when performing the merging operation, and preferably introduce a boundary buffer zone. The buffer width can be set to 10% to 20% of the coverage radius of the enhanced drone to ensure that the receiving equipment at the boundary can also obtain continuous and stable enhanced signals. At the same time, in order to avoid waste of resources caused by boundary expansion, the buffer strategy can be adjusted in combination with task priority or channel interference estimation.

[0130] By fusing and merging overlapping monitoring sub-areas, the number of redundant nodes can be effectively reduced, the interference problems caused by overlapping signal enhancement areas can be minimized, and the maintainability and scalability of the signal relay network structure can be improved. It is suitable for achieving high-density GNSS signal enhancement deployment in complex slope environments with limited spatial resources or large differences in mission levels.

[0131] In an exemplary embodiment of the present disclosure, Figure 3 The steps in the above are used to dispatch the first drone cluster to hover at the preset hovering height of each signal enhancement area to form a signal enhancement network. Figure 3 Specifically, it may include:

[0132] Step S310: Based on the preset hovering height, the space above the area to be enhanced is divided into spatial grids, and the signal transmission stability of the enhanced drone at each spatial grid is determined based on the drone parameters of the enhanced drone.

[0133] Step S320, selecting at least two hovering points according to the signal transmission stability;

[0134] Step S330, adjusting the hovering residence point of each enhanced unmanned aerial vehicle in the corresponding signal-to-be-enhanced area according to the received GNSS signal quality fed back by each enhanced unmanned aerial vehicle in the first unmanned aerial vehicle cluster, so as to ensure the quality of the enhanced GNSS satellite signal received by all GNSS signal receivers within the signal enhancement network coverage.

[0135] Among them, a three-dimensional space grid can be constructed in the space above the signal-to-be-enhanced area to assist in determining the adaptive unmanned aerial vehicle hovering residence point. The three-dimensional space grid refers to a set of spatial coordinates formed above the space of the signal-to-be-enhanced area according to the set height level and horizontal division rule, and each grid unit represents a potential hovering candidate point. The grid division can be generated based on a regular cubic grid, an equidistant spherical grid, or a terrain contour driven grid. In addition, a grid layout surface can be generated at a preset hovering height with a spacing of 5% to 10% coverage redundancy, so as to ensure that the signal enhancement areas between the deployed unmanned aerial vehicle nodes minimize overlapping interference while maintaining coverage continuity.

[0136] On the basis of the three-dimensional space grid, the signal transmission stability of each space grid unit can be evaluated in combination with the flight parameters, attitude control capability, antenna beam width, antenna directivity, and GNSS signal enhancement module transmission power curve of the enhanced unmanned aerial vehicle. The signal transmission stability refers to the power stability, noise suppression capability, and multipath interference tolerance of the GNSS signal from space to ground in this link path when the enhanced unmanned aerial vehicle performs a relay task at a specific location. The evaluation can be calculated based on a combination of the free space path loss model (FSPL) and the terrain obstruction correction model, while considering the trade-off relationship between the main lobe directivity attenuation and the edge side lobe noise enhancement of the enhanced signal at different azimuth angles.

[0137] Analog link propagation simulation can be introduced to predict the path loss distribution of the signal from the unmanned aerial vehicle to the ground target area according to the DEM height data of each grid point, and to generate a signal transmission stability for each grid point. This is a common technical means for those skilled in the art, and will not be repeated here. The higher the signal transmission stability, the more suitable the point is as a hovering residence point for performing enhancement tasks. Of course, a channel gain-disturbance prediction function can also be constructed by combining parameters such as unmanned aerial vehicle task load, environmental wind field, and ground reflection interference coefficient, to further improve the discrimination accuracy of different deployment points.

[0138] After obtaining the signal transmission stability, at least two grid points with high stability scores can be selected as the final hovering points in the current signal enhancement area. Of course, in some optional embodiments, the selection process can also consider the mutual influence between all signal enhancement areas. For example, a greedy optimization algorithm or a multi-objective function scheduling strategy can also be used to preferentially select a combination of points that meet the minimum deployment interval, minimize channel interference, and optimize redundant coverage as the deployment output. If multiple signal enhancement areas are adjacent to each other, local regulation logic can be introduced during deployment to form a continuous signal link across regions, which is beneficial for the integration of subsequent blind network and enhanced network.

[0139] After completing the preliminary deployment, to ensure the stability and timeliness of the network, GNSS signal quality data feedback from each enhanced unmanned vehicle in the first unmanned vehicle cluster can be continuously received. The signal quality data can include real-time signal-to-noise ratio, satellite geometry distribution, carrier phase continuity, and relay signal modulation degree. To improve system response speed and avoid unnecessary frequent position adjustment, a dynamic update period and trigger condition can be set. If the signal-to-noise ratio continues to decrease, the GDOP continues to rise, or the multipath interference continues to rise for several consecutive periods, the position adjustment instruction can be triggered to switch the hovering point.

[0140] In the adjustment strategy, the local grid relocation method is used, that is, a hovering point with better signal stability is selected from the set of pre-set feasible hovering points near the current hovering point to switch, ensuring that the network connectivity is maintained while the enhancement coverage capability is improved. If necessary, the self-organizing network reconstruction mechanism can also be used to guide the coordinated movement of adjacent nodes to fill the gaps or optimize the link structure, so that the GNSS signal receivers within the entire signal enhancement network coverage area are always within the high-quality enhanced signal action area.

[0141] Through the above deployment mechanism, the spatial deployment accuracy can be improved while the adaptability of the network to terrain changes, environmental disturbances, or task changes is enhanced, providing a high-robustness and high-controllability air signal enhancement support for high-precision GNSS monitoring systems in complex slope areas.

[0142] In an example embodiment of the present disclosure, the second unmanned vehicle cluster can be dispatched to the dynamic blind-filling position corresponding to the signal blind area by the following steps to form a signal blind-filling network. Specifically, it can include:

[0143] The current hovering residence position of the enhanced unmanned aerial vehicle in the first unmanned aerial vehicle cluster corresponding to each signal blind area can be determined, and the signal enhancement coverage radius of the blind-filling unmanned aerial vehicle and the preset blind-filling height are combined to determine the blind-filling deployment point; each blind-filling unmanned aerial vehicle in the second unmanned aerial vehicle cluster is dispatched to fly to the blind-filling deployment point and hover at a height higher than the preset blind-filling height of the corresponding enhanced unmanned aerial vehicle to construct a signal blind-filling network.

[0144] In order to ensure that the position of the blind-filling unmanned aerial vehicle can achieve full coverage of the blind area, the signal enhancement coverage radius of the blind-filling unmanned aerial vehicle, the hovering flight height thereof, and the positional relationship of the adjacent enhanced unmanned aerial vehicle are comprehensively considered to determine the blind-filling deployment point. The signal enhancement coverage radius of the blind-filling unmanned aerial vehicle refers to the stable signal enhancement range formed by the blind-filling unmanned aerial vehicle under the condition of a set transmission power, an antenna directivity parameter, and a specific hovering height. The radius can be obtained by joint simulation calculation of a channel propagation model, or typical parameters can be obtained by field measurement, and the present embodiment is not limited thereto. The size of the coverage radius determines the number of enhanced unmanned aerial vehicles assisted by a single blind-filling unmanned aerial vehicle for signal enhancement and the residence range, and the optimization goal is to cover as many enhanced unmanned aerial vehicles in as many signal blind areas as possible with as few blind-filling nodes as possible.

[0145] In the process of determining the blind-filling deployment point, a spatial candidate deployment point set can be first constructed. The deployment point set is a three-dimensional equidistant lattice set generated by taking the center point of the signal blind area as the origin and according to the coverage radius of the blind-filling unmanned aerial vehicle. Each point is a possible blind-filling deployment point of the blind-filling unmanned aerial vehicle. To further ensure the effectiveness of the deployment, the candidate blind-filling deployment point set can be subjected to visibility analysis to evaluate whether there is an occlusion path to the boundary of the target blind area, and the interconnection possibility and signal redundancy degree with the surrounding enhanced unmanned aerial vehicles are analyzed, and if necessary, the nodes are removed and screened in combination with the communication link topology requirements.

[0146] The height of the blind-filling deployment point can be generally higher than the preset hovering height of the corresponding enhanced unmanned aerial vehicle. For example, the height of the blind-filling deployment point can be set to an interval of 10 meters to 30 meters above the hovering height of the enhanced node, which can depend on the topographic features of the current task area, airspace control requirements, and signal propagation attenuation characteristics. By increasing the hovering height of the blind-filling node, a larger signal propagation angle and a clearer sky view can be obtained, thereby improving the effective range of the ground GNSS receiver and reducing the risk of link failure due to terrain occlusion or edge interference.

[0147] After the above blind-filling deployment point determination is completed, a suitable blind-filling UAV can be called from the second UAV cluster according to the task priority, flight resource scheduling table and power state, and flight path planning and target instruction issuing can be performed. The flight path planning can use a high-precision flight trajectory planning strategy based on a three-dimensional A-star algorithm or a Bezier curve fitting to ensure that the blind-filling UAV safely avoids obstacles and efficiently reaches the target blind-filling deployment point in a complex airspace environment. This is a common technical means in the art, and will not be described here.

[0148] After the blind-filling UAV reaches the deployment point, it can be stably hovered at a preset blind-filling height and start the GNSS signal receiving, enhancing and forwarding device to form a new air relay node, thereby filling the originally existing signal blind area. All deployed blind-filling nodes collectively constitute a signal blind-filling network, which serves as an upper space signal supplement layer of the first enhancement network, not only achieving dynamic plugging of the blind area range, but also having the ability to perform redundant compensation and structure reconstruction when a node fails, thereby greatly improving the spatial adaptability, system robustness and monitoring data continuity of the overall GNSS signal enhancement system in complex terrain of a dam slope.

[0149] In an example embodiment of the present disclosure, when there is only one enhancement UAV in the signal coverage space range constituted by the signal enhancement coverage radius of the blind-filling UAV, the blind-filling deployment point can be determined by the following steps, which can specifically include:

[0150] A plurality of candidate deployment points can be determined such that the current hovering residence position of the enhancement UAV corresponding to the signal blind area is within the signal enhancement coverage radius of the blind-filling UAV; at least one blind-filling deployment point is selected from the plurality of candidate deployment points under the condition of a preset blind-filling height.

[0151] The current hovering residence position refers to the stable hovering residence position of the enhancement UAV during the execution of the signal relay task, which is usually a fusion positioning result in three-dimensional space mainly based on real-time GNSS data and supplemented by inertial navigation system output. This position not only determines the central axis of the signal enhancement, but also constitutes a spatial reference point for blind-filling path planning.

[0152] The candidate deployment point refers to a set of spatial points in three-dimensional space that satisfy certain geometric constraints and propagation conditions. The geometric constraints mainly include that the spatial distance between the candidate point and the target enhancement UAV should be less than or equal to the signal enhancement coverage radius of the blind-filling UAV, and the residence point of the enhancement UAV should be at the edge or effective coverage boundary area of the blind-filling UAV signal action area to avoid central redundancy and inability to expand the blind area coverage range.

[0153] Specifically, the residence point of the enhancement unmanned aerial vehicle can be taken as the center, a plurality of uniformly distributed candidate points can be generated in the upper space by spherical geometry modeling in combination with the preset signal enhancement coverage radius and the vertical blind-filling height, and a candidate deployment point spherical shell is formed. In the calculation process, the propagation characteristics and interference tolerance of the GNSS signal can be considered, for example, by setting standards such as a lower limit of the emission elevation angle, a terrain shielding tolerance range, and an upper limit of the path loss, points that do not meet the propagation conditions can be removed from all candidate points, thereby improving the effectiveness and channel stability of the deployment.

[0154] To further ensure that the blind-filling deployment point does not interfere with the existing link and can maximize the signal quality of the target blind area, a link gain evaluation function can also be introduced. The link gain evaluation function takes the channel gain prediction value from the blind-filling candidate point to the center of the blind area, the propagation path shielding risk, and the enhancement link intersection degree as the main input indicators, and assigns a weight score to each candidate point. The highest score in the candidate point set is selected as the final blind-filling deployment point. Of course, an improved particle swarm algorithm or a simulated annealing strategy can also be used for global search to improve the search efficiency and deployment accuracy. This example embodiment does not make special limitations.

[0155] The preset blind-filling height refers to that compared with the current residence height of the enhancement unmanned aerial vehicle, the blind-filling unmanned aerial vehicle should perform hovering operation within a specific height range above it. The height difference can generally be set to 10 to 30 meters. The purpose of setting the preset blind-filling height is to expand the downward signal cone angle, improve the visible coverage range of each enhancement unmanned aerial vehicle in the signal blind area, and avoid same-frequency resonance or reverse reflection link disturbance with the enhancement node signal interference path. The preset blind-filling height can be dynamically adjusted according to the terrain fluctuation amplitude, the target coverage radius, and the airspace restriction, or can be self-defined by combining a simulation modeling optimization algorithm. This example embodiment does not make specific limitations on the preset blind-filling height.

[0156] The blind-filling deployment point determined by the above deployment logic has the characteristics of clear structure, small interference, and high gain, which can enable the deployed blind-filling unmanned aerial vehicle to realize directional enhancement compensation for the signal blind area when only facing a single enhancement node, ensure that the ground GNSS receiving equipment in the area covered by the blind area receives an enhanced signal that meets the positioning calculation requirements, and thus guarantee the monitoring continuity and signal integrity of the key parts of the dam slope area.

[0157] In an example embodiment of the present disclosure, when there are two or more enhancement unmanned aerial vehicles in the signal coverage space range constituted by the signal enhancement coverage radius of the blind-filling unmanned aerial vehicle, the blind-filling deployment point can be determined by the steps in Figure 4 , as shown in Figure 4 , which can specifically include:

[0158] Step S410, determining a deviation ratio of each of the enhanced unmanned aerial vehicles according to a signal-to-noise ratio of GNSS satellite signals received by the enhanced unmanned aerial vehicle, wherein the smaller the signal-to-noise ratio of the enhanced unmanned aerial vehicle, the greater the deviation ratio, and the closer the blind-filling deployment point to the enhanced unmanned aerial vehicle;

[0159] Step S420, determining a plurality of candidate deployment points for each of the enhanced unmanned aerial vehicles to be in a signal-enhanced coverage radius of the blind-filling unmanned aerial vehicle;

[0160] Step S430, screening at least one blind-filling deployment point from the plurality of candidate deployment points with the deviation ratio and the preset blind-filling height as limiting conditions.

[0161] The deviation ratio refers to a relative proportional factor of a spatial deviation degree of a geometric center of the blind-filling unmanned aerial vehicle deployment point to each of the enhanced unmanned aerial vehicles. The deviation ratio can be calculated by using a normalized inverse proportional function or a fuzzy weighting strategy. For example, the deviation ratio can be determined by the following relationship:

[0162] ;

[0163] Wherein, The deviation ratio of the xth enhanced unmanned aerial vehicle can be represented as The signal-to-noise ratio measured by the xth enhanced unmanned aerial vehicle can be represented as The signal-to-noise ratio measured by the yth enhanced unmanned aerial vehicle can be represented as The total number of enhanced unmanned aerial vehicles in the signal coverage space can be represented as

[0164] After obtaining the deviation ratio, a plurality of candidate blind-filling deployment points can be determined in the candidate deployment area, each of which satisfies that the signal-enhanced coverage radius can cover the hovering residence positions of all target enhanced unmanned aerial vehicles and has good air propagation path visibility and link interference tolerance.

[0165] The optimal blind-filling deployment point can be screened from the candidate deployment point set with the deviation ratio and the preset blind-filling height as joint limiting conditions. The preset blind-filling height refers to a minimum vertical height condition required by the blind-filling unmanned aerial vehicle when covering a plurality of enhanced nodes, which is usually 10-30 meters higher than any target enhanced unmanned aerial vehicle, to ensure that its signal path can enhance the signal coverage of multiple enhanced network nodes from a higher angle, avoiding terrain obstruction, mutual interference and path interference overlap.

[0166] After selecting the blind spot deployment points, the blind spot drones in the second drone cluster can be dispatched to fly to their corresponding blind spot deployment points along the optimal path and hover at the designated blind spot altitude to perform signal reception, enhancement, and directional forwarding tasks. This deployment method not only enables aerial blind spot repair between multiple enhancement drones, but also enhances the dynamic compensation capability for signal fluctuation areas under complex link structures through an offset scheduling strategy guided by signal strength differences, improving the robustness and spatial redundancy control level of the overall GNSS signal enhancement network in highly complex slope areas.

[0167] In an exemplary embodiment of the present disclosure, the UAV flight platform structure of the UAV for enhancing or blind spot filling can be the same, and can include: a flight control module for controlling the UAV to fly to the deployment location corresponding to the signal-to-be-enhanced area or the signal blind spot, and to stably hover at a set hovering altitude; a GNSS signal receiving module for receiving GNSS satellite signals from multiple GNSS satellite systems; a signal enhancement and relay module electrically connected to the GNSS signal receiving module for amplifying and filtering the received GNSS satellite signals and directionally transmitting the enhanced GNSS satellite signals to a GNSS signal receiver on the ground via a relay transmitting antenna; a signal quality sensing module for real-time detection of the signal-to-noise ratio, number of visible satellites, or multipath interference characteristics of the GNSS signals received by the UAV, and sending signal quality data to a ground control center; a communication module for communication and interaction between the UAV and other UAV nodes and with the ground control system to achieve collaborative construction and topology optimization of the signal enhancement network or signal blind spot filling network; and an energy management module for managing the UAV's power supply system and, when the power supply falls below a preset power threshold, sending a replacement request to the ground control center or activating a backup UAV node to fill the gap.

[0168] The flight control module is the core control unit for performing track navigation, fixed-point hovering, and attitude stabilization. Its main function is to control the entire process of the UAV from takeoff to hovering based on flight mission instructions issued by the ground control center, combined with the autonomous navigation system and flight status feedback. The flight control module can be composed of a flight control main control chip, an attitude solver unit, a navigation fusion unit, a GPS receiver module, a gyroscope, an accelerometer, a barometer, and a magnetometer.

[0169] The GNSS signal receiving module refers to a high-sensitivity radio frequency receiving unit configured on the flight platform for receiving multi-source GNSS satellite signals. The GNSS signal receiving module supports at least multi-system compatible reception of L1, L2 or B1, B2 and other frequency bands, including but not limited to GPS, GLONASS, Galileo and Beidou systems. The GNSS signal receiving module can include an antenna receiving unit, a radio frequency front end, a power manager, a low noise amplifier (LNA) and a baseband processor. In implementation, to enhance the robustness of signal reception, the GNSS signal receiving module can be a high-performance GNSS module with multi-frequency concurrent reception, anti-interference processing and dynamic tracking capability. An RTK solving engine can be optionally configured to improve the positioning accuracy and signal acquisition capability in high-shielding scenarios.

[0170] The signal enhancement and relay module refers to a special signal processing unit deployed on the unmanned aerial vehicle, which has the ability of GNSS signal enhancement, filtering, power amplification and directional retransmission. The module includes a receiving channel controller, a programmable gain amplifier (PGA), a band-pass filter, a transmission modulator, a power amplifier and a directional transmission antenna system. In the running process, the signal enhancement and relay module performs low-noise amplification and intermediate frequency filtering processing on the received GNSS signals, removes background interference and spurious frequency components, and then can introduce the signals into the transmission path, control the beam through high-gain directional antennas, and realize directional compensation projection to the below enhanced unmanned aerial vehicle or GNSS signal receiver.

[0171] The signal quality sensing module refers to a real-time signal monitoring device deployed on the flight platform, which is used to detect the strength, integrity and noise level of the current received GNSS signal. The signal quality sensing module can be composed of a spectrum collector, an analog-to-digital converter, a digital signal processor and a parameter evaluation unit, which can extract key indicators such as SNR, carrier-to-noise ratio, number of visible satellites, channel utilization, multi-path interference characteristics, etc. In implementation, the signal quality sensing module can periodically send detection results to the ground control center as a basis for subsequent blind compensation judgment, path adjustment and network topology optimization decisions. To adapt to the change of side slope shielding environment, the signal quality sensing module can also support high-frequency refresh and dynamic window detection strategy to identify short-time shielding events and path shielding trends.

[0172] The communication module refers to the communication subsystem used to implement information exchange between the flight platform and the ground control system, as well as between the flight platform and other UAV nodes. The communication module typically includes a wireless communication interface, a data link manager, an encrypted transmission unit, and a channel coordinator. The communication module can also support multiple communication protocols, including Wi-Fi Mesh, LoRa, ZigBee, LTE-M, or millimeter wave relay links, and can be flexibly configured according to the mission distance and data volume requirements. The inter-node communication function supports the construction of a mesh self-organizing network structure between the enhanced UAV and the blind spot UAV, facilitating the dynamic coordination of the signal enhancement link and the automatic replacement of faulty nodes. At the same time, the communication module can also undertake the task of transmitting node location, energy consumption status, and signal quality indicators to the ground dispatch center in real time to ensure the global controllability and dispatch response efficiency of the signal enhancement network.

[0173] The energy management module refers to a power scheduling and monitoring system integrated into the flight platform, used to monitor the drone's overall energy supply in real time and adjust load distribution. The energy management module may include a power detector, an energy consumption calculation unit, a power switching controller, and an energy utilization optimization module. During operation, the energy management module monitors the power supply of the main battery in the power supply system in real time and, combining flight time estimates with the current mission intensity, predicts the remaining operational time. When the power supply is detected to be below a preset threshold (e.g., 20%), the energy management module can proactively send a low-battery alarm signal to the ground control system, triggering the takeoff of a backup drone or guiding the current node back to a recovery point. Furthermore, the energy management module can be equipped with a solar panel auxiliary charging unit or a wireless energy receiving device to extend the drone's airborne time and enhance the sustainability and autonomy of the signal enhancement system during long-term monitoring missions.

[0174] By integrating the above modules into enhanced drones or blind spot-filling drones, this type of aircraft not only has the ability to fly independently and hover stably, but can also complete GNSS signal enhancement, network reconstruction and channel self-recovery tasks in complex obstruction environments, thereby significantly improving the availability, stability and spatial distribution balance of GNSS signals in the dam slope area, and ensuring the high-precision, full-coverage positioning requirements of the GNSS monitoring terminals below.

[0175] Figure 5 A schematic diagram of an application scenario of GNSS signal enhancement based on drone signal relay in an embodiment of the present disclosure is shown.

[0176] refer to Figure 5As shown, in the dam slope area 501, the slope deformation in the dam slope area 501 is monitored by setting a plurality of GNSS signal receivers 502, so as to realize the geological disaster monitoring of the dam slope area 501. The GNSS signal receiver 502 can receive the GNSS satellite signal emitted by the GNSS satellite system 503 through a dual-frequency or multi-frequency GNSS receiving module, and analyze the pseudorange, carrier phase or differential observation of the received GNSS satellite signal, so as to generate a deformation trend monitoring result.

[0177] However, due to the complex topographic environment of the dam slope area 501, especially in the canyon, steep slope or serious shielding area, the GNSS satellite signal attenuation problem is more serious, and even the signal blind area appears, which affects the accuracy and continuity of the positioning data acquisition. Through the GNSS signal enhancement method based on unmanned aerial vehicle signal relay in the embodiment of the present disclosure, a plurality of enhanced unmanned aerial vehicles 504 in the first unmanned aerial vehicle cluster can be deployed to form a signal enhancement network 505, so as to enhance the GNSS satellite signal emitted by the GNSS satellite system 503 through the signal enhancement network 505, and then relay the enhanced GNSS satellite signal to the GNSS signal receiver 502, thereby effectively improving the quality of the GNSS satellite signal received by the GNSS signal receiver 502, and ensuring the accuracy and stability of the deformation trend monitoring result.

[0178] For the enhanced unmanned aerial vehicle 504 in the signal enhancement network 505, due to the different positions in the dam slope area 501, part of the enhanced unmanned aerial vehicle 504 still receives very weak GNSS satellite signals, which cannot complete the enhancement and relay of the GNSS satellite signal, or the signal quality of the enhanced GNSS satellite signal cannot meet the requirements. At this time, a plurality of blind-filling unmanned aerial vehicles 506 in the second unmanned aerial vehicle cluster can be deployed to form a signal blind-filling network 507, so as to realize the preliminary enhancement of the GNSS satellite signal at a higher space, and transmit the preliminary enhanced GNSS satellite signal to part of the enhanced unmanned aerial vehicles 504 in the signal enhancement network 505 which are in the signal blind area for further enhancement, and then transmit to the GNSS signal receiver 502. Through the multi-layer signal enhancement and relay network, the availability, stability and spatial distribution balance of the GNSS satellite signal can be improved in the complex topographic environment of the dam slope area 501, and the high-precision and full-coverage positioning requirements of the GNSS signal receiver 502 below can be guaranteed.

[0179] Figure 6 Another application scenario diagram of the GNSS signal enhancement based on unmanned aerial vehicle signal relay in the embodiment of the present disclosure is shown.

[0180] Reference Figure 6As shown, in the embodiment of the present disclosure, a two-layer signal enhancement and relay network is proposed, but those skilled in the art can understand that in some application scenarios, the enhanced drones 504 in the signal enhancement network 505 can directly complete the signal enhancement relay task, and do not need to be supplemented by a signal blindness network formed by multiple blindness-filling drones in the second drone cluster. The signal enhancement relay task can be completed only by forming a signal enhancement network 505 by multiple enhanced drones 504 in the first drone cluster. This is also within the protection scope of the embodiment of the present disclosure.

[0181] In the embodiment of the present disclosure, a GNSS signal enhancement device based on drone signal relay is also provided. Figure 7 As shown, the GNSS signal enhancement device 700 based on drone signal relay may include an area determination module 710, a signal enhancement network construction module 720, a signal blind area screening module 730, a signal blind area network construction module 740 and a signal enhancement module 750, wherein:

[0182] The region determination module 710 is configured to obtain pre-collected GNSS signal characteristic data and terrain characteristic data corresponding to the dam slope region, and determine a plurality of signal enhancement regions based on the GNSS signal characteristic data and the terrain characteristic data;

[0183] The signal enhancement network construction module 720 is configured to dispatch the first drone cluster to hover at a preset hovering height in each of the signal enhancement areas to form a signal enhancement network, wherein each enhanced drone in the first drone cluster constitutes a network node of the signal enhancement network;

[0184] a signal blind spot screening module 730 for determining the signal-to-noise ratio of the GNSS satellite signals received by each network node in the signal enhancement network; and determining the signal-to-noise ratio of the target network node to be enhanced as a signal blind spot if the signal-to-noise ratio of the target network node is less than or equal to a preset signal-to-noise ratio threshold.

[0185] A signal blind spot network construction module 740 is configured to dispatch a second UAV cluster to a dynamic blind spot location corresponding to the signal blind spot to form a signal blind spot network. Each blind spot filling UAV in the second UAV cluster constitutes a network node of the signal blind spot network. The dynamic blind spot filling location is determined based on the real-time updated distribution of the enhanced UAVs in the signal blind spot.

[0186] The signal enhancement module 750 is used to enhance the received GNSS satellite signal based on the constructed signal enhancement network and the signal blind spot network, and transmit it directionally to the GNSS signal receivers installed at each slope deformation monitoring point in the dam slope area to achieve signal enhancement for the GNSS signal receiver.

[0187] In an example embodiment of the present disclosure, the region determination module 710 is configured to:

[0188] perform region division on the dam slope region according to the signal coverage parameter of the signal enhancement UAV, to determine a plurality of monitoring sub-regions;

[0189] determine, according to the GNSS signal feature data, GNSS signal strength of each monitoring sub-region, and determine, according to the terrain feature data, a terrain shielding degree of each monitoring sub-region;

[0190] determine, by the GNSS signal strength and the terrain shielding degree, a signal quality score of each monitoring sub-region;

[0191] label, as the signal to be enhanced region, each monitoring sub-region with a signal quality score less than or equal to a preset score threshold, to obtain a plurality of signal to be enhanced regions.

[0192] In an example embodiment of the present disclosure, the region determination module 710 is configured to:

[0193] determine a signal enhancement coverage radius of the signal enhancement UAV, and determine, according to the preset hovering height and the signal enhancement coverage radius, a signal coverage region on the dam slope region;

[0194] determine, based on stored historical signal quality distribution data, a plurality of weak signal distribution aggregation points on the dam slope region, wherein each weak signal distribution aggregation point corresponds to a GNSS signal receiver;

[0195] determine, as the monitoring sub-region, a target signal coverage region in a plurality of different positions, with the goal of covering all the weak signal distribution aggregation points with the least signal coverage region.

[0196] In an example embodiment of the present disclosure, the region determination module 710 is configured to:

[0197] merge target signal coverage regions with a spacing distance between center points less than or equal to a preset spacing distance, to obtain a plurality of monitoring sub-regions.

[0198] In an example embodiment of the present disclosure, the signal enhancement network construction module 720 is configured to:

[0199] perform spatial grid division at a space above the signal to be enhanced region, with the preset hovering height as a reference, and determine, according to UAV parameters of the signal enhancement UAV, a signal transmission stability degree of the signal enhancement UAV at each spatial grid;

[0200] Select at least two hovering points according to the signal transmission stability;

[0201] In real time, based on the received GNSS signal quality feedback from each enhanced UAV in the first UAV cluster, the hovering point of each enhanced UAV in the corresponding signal to be enhanced area is adjusted to ensure the quality of the enhanced GNSS satellite signal received by all GNSS signal receivers within the coverage range of the signal enhancement network.

[0202] In an exemplary embodiment of the present disclosure, the signal blind spot network construction module 740 is configured to:

[0203] Determine the blind spot deployment point based on the current hovering position of the enhanced drone in the first drone cluster corresponding to each of the signal blind spots, and in combination with the signal enhancement coverage radius of the blind spot filling drone and the preset blind spot filling height;

[0204] Each blind spot filling UAV in the second UAV cluster is dispatched to fly to the blind spot filling deployment point, and hovers at a preset blind spot filling height higher than the corresponding enhanced UAV to build the signal blind spot filling network.

[0205] In an exemplary embodiment of the present disclosure, when there is only one enhancement drone within the signal coverage space formed by the signal enhancement coverage radius of the blind spot filling drone, the signal blind spot filling network construction module 740 is configured to:

[0206] Determine multiple candidate deployment points so that the current hovering position of the enhancement drone corresponding to the signal blind spot is within the signal enhancement coverage radius of the blind spot-filling drone;

[0207] At least one blind spot filling deployment point is selected from the plurality of candidate deployment points using the preset blind spot filling height as a restriction condition.

[0208] In an exemplary embodiment of the present disclosure, when there are two or more enhancement drones within the signal coverage space formed by the signal enhancement coverage radius of the blind spot filling drone, the signal blind spot filling network construction module 740 is configured to:

[0209] Determining an offset ratio with respect to each of the enhanced drones based on a signal-to-noise ratio of a GNSS satellite signal received by the enhanced drone, wherein the smaller the signal-to-noise ratio of the enhanced drone, the larger the offset ratio, and the closer the blind spot deployment point will be to the enhanced drone;

[0210] Determining a plurality of candidate deployment points such that the current hovering position of each of the enhancement drones is within the signal enhancement coverage radius of the blind spot filling drone;

[0211] At least one blind spot filling deployment point is selected from the plurality of candidate deployment points using the offset ratio and the preset blind spot filling height as restriction conditions.

[0212] In an exemplary embodiment of the present disclosure, the enhanced drone or the blind spot filling drone includes:

[0213] A flight control module is used to control the UAV to fly to the deployment position corresponding to the signal enhancement area or the signal blind area, and to hover stably at a set hovering height;

[0214] A GNSS signal receiving module, configured to receive GNSS satellite signals from multiple GNSS satellite systems;

[0215] a signal enhancement and relay module, electrically connected to the GNSS signal receiving module, for amplifying and filtering the received GNSS satellite signals, and directionally transmitting the enhanced GNSS satellite signals to a GNSS signal receiver on the ground through a relay transmitting antenna;

[0216] A signal quality sensing module is used to detect the signal-to-noise ratio, number of visible satellites or multipath interference characteristics of the GNSS signal received by the UAV in real time, and send the signal quality data to the ground control center;

[0217] A communication module is used for communication and interaction between the UAV and other UAV nodes and the ground control system to achieve collaborative construction and topology optimization of signal enhancement networks or signal blind spot filling networks;

[0218] An energy management module is used to manage the UAV's power supply system and, when the power supply of the power supply system falls below a preset power threshold, send a replacement request to the ground control center or activate a backup UAV node to fill the gap. The specific details of each module of the above-mentioned GNSS signal enhancement device based on UAV signal relay have been described in detail in the corresponding GNSS signal enhancement method based on UAV signal relay, and will not be repeated here.

Claims

1. A GNSS signal enhancement method based on UAV signal relay, characterized in that: include: Acquire pre-collected GNSS signal characteristic data and terrain characteristic data corresponding to the dam slope area, and determine a plurality of signal enhancement areas based on the GNSS signal characteristic data and the terrain characteristic data; Dispatching the first drone cluster to hover and stay at a preset hovering height in each of the signal enhancement areas to form a signal enhancement network, wherein each enhanced drone in the first drone cluster constitutes a network node of the signal enhancement network; Determining a signal-to-noise ratio (SNR) of GNSS satellite signals received by each network node in the signal enhancement network; if the SNR of a target network node is less than or equal to a preset SNR threshold, determining a signal enhancement area corresponding to the target network node as a signal blind spot; Dispatching a second drone cluster to a dynamic blind spot filling position corresponding to the signal blind spot to form a signal blind spot filling network, wherein each blind spot filling drone in the second drone cluster constitutes a network node of the signal blind spot filling network, and the dynamic blind spot filling position is determined based on the real-time updated distribution of the enhanced drones in each of the signal blind spots; Based on the constructed signal enhancement network and the signal blind spot network, the received GNSS satellite signal is enhanced and directionally transmitted to the GNSS signal receivers installed at each slope deformation monitoring point in the dam slope area to achieve signal enhancement of the GNSS signal receiver.

2. The GNSS signal enhancement method according to claim 1, characterized in that: The determining a plurality of signal enhancement areas according to the GNSS signal characteristic data and the terrain characteristic data includes: Divide the dam slope area according to the signal coverage parameters of the enhanced UAV to determine multiple monitoring sub-areas; Determining the GNSS signal strength of each monitoring sub-area according to the GNSS signal characteristic data, and determining the terrain shielding degree of each monitoring sub-area according to the terrain characteristic data; Determining a signal quality score for each monitoring sub-area based on the GNSS signal strength and the terrain obstruction degree; The monitoring sub-areas whose signal quality scores are less than or equal to a preset score threshold are marked as the signal to-be-enhanced areas, thereby obtaining a plurality of signal to-be-enhanced areas.

3. The GNSS signal enhancement method according to claim 2, characterized in that: The dam slope area is divided according to the signal coverage parameters of the enhanced UAV to determine multiple monitoring sub-areas, including: Determining the signal enhancement coverage radius of the enhanced UAV, and determining the signal coverage area on the dam slope area according to the preset hovering height and the signal enhancement coverage radius; Determining a plurality of weak signal distribution aggregation points in the dam slope area based on stored historical signal quality distribution data, wherein each of the weak signal distribution aggregation points corresponds to a GNSS signal receiver; With the goal of covering all the weak signal distribution points with the minimum signal coverage area, target signal coverage areas at multiple different locations are determined, and the target signal coverage areas are used as the monitoring sub-areas.

4. The GNSS signal enhancement method according to claim 3, characterized in that: The determining target signal coverage areas at a plurality of different locations and using the target signal coverage areas as the monitoring sub-areas includes: The target signal coverage areas whose center points are spaced apart by a distance less than or equal to a preset distance are merged to obtain multiple monitoring sub-areas.

5. The GNSS signal enhancement method according to claim 1, wherein: The first drone cluster is dispatched to hover at a preset hovering height in each of the signal enhancement areas to form a signal enhancement network, including: Based on the preset hovering height, the space above the area to be enhanced is divided into spatial grids, and the signal transmission stability of the enhanced drone at each spatial grid is determined based on the drone parameters of the enhanced drone; Select at least two hovering points according to the signal transmission stability; In real time, based on the received GNSS signal quality feedback from each enhanced UAV in the first UAV cluster, the hovering point of each enhanced UAV in the corresponding signal to be enhanced area is adjusted to ensure the quality of the enhanced GNSS satellite signal received by all GNSS signal receivers within the coverage range of the signal enhancement network.

6. The GNSS signal enhancement method according to claim 1, characterized in that: The dispatching of the second UAV cluster to the dynamic blind spot filling position corresponding to the signal blind spot to form a signal blind spot filling network includes: Determine the blind spot deployment point based on the current hovering position of the enhancement drone in the first drone cluster corresponding to each of the signal blind spots, and in combination with the signal enhancement coverage radius of the blind spot filling drone and the preset blind spot filling height; Each blind spot filling UAV in the second UAV cluster is dispatched to fly to the blind spot filling deployment point, and hovers at a preset blind spot filling height higher than the corresponding enhanced UAV to build the signal blind spot filling network.

7. The GNSS signal enhancement method according to claim 6, characterized in that: When there is only one enhancement drone within the signal coverage space formed by the signal enhancement coverage radius of the blind spot filling drone, determining the blind spot filling deployment point includes: Determine multiple candidate deployment points so that the current hovering position of the enhancement drone corresponding to the signal blind spot is within the signal enhancement coverage radius of the blind spot-filling drone; At least one blind spot filling deployment point is selected from the plurality of candidate deployment points using the preset blind spot filling height as a restriction condition.

8. The GNSS signal enhancement method according to claim 6, characterized in that: When there are two or more enhancement drones within the signal coverage space formed by the signal enhancement coverage radius of the blind spot filling drone, determining the blind spot filling deployment point includes: Determining an offset ratio with respect to each of the enhanced drones based on a signal-to-noise ratio of a GNSS satellite signal received by the enhanced drone, wherein the smaller the signal-to-noise ratio of the enhanced drone, the larger the offset ratio, and the closer the blind spot deployment point will be to the enhanced drone; Determining a plurality of candidate deployment points such that the current hovering position of each of the enhancement drones is within the signal enhancement coverage radius of the blind spot filling drone; At least one blind spot filling deployment point is selected from the plurality of candidate deployment points using the offset ratio and the preset blind spot filling height as restriction conditions.

9. The GNSS signal enhancement method according to claim 1, wherein: The enhanced UAV or the blind spot filling UAV includes: A flight control module is used to control the UAV to fly to the deployment position corresponding to the signal enhancement area or the signal blind area, and to hover stably at a set hovering height; A GNSS signal receiving module, configured to receive GNSS satellite signals from multiple GNSS satellite systems; a signal enhancement and relay module, electrically connected to the GNSS signal receiving module, for amplifying and filtering the received GNSS satellite signals, and directionally transmitting the enhanced GNSS satellite signals to a GNSS signal receiver on the ground through a relay transmitting antenna; A signal quality sensing module is used to detect the signal-to-noise ratio, number of visible satellites or multipath interference characteristics of the GNSS signal received by the UAV in real time, and send the signal quality data to the ground control center; A communication module is used for communication and interaction between the UAV and other UAV nodes and the ground control system to achieve collaborative construction and topology optimization of signal enhancement networks or signal blind spot filling networks; The energy management module is used to manage the power supply system of the UAV and send a replacement request to the ground control center or activate a spare UAV node to fill the position when the power supply of the power supply system is lower than a preset power threshold.

10. A GNSS signal enhancement device based on drone signal relay, characterized in that: include: an area determination module, configured to obtain pre-collected GNSS signal characteristic data and terrain characteristic data corresponding to the dam slope area, and determine a plurality of signal enhancement areas based on the GNSS signal characteristic data and the terrain characteristic data; A signal enhancement network construction module is used to dispatch the first drone cluster to hover and reside at a preset hovering height in each of the signal enhancement areas to form a signal enhancement network, wherein each enhanced drone in the first drone cluster constitutes a network node of the signal enhancement network; a signal blind spot screening module, configured to determine the signal-to-noise ratio of the GNSS satellite signals received by each network node in the signal enhancement network; and if the signal-to-noise ratio of the target network node is less than or equal to a preset signal-to-noise ratio threshold, determining the signal enhancement area corresponding to the target network node as a signal blind spot; A signal blind spot network construction module is used to dispatch a second UAV cluster to a dynamic blind spot location corresponding to the signal blind spot to form a signal blind spot network. Each blind spot filling UAV in the second UAV cluster constitutes a network node of the signal blind spot network. The dynamic blind spot filling location is determined based on the real-time updated distribution of the enhanced UAVs in each of the signal blind spots. The signal enhancement module is used to enhance the received GNSS satellite signal based on the constructed signal enhancement network and the signal blind spot network, and transmit it directionally to the GNSS signal receivers set at each slope deformation monitoring point in the dam slope area to achieve signal enhancement for the GNSS signal receiver.

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