GNSS signal enhancement method and device based on unmanned aerial vehicle signal transfer

Abstract

The invention provides a GNSS signal enhancement method and device based on unmanned aerial vehicle signal transfer, and relates to the technical field of dam slope monitoring. The method comprises the following steps: determining a signal to-be-enhanced area in a dam slope area; scheduling the first unmanned aerial vehicle cluster to hover and reside 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 a dynamic blind compensation position corresponding to the signal blind area to form a signal blind compensation network; and based on the signal enhancement network and the signal blind compensation network, signal enhancement is performed on the received GNSS satellite signal, and the signal is directionally transmitted to a GNSS signal receiver to realize dam slope deformation monitoring. According to the technical scheme, the GNSS satellite signal can be effectively enhanced, and the stability and accuracy of deformation monitoring of the dam slope area by the GNSS signal receiver in severe weather are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of dam slope monitoring. Specifically, it relates to a GNSS signal enhancement method and device based on unmanned aerial vehicle (UAV) signal relaying. Background Art

[0002] In the fields of geological disaster monitoring and dam safety operation and maintenance, the Global Navigation Satellite System (GNSS) has been widely used in scenarios such as dam slope deformation monitoring and dam structure stability assessment due to its characteristics of high precision, continuity, and full automation. However, limited by the complex terrain environment in the dam slope area, especially in canyons, steep slopes, or areas with severe signal blockage, the GNSS satellite signal attenuation problem is relatively serious, and even signal blind spots appear, affecting the accuracy and continuity of positioning data acquisition.

[0003] In related technical solutions, usually, more ground GNSS signal receivers are deployed in the dam slope area or the layout angle of the receiving antenna is adjusted to alleviate the signal blockage problem. However, these methods are limited by factors such as terrain features and construction conditions and are difficult to operate continuously and effectively in areas with complex terrain and severe signal attenuation. In addition, in some technical solutions, signal relay devices set at fixed ground positions are tried to be introduced. However, due to structural defects such as fixed deployment positions and limited signal coverage ranges, their signal coverage ability in dynamic environmental changes is insufficient, and they cannot flexibly respond to the 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 adjustable positions, dynamically controllable coverage ranges, and strong adaptability of deployment methods to meet the GNSS signal enhancement and relaying requirements under complex terrain conditions.

[0005] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. 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 UAV signal relaying, which can effectively enhance GNSS satellite signals and improve the stability and accuracy of deformation monitoring of the dam slope area by GNSS signal receivers in bad weather.

[0007] According to the first aspect of the embodiments of the present disclosure, a GNSS signal enhancement method based on UAV signal relaying is provided, including: Obtain the GNSS signal feature data and terrain feature data corresponding to the pre-acquired dam slope area, and determine multiple signal areas to be enhanced according to the GNSS signal feature data and the terrain feature data; Dispatch the first unmanned aerial vehicle (UAV) cluster to hover and stay at the preset hovering height of each signal area to be enhanced, forming a signal enhancement network. Each enhancement UAV in the first UAV cluster constitutes a network node of the signal enhancement network; Determine 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 the preset signal-to-noise ratio threshold, then determine the signal area to be enhanced corresponding to the target network node as a signal blind area; Dispatch the second UAV cluster to the dynamic blind area compensation position corresponding to the signal blind area, forming a signal blind area compensation network. Each blind area compensation UAV in the second UAV cluster constitutes a network node of the signal blind area compensation network. The dynamic blind area compensation position is determined based on the distribution of the enhancement UAVs in each signal blind area updated in real time; Based on the constructed signal enhancement network and the signal blind area compensation network, enhance the received GNSS satellite signals and transmit them directionally to the GNSS signal receivers set at each slope deformation monitoring point in the dam slope area, so as to realize signal enhancement of the GNSS signal receivers.

[0008] In an exemplary embodiment of the present disclosure, the determining multiple signal areas to be enhanced according to the GNSS signal feature data and the terrain feature data includes: Divide the dam slope area according to the signal coverage parameters of the enhancement UAVs to determine multiple monitoring sub-areas; Determine the GNSS signal strength of each monitoring sub-area according to the GNSS signal feature data, and determine the terrain occlusion degree of each monitoring sub-area according to the terrain feature data; Determine the signal quality score of each monitoring sub-area through the GNSS signal strength and the terrain occlusion degree; Mark the monitoring sub-areas with the signal quality score less than or equal to the preset score threshold as the signal areas to be enhanced, and obtain multiple signal areas to be enhanced.

[0009] In an exemplary embodiment of the present disclosure, the dividing the dam slope area according to the signal coverage parameters of the enhancement UAVs to determine multiple monitoring sub-areas includes: Determine the signal enhancement coverage radius of the enhancement UAV, and determine the signal coverage area on the dam slope area according to the preset hovering height and the signal enhancement coverage radius; Determine multiple weak signal distribution aggregation points 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; Aim to cover all the weak signal distribution aggregation points with the least signal coverage areas, determine target signal coverage areas at multiple different positions, and use the target signal coverage areas as the monitoring sub-areas.

[0010] In an exemplary embodiment of the present disclosure, The determining the target signal coverage areas at multiple different positions and using the target signal coverage areas as the monitoring sub-areas includes: Fuse and combine the target signal coverage areas with the interval distance between the center points less than or equal to the preset interval distance to obtain multiple monitoring sub-areas.

[0011] In an exemplary embodiment of the present disclosure, The dispatching the first drone cluster to hover and stay at the preset hovering height in each signal to be enhanced area to form a signal enhancement network includes: Taking the preset hovering height as a reference, conduct spatial grid division in the upper space of the signal to be enhanced area, and determine the signal transmission stability degree of the enhancement drones at each spatial grid according to the drone parameters of the enhancement drones; Screen at least two hovering and staying points according to the signal transmission stability degree; Adjust the hovering and staying points of each enhancement drone in the corresponding signal to be enhanced area in real time according to the received GNSS signal quality feedback by each enhancement drone in the first drone cluster, so as to ensure the enhanced GNSS satellite signal quality received by all GNSS signal receivers within the coverage range of the signal enhancement network.

[0012] In an exemplary embodiment of the present disclosure, the dispatching the second drone cluster to the dynamic blind compensation position corresponding to the signal blind area to form a signal blind compensation network includes: Determine the blind compensation deployment points according to the current hovering and staying positions of the enhancement drones in the first drone cluster corresponding to each signal blind area, and in combination with the signal enhancement coverage radius of the blind compensation drones and the preset blind compensation height; Dispatch each blind compensation drone in the second drone cluster to fly to the blind compensation deployment points and hover and stay at a preset blind compensation height higher than the corresponding enhancement drone to construct the signal blind compensation network.

[0013] In an exemplary embodiment of the present disclosure, when there is only one such enhancement UAV within the signal coverage space formed by the signal enhancement coverage radius of the blind compensation UAV, determining the blind compensation deployment point includes: Determining a plurality of candidate deployment points that enable the current hovering and staying position of the enhancement UAV corresponding to the signal blind area to be within the signal enhancement coverage radius of the blind compensation UAV; With the preset blind compensation height as a limiting condition, screening at least one blind compensation deployment point from among the plurality of candidate deployment points.

[0014] In an exemplary embodiment of the present disclosure, when there are two or more such enhancement UAVs within the signal coverage space formed by the signal enhancement coverage radius of the blind compensation UAV, determining the blind compensation deployment point includes: Determining the offset ratio for each enhancement UAV according to the signal-to-noise ratio of the GNSS satellite signals received by the enhancement UAV, wherein the smaller the signal-to-noise ratio of the enhancement UAV, the larger the offset ratio, and the closer the blind compensation deployment point will be to the enhancement UAV; Determining a plurality of candidate deployment points that enable the current hovering and staying positions of each enhancement UAV to be within the signal enhancement coverage radius of the blind compensation UAV; With the offset ratio and the preset blind compensation height as limiting conditions, screening at least one blind compensation deployment point from among the plurality of candidate deployment points.

[0015] In an exemplary embodiment of the present disclosure, the enhancement UAV or the blind compensation UAV includes: A flight control module, configured to control the UAV to fly to the signal area to be enhanced or the deployment position corresponding to the signal blind area, and stably hover and stay 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, configured to amplify and filter the received GNSS satellite signals, and directionally transmit the enhanced GNSS satellite signals to a GNSS signal receiver on the ground through a relay transmitting antenna; A signal quality perception module, configured to detect in real time the signal-to-noise ratio, the number of visible satellites, or the multipath interference characteristics of the GNSS signals received by the UAV, and send the signal quality data to the ground control center; A communication module, configured for communication interaction between the UAV and other UAV nodes and between the UAV and the ground control system, so as to realize collaborative construction and topology optimization of a signal enhancement network or a signal blind compensation network; An energy management module is configured to manage the power supply system of the drone, and send a replacement request to the ground control center or enable a standby drone node to fill in when the power supply of the power supply system is lower than a preset power threshold.

[0016] According to a second aspect of the embodiments of the present disclosure, there is provided a GNSS signal enhancement device based on drone signal relaying, including: A region determination module is configured to obtain GNSS signal feature data and terrain feature data corresponding to a pre-collected dam slope region, and determine a plurality of signal regions to be enhanced according to the GNSS signal feature data and the terrain feature data; A signal enhancement network construction module is configured to dispatch a first drone cluster to hover and stay at a preset hovering height in each of the signal regions to be enhanced, so as to form a signal enhancement network, and each enhanced drone in the first drone cluster constitutes a network node of the signal enhancement network; A signal blind area screening module is configured to determine 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 a target network node is less than or equal to a preset signal-to-noise ratio threshold, the signal region to be enhanced corresponding to the target network node is determined as a signal blind area; A signal blind area compensation network construction module is configured to dispatch a second drone cluster to a dynamic blind area compensation position corresponding to the signal blind area to form a signal blind area compensation network. Each blind area compensation drone in the second drone 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 drones in each of the signal blind areas updated in real time; A signal enhancement module is configured to perform signal enhancement on the received GNSS satellite signals based on the constructed signal enhancement network and the signal blind area compensation network, and direct the transmission to GNSS signal receivers arranged at each slope deformation monitoring point in the dam slope region, so as to achieve signal enhancement of the GNSS signal receivers.

[0017] The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: The GNSS signal enhancement method based on UAV signal relaying provided in the exemplary embodiments of the present disclosure, on the one hand, determines multiple signal areas to be enhanced in the dam slope area through GNSS signal feature data and terrain feature data, and combines the hierarchical scheduling and deployment of the first UAV cluster and the second UAV cluster, which can effectively avoid problems such as spatial attenuation, uneven occlusion, and dynamic change of blind area distribution of GNSS signals in complex terrains in related technologies. Without relying on fixed base stations, it realizes the spatial reconstruction and link reinforcement of signal weak coverage areas, improves the stability and signal quality of GNSS satellite signals received by GNSS signal receivers, and thus ensures the accuracy of slope deformation result monitoring; on the other hand, through the signal enhancement network composed of enhanced UAVs in the first UAV cluster, the signal enhancement foundation for the dam slope area is realized. On this basis, through the real-time signal-to-noise ratio collected by each network node in the signal enhancement network, the signal blind area is identified, and then the second UAV cluster is scheduled to construct a signal blind area compensation network, which can add blind area compensation enhancement paths with less aerial occlusion in areas where the signal occlusion or signal attenuation in the dam slope area is relatively serious, forming a multi-node and multi-level spatial link reinforcement architecture. The dynamic generation of the blind area compensation deployment position in this structure, combined with the actual hovering position and distribution pattern of the enhanced UAVs, can avoid the situation where the fixed deployment structure cannot compensate for signals due to the inability to cope with the expansion of blind areas or changes in spatial occlusion, further improving the signal transmission intensity while taking into account the flexibility of the system structure and the robustness of the signal enhancement network, so as to provide a GNSS signal service with stable coverage and timely response for GNSS signal receivers, especially ensuring the stability and accuracy of the deformation monitoring of the dam slope area by GNSS signal receivers in bad weather.

[0018] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0020] Figure 1 The flowchart showing a GNSS signal enhancement method based on UAV signal relaying in an embodiment of the present disclosure is shown.

[0021] Figure 2 The flowchart showing a process of screening and determining signal areas to be enhanced in an embodiment of the present disclosure is shown.

[0022] Figure 3 Schematic diagram of a process for determining a hovering stop point of an enhanced unmanned aerial vehicle (UAV) in a signal to be enhanced area is shown.

[0023] Figure 4 Schematic diagram of a process for determining a blind spot filling deployment point of a blind spot filling UAV in a signal blind area is shown.

[0024] Figure 5 Schematic diagram of an application scenario of GNSS signal enhancement based on UAV signal relaying in an embodiment of the present disclosure is shown.

[0025] Figure 6 Schematic diagram of another application scenario of GNSS signal enhancement based on UAV signal relaying in an embodiment of the present disclosure is shown.

[0026] Figure 7 Schematic diagram of a GNSS signal enhancement device based on UAV signal relaying in an embodiment of the present disclosure is shown.

[0027] In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed implementation manners

[0028] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a", "said", and "the" used in this specification and the appended claims are also intended to include the plural forms 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.

[0029] 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 only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0030] Embodiments of the present disclosure first provide a GNSS signal enhancement method based on UAV signal relaying. Hereinafter, an example in which a server of a ground control center executes this method will be used for illustration. Figure 1 Schematic flowchart of a GNSS signal enhancement method based on UAV signal relaying in an embodiment of the present disclosure is shown. Refer to Figure 1 As shown, this method may include steps S110 to S150: Step S110: Obtain the GNSS signal feature data and terrain feature data corresponding to the pre-collected dam slope area, and determine multiple signal areas to be enhanced based on the GNSS signal feature data and the terrain feature data; Step S120: Dispatch the first UAV cluster to hover and stay at the preset hovering height of each signal area to be enhanced, forming a signal enhancement network. Each enhancement UAV in the first UAV cluster constitutes a network node of the signal enhancement network; Step S130: Determine 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 the preset signal-to-noise ratio threshold, determine the signal area to be enhanced corresponding to the target network node as a signal blind area; Step S140: Dispatch the second UAV cluster to the dynamic blind area compensation position corresponding to the signal blind area, forming a signal blind area compensation network. Each blind area compensation UAV in the second UAV cluster constitutes a network node of the signal blind area compensation network. The dynamic blind area compensation position is determined based on the distribution of the enhancement UAVs in each signal blind area updated in real time; Step S150: Based on the constructed signal enhancement network and the signal blind area compensation network, enhance the received GNSS satellite signals and transmit them directionally to the GNSS signal receivers set at each slope deformation monitoring point in the dam slope area, so as to realize the signal enhancement of the GNSS signal receivers.

[0031] According to the GNSS signal enhancement method based on UAV signal relaying in the present disclosure, on the one hand, by determining multiple signal regions to be enhanced in the dam slope area based on GNSS signal feature data and terrain feature data, and combining the hierarchical scheduling and deployment of the first UAV cluster and the second UAV cluster, it is possible to effectively avoid problems such as spatial attenuation, uneven occlusion, and dynamic changes in blind area distribution of GNSS signals in complex terrains in related technologies. Without relying on fixed base stations, it is possible to achieve spatial reconstruction and link reinforcement of signal weak coverage areas, improve the stability and signal quality of GNSS satellite signals received by GNSS signal receivers, and thus ensure the accuracy of slope deformation result monitoring. On the other hand, through the signal enhancement network composed of enhancement UAVs in the first UAV cluster, the signal enhancement foundation for the dam slope area is realized. On this basis, by collecting the real-time signal-to-noise ratio of each network node in the signal enhancement network, signal blind areas are identified, and then the second UAV cluster is scheduled to construct a signal blind area compensation network, which can add blind area compensation enhancement paths with less aerial occlusion in areas with severe signal occlusion or signal attenuation in the dam slope area, forming a multi-node and multi-level spatial link reinforcement architecture. The dynamic generation of the blind area compensation deployment position in this structure, combined with the actual hovering positions and distribution patterns of the enhancement UAVs, can avoid the situation where the fixed deployment structure cannot compensate for signals due to the inability to handle blind area expansion or spatial occlusion changes, further improving the signal transmission intensity while taking into account the flexibility of the system structure and the robustness of the signal enhancement network, thereby providing a GNSS signal service with stable coverage and timely response for GNSS signal receivers, especially ensuring the stability and accuracy of the deformation monitoring of the dam slope area by GNSS signal receivers in bad weather.

[0032] Next, the GNSS signal enhancement method based on UAV signal relaying in the embodiments of the present disclosure will be described in detail.

[0033] In step S110, GNSS signal feature data and terrain feature data corresponding to the pre-collected dam slope area are obtained, and multiple signal regions to be enhanced are determined according to the GNSS signal feature data and the terrain feature data.

[0034] In an exemplary embodiment of the present disclosure, GNSS signal characteristic data refers to measurement parameters related to GNSS signal propagation recorded by deployed GNSS receiving devices, mobile acquisition terminals, or historical databases in a target area. For example, GNSS signal characteristic data can be the signal-to-noise ratio (SNR) at each sampling point, the number of visible satellites, the geometric dilution of precision (GDOP), the multipath interference amplitude, and the signal loss lock rate. Of course, the type of GNSS signal characteristic data is not specifically limited in this embodiment. GNSS signal characteristic data can be real-time acquisition data or offline imported monitoring log files, and can be collected by GNSS observation terminals equipped with multi-system and multi-band receiving capabilities. Moreover, these data can be organized according to a spatial grid structure or a measurement point coordinate system for fusion analysis with terrain feature data.

[0035] Terrain feature data refers to a set of spatial data describing the three-dimensional geomorphic spatial structure in a target area. For example, terrain feature data can include high-resolution digital elevation models (DEMs), oblique photography models, lidar point cloud models, or interferometric synthetic aperture radar (InSAR DEMs), etc. Terrain feature data can be used to extract indicators such as slope, aspect, occlusion angle, relative height difference, reflector distribution, and degree of concavity and convexity. These indicators will serve as terrain input factors for judging the GNSS signal occlusion degree and the reliability of the signal transmission path. Optionally, the terrain feature data can also include surface cover types (such as bare rock, vegetation, and structures) or reflection characteristic parameters for correcting and modeling the influence of multipath interference. In this embodiment, the GNSS signal characteristic data and the terrain feature data can establish a corresponding relationship through a spatial alignment algorithm to form an input data matrix for subsequent area division.

[0036] The signal enhancement required area refers to the ground space area that exhibits high signal attenuation characteristics or long-term signal quality deficiencies during the GNSS signal transmission process. For example, the signal enhancement required area can be a set of areas where the signal-to-noise ratio is significantly lower than that of the surrounding areas, the number of visible satellites is chronically insufficient, or the positioning solution error fluctuates greatly. The determination process of the signal enhancement required area can be combined with enhancing the signal coverage ability of the UAV to divide the area. Taking the enhanced coverage radius of the UAV's signal enhancement as the scale reference, regular or adaptive spatial grid division is performed on the terrain model, and each grid cell is a candidate monitoring sub-area. Subsequently, key indicators in the GNSS signal feature data (such as average SNR, GDOP, number of visible satellites) can be fused and scored with input variables such as the occlusion angle and slope aspect in the terrain feature data. For example, a logical weighting model, a support vector machine model, or a neural network regression model can be used to achieve area scoring. Among them, the occlusion angle refers to the total angle of obstacles within the typical elevation angle range of GNSS satellites after horizontal projection around from a certain assumed height (such as 30 meters) above the monitoring sub-area, which can be used to quantify the terrain occlusion degree; and the signal-to-noise ratio can be used to reflect the actual signal strength and stability. By setting a scoring threshold, the monitoring sub-areas with scores lower than this threshold are marked as signal enhancement required areas, and their corresponding spatial ranges, center point coordinates, or priority levels are output for subsequent use in the UAV deployment link for signal enhancement.

[0037] In step S120, the first UAV cluster is scheduled to hover and stay at the preset hovering height in each of the signal enhancement required areas, forming a signal enhancement network, and each enhanced UAV in the first UAV cluster constitutes a network node of the signal enhancement network.

[0038] In an exemplary embodiment of the present disclosure, the first UAV cluster refers to a cluster system composed of multiple enhanced flight platforms equipped with GNSS signal reception and relay devices. The enhanced UAVs in the first UAV cluster can have functions such as autonomous flight, fixed-point hovering, positioning ability monitoring, and air relay communication. An enhanced UAV refers to a flight unit used to perform GNSS signal reception, enhancement, and directional relay transmission tasks in the target space area. The enhanced UAV can receive GNSS satellite signals, perform low-noise amplification and filtering processing on the signals, and transmit enhanced signals downward through a relay antenna for use by ground GNSS receivers. In an optional implementation, the enhanced UAV can be a multi-rotor aircraft, which has excellent hovering stability and vertical takeoff and landing capabilities, and can carry core components such as a high-gain directional antenna, a GNSS signal module, a flight control system, and a communication module. Of course, a fixed-wing vertical takeoff and landing composite aircraft can also be used as an alternative platform, and this embodiment does not make special limitations in this regard.

[0039] The preset hover height refers to the vertical hover height set above the signal enhancement area according to factors such as terrain undulation, signal coverage radius, and airspace restrictions. The range of the preset hover height can generally be set from 20 meters to 80 meters and can be dynamically adjusted according to the steepness of the slope. The setting of the preset hover 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 avoid local occlusion or reflection interference as much as possible. The setting of the preset hover height can enable the signal enhancement coverage radius to cover the main observation points of the target area under the given antenna pattern and transmission power, and at the same time meet the power consumption and endurance limitations of the UAV flight platform. The setting of the preset hover height specifically needs to be customized according to the specific application scenario, and this embodiment is not limited thereto.

[0040] The signal enhancement network refers to a multi-node structure formed by multiple enhancement UAVs in the first UAV cluster in the air according to a predetermined layout rule, so as to make it have spatial coverage continuity, inter-node cooperation ability, and link reconfigurability. In order to construct the signal enhancement network, the hover stop points in each signal enhancement area can be determined first. The hover stop point refers to the specific position coordinates of the enhancement UAV performing the enhancement task in space. The hover stop point can take into account the signal transmission stability, the GNSS signal fluctuation condition in the area, the terrain occlusion situation, and the deployment spacing of adjacent UAVs.

[0041] Optionally, a three-dimensional space grid structure can be constructed above the signal enhancement area. Each space grid represents an optional hover position, and the signal enhancement ability of each point can be evaluated by combining flight simulation data and channel models. During the evaluation process, the evaluation can be carried out from data such as the elevation angle distribution of receiving GNSS satellite signals at this point, the possible occlusion angle range, the line-of-sight path length and attenuation factor between the UAV and the target area, calculate the reliability score of the signal relay path, and screen the relatively reliable hover stop points according to the score. The spacing between the hover stop points should be slightly larger than the signal coverage radius of the enhancement UAV to avoid redundant deployment, and at the same time retain a certain overlapping redundant area to enhance the system robustness.

[0042] After the selection of the hover stop points is completed, the system can send a scheduling instruction to the first UAV cluster. Each enhancement UAV flies to the specified position according to the task instruction path and hovers at the corresponding height. The flight control process can achieve precise hovering based on the dual redundancy mechanism of GPS positioning and lidar ranging, and can also use an on-board Inertial Measurement Unit (IMU), visual positioning module or ultrasonic sensor to achieve fine movement correction to ensure the stability of the position under the condition of slight wind disturbance or air disturbance.

[0043] After the hover and stay are completed, each enhanced UAV sequentially starts the GNSS signal reception and relay process, that is, taking its own spatial position as the receiving antenna platform to receive satellite signals. After the signals are processed by amplification and filtering, they are transmitted downward by the directional relay module to cover the target ground GNSS receiver. Multiple enhanced UAV nodes can also form a relay subnet through a wireless communication link for link redundancy, signal synchronization, and control command forwarding, improving the fault tolerance and expandability of the overall enhanced network of the system. Of course, the above deployment process is only a schematic example, and other methods can be used to implement the deployment in combination with the real scenario. This embodiment is not limited thereto.

[0044] In step S130, determine 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 the preset signal-to-noise ratio threshold, determine the signal area to be enhanced corresponding to the target network node as a signal blind area.

[0045] In an exemplary embodiment of the present disclosure, a network node refers to each enhanced UAV in the signal enhancement network that is in a stable hover state and undertakes the tasks of GNSS signal reception and forwarding. Each enhanced UAV is a signal enhancement network node. To accurately evaluate the signal environment of the spatial position where the network node is located, each network node can be equipped with a GNSS signal quality perception module for collecting real-time signal strength data. For example, the GNSS signal quality perception module can collect, including but not limited to, the signal-to-noise ratio (Signal-to-Noise Ratio, SNR), the carrier-to-noise ratio of the GNSS system receiving channel, the number of visible satellites, and the satellite arrangement quality index (such as GDOP or PDOP).

[0046] The signal-to-noise ratio is a key parameter describing the ratio of the GNSS signal strength to the background noise, usually measured in decibels (dB). Its measurement method can also be based on the internal demodulation result of the GNSS receiving module or obtained by sampling with an external power detector. To ensure the evaluation stability, the signal-to-noise ratio can be smoothed in the time series by using the moving 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 requirements, which 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. This exemplary embodiment does not make special limitations thereto.

[0047] It is possible to periodically receive signal quality feedback data from each enhanced drone node and compare whether the signal-to-noise ratio of the current node is lower than or equal to a 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 continuously, or is 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 that cannot be normally covered by the first-layer enhancement network, that is, a signal blind area. To prevent misjudgment, the spatial consistency of the judgment result can also be verified through the signal-to-noise ratio difference between adjacent network nodes. When necessary, a spatial interpolation model can be introduced to finely estimate the boundary of the blind area to form a spatially continuous blind area recognition result.

[0048] The area identified as the signal blind area will be marked as the blind area compensation target area and used as one of the input data for subsequent blind area compensation deployment. A deployment strategy for the second drone cluster can be dynamically generated based on this annotation result, and a blind area compensation signal link structure for covering these blind areas can be constructed, so as to effectively solve the problem of signal coverage holes caused by insufficient coverage ability of the first-layer node network or terrain mutation occlusion, thereby improving the continuity and integrity of the overall signal enhancement network.

[0049] In step S140, the second drone cluster is scheduled 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 drone in the second drone cluster constitutes a network node of the signal blind area compensation network. The dynamic blind area compensation position is determined based on the distribution of the enhanced drones in each signal blind area updated in real time.

[0050] In an exemplary embodiment of the present disclosure, the second drone cluster refers to a set of flight platforms dedicated to covering and enhancing signal blind areas that are difficult to effectively cover by the first-layer enhancement network. The drones in the second drone cluster are blind area compensation drones. A blind area compensation drone refers to a flight device with GNSS signal reception, enhancement, and directional forwarding capabilities. Its functional structure can be similar to that of an enhanced drone, but there are differences in deployment strategies, hovering heights, path scheduling, and target task positioning. The blind area compensation drone can be set as a multi-rotor flight platform with a high-power directional transmission module and a high-fault-tolerant flight control ability. Of course, when the terrain permits or the blind area compensation area has a large span, a vertical takeoff and landing fixed-wing drone with stronger endurance can also be selected.

[0051] The dynamic blind area compensation position refers to the hovering deployment point of the blind area compensation drone in the airspace. The dynamic blind area compensation position is a spatial coordinate specially calculated and generated to compensate for the signal blind area, rather than a statically preset conventional deployment point. The determination of the dynamic blind area compensation position can comprehensively consider information such as the current hovering position of the first-layer enhanced drones, the boundary contour of the signal blind area, terrain occlusion conditions, the coverage radius of the blind area compensation drone, and the target priority of the blind area compensation task.

[0052] In an alternative embodiment, by analyzing the positional relationship between the signal blind area and the adjacent first-layer enhancement nodes, a blind area spatial distribution map can be constructed, and based on the coverage shortage direction of the known enhancement network nodes, the main direction vector of the coverage gap can be calculated. Subsequently, in combination with the standard signal coverage radius of the blind area compensation UAV and the preset blind area compensation height (this height is generally set to be about 10 to 30 meters higher than the hovering height of the corresponding enhancement 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 points not only requires being within the visible propagation path range of the signal blind area in three-dimensional space, but also needs to satisfy that the GNSS signal link from the blind area compensation node to the target area has an unobstructed line of sight. When necessary, the occlusion angle between each candidate point and the ground receiving point can be calculated using the digital terrain model for exclusion.

[0053] During the process of determining the blind area compensation deployment points, a node distance constraint can also be introduced, that is, it is required to maintain a specific minimum distance between the boundary of the coverage area of the blind area compensation UAV and the adjacent enhancement nodes to avoid frequency interference or signal overlap. In addition, if there is only a single enhancement node within the signal blind area, it is preferably to set the blind area compensation deployment point in the direction where the enhancement node is at the boundary of the coverage area; if the blind area is formed by the coverage gap between two or more enhancement nodes, the position of the blind area compensation deployment point should be adjusted using an offset weighting strategy in combination with the signal-to-noise ratio data received by each node, making the blind area compensation UAV closer to the direction of the node with a weaker signal, and constructing the most balanced blind area compensation network topology for the spatial link structure.

[0054] After the blind area compensation deployment points are determined, the blind area compensation UAVs in the second UAV cluster can be guided to fly to the target deployment points through flight control instructions and hover and stay at the preset blind area compensation height. The set of network nodes formed by all the blind area compensation UAVs constitutes the signal blind area compensation network. As the upper layer or auxiliary layer structure of the signal enhancement network, the signal blind area compensation network has the functions of initially enhancing, amplifying, and re-directing and transmitting the received satellite signals. This hierarchical network setting for signal enhancement can effectively solve the problem of airspace blind areas caused by terrain barriers, complex reflections, or sparse enhancement network structures, and improve the spatial integrity and stability of the entire GNSS signal enhancement system.

[0055] In step S150, based on the constructed signal enhancement network and the signal blind area compensation network, the received GNSS satellite signals are enhanced and directionally transmitted to the GNSS signal receivers arranged at each slope deformation monitoring point in the dam slope area, so as to achieve signal enhancement for the GNSS signal receivers.

[0056] In an exemplary embodiment of the present disclosure, the signal enhancement network and the signal blind spot compensation network cooperate to form a multi-level and dynamically adaptive airborne GNSS signal enhancement system. This airborne GNSS signal enhancement system in the airspace is flexible in layout, adjustable in space link, and controllable in signal path, and can meet the high-quality signal transmission requirements in the complex occlusion environment of the dam slope area.

[0057] Signal enhancement refers to processing the original GNSS satellite signals received by the enhancement UAVs and the blind spot compensation UAVs to make them have higher signal strength, lower noise interference, more stable phase continuity, and waveform characteristics more suitable for ground GNSS receivers to receive. The signal enhancement process can be achieved through low-noise amplification, that is, using a Low Noise Amplifier (LNA) module to amplify the input weak GNSS signal with low power and low distortion to improve the signal-to-noise ratio of the received signal; and by using intermediate frequency or baseband filtering processing, removing non-GNSS band interference or multipath superposition components through a band-pass filter to ensure that the output signal has good spectral purity and time synchronization characteristics.

[0058] After signal enhancement is completed, the enhanced GNSS signal is re-directed and transmitted to the ground through a relay transmission module installed on the UAV. The relay transmission module refers to a transmission device integrated with a high-gain directional antenna, a power control unit, and a transmitted signal modulation component, which is used to radiate the processed GNSS signal downward at a specific beam angle and transmission power to ensure that the signal can cover the set target area. Directional transmission can achieve spatial alignment through antenna elevation angle adjustment, phased array beam control, or mechanical servo actuators. It can also use a narrow beam high-gain directional antenna combined with an automatic correction algorithm to achieve directional stability under the condition of slight pose disturbance of the flight platform.

[0059] The signal receiving object is a GNSS signal receiver deployed in the dam slope area. The GNSS signal receiver refers to a terminal device with high-precision positioning capabilities and can receive GNSS signals from an airborne relay signal source. It is usually a dual-frequency or multi-frequency GNSS receiving module, and combined with the installation method of the deformation monitoring point and the data transmission interface, it can analyze the pseudorange, carrier phase, or differential observation of the received enhanced signal to generate the deformation trend monitoring result.

[0060] In the actual signal transmission process, the enhancement UAVs and the blind spot compensation UAVs can dynamically adjust the transmission power and angle according to parameters such as the distribution density of GNSS signal receivers, the signal path environment, and the antenna beam width to achieve signal enhancement for multiple GNSS signal receivers. In addition, to ensure the stability of the link transmission, the signal enhancement network and the signal blind spot compensation network can also share node status information, signal quality parameters, and location information through a communication link to construct a multi-level airborne relay network with self-adaptive topology reconstruction and link backup functions.

[0061] By constructing a signal enhancement network and a signal blind spot compensation network, GNSS signals can be stably received in the dam slope area where there are originally severe obstructions or weak coverage, so that ground GNSS receiving devices can continuously obtain high-quality positioning and calculation inputs under complex terrain conditions, ensuring the continuity, accuracy, and timeliness of slope deformation monitoring data. This process has the capabilities of non-intrusive layout, high spatial resolution coverage, and aerial dynamic scheduling, and is applicable to monitoring scenarios of dams, landslide bodies, canyons, and other areas where GNSS signals are prone to attenuation.

[0062] Next, the content in steps S110 to S150 will be described in detail.

[0063] In an exemplary embodiment of the present disclosure, the determination of multiple signal regions to be enhanced based on GNSS signal characteristic data and terrain characteristic data can be achieved through the steps in Figure 2 , as shown in Figure 2 , and specifically may include: Step S210: Divide the dam slope area according to the signal coverage parameters of the enhancement UAV to determine multiple monitoring sub-areas; Step S220: Determine the GNSS signal strength of each monitoring sub-area according to the GNSS signal characteristic data, and determine the terrain occlusion degree of each monitoring sub-area according to the terrain characteristic data; Step S230: Determine the signal quality score of each monitoring sub-area through the GNSS signal strength and the terrain occlusion degree; Step S240: Mark the monitoring sub-areas with signal quality scores less than or equal to a preset score threshold as the signal regions to be enhanced, and obtain multiple signal regions to be enhanced.

[0064] Among them, the signal coverage parameter of the enhancement UAV refers to the effective signal enhancement coverage radius jointly determined by the UAV's antenna pattern, transmission power, flight stability, and terrain visibility range at a specific hovering height. This radius can be represented as a three-dimensional spherical cap area with the hovering point of the enhancement UAV as the center of the sphere and the signal reach distance corresponding to a certain power lower limit as the radius. This coverage area can be parameter calibrated through actual flight test data or field strength simulation models in combination with multiple factors such as GNSS signal frequency, signal enhancement module gain characteristics, antenna pitch angle range, and preset hovering height.

[0065] In specific implementation, grid division can be used to divide the dam slope area into sub-areas. The size of the spatial grid can be equal to or slightly smaller than the signal coverage diameter of the enhanced UAV to ensure that each sub-area after division can be effectively covered by a single UAV without dead spots. Optionally, if the dam slope area exhibits obvious slope changes or discontinuous spatial topography features, contour-driven terrain adaptive zoning can be adopted to hierarchically divide the target area with different elevation levels as the cutting reference, so as to improve the terrain adaptability on the basis of ensuring signal coverage consistency.

[0066] On the basis of completing the division of the monitoring sub-areas, the GNSS signal strength and terrain occlusion degree of each monitoring sub-area can be further evaluated. The GNSS signal strength refers to the stable strength performance of the GNSS signal received by the sampling points in the sub-area per unit time. For example, the signal-to-noise ratio (SNR) can be used as the main evaluation index, and parameters such as the number of visible satellites, the number of signal interruptions, and the average carrier-to-noise ratio can be combined as references. The SNR data can be provided by the ground receiving equipment deployed historically or collected by the UAV through initial detection flights in the non-enhanced state in the area. Optionally, by introducing a GNSS signal quality map, three-dimensional interpolation and spatial fitting of the historical flight data can be performed to form a signal strength distribution map of the target area.

[0067] The terrain occlusion degree refers to the degree of occlusion of the GNSS signal propagation path caused by factors such as mountains, buildings, or vegetation in the monitoring sub-area, which can be quantified by calculating the occlusion angle or sky view factor (SVF) in each direction through a three-dimensional terrain model. The occlusion angle refers to the angular range of invisible obstacles in the conical space from the center point of the sub-area vertically upward to the zenith. The larger it is, the more serious the occlusion is. The sky view factor is the ratio of the visible sky area of a unit space point to the total ideal sky area. The lower the sky view factor, the stronger the occlusion. The above parameters can be solved by simulation based on the digital elevation model combined with the ray projection algorithm, or obtained by the UAV carrying lidar to scan in real time to obtain point clouds and perform airspace inversion. This example embodiment does not make special limitations on this.

[0068] After obtaining the GNSS signal strength and terrain occlusion degree of each monitoring sub-area, the GNSS signal strength and terrain occlusion degree can be fused and calculated to obtain the signal quality score of each monitoring sub-area. The signal quality score is a comprehensive evaluation index used to characterize the positioning reliability and enhancement requirement degree of the area under the current GNSS network. The weighted linear model, analytic hierarchy process model, or support vector machine regression model can be used for score calculation. This embodiment is not limited thereto. For example, the signal quality score can be determined by the following relational expression: ; where, may represent the signal quality score of the i-th monitoring sub-region, may represent the average signal-to-noise ratio of the i-th monitoring sub-region, may represent the maximum signal-to-noise ratio in the i-th monitoring sub-region, may represent the average occlusion angle of the i-th monitoring sub-region, may represent the maximum occlusion angle in the i-th monitoring sub-region, and may represent adjustable weight parameters, respectively representing the attention degrees to signal strength and terrain influence.

[0069] After the signal quality score calculation is completed, the score results of all monitoring sub-regions can be compared with a preset score threshold. The monitoring sub-regions with signal quality scores lower than or equal to the preset score threshold will be determined as signal areas with poor quality that require priority deployment of signal-boosting drones. The score threshold can be dynamically set in combination with the application scenario. For example, in the main control area of the dam slope with high safety monitoring requirements, a higher scoring standard can be adopted to ensure no blind spots in key areas. There are no special restrictions on the setting of the score threshold in this exemplary embodiment.

[0070] By determining the signal quality score through GNSS signal feature data and terrain feature data, multiple signal areas to be enhanced are determined, realizing the quantitative analysis of the signal environment under the complex spatial structure of the dam slope, which can effectively reduce the number of drones required for signal relay enhancement and reduce the operating cost.

[0071] In an exemplary embodiment of the present disclosure, the area division of the dam slope area according to the signal coverage parameters of the signal-boosting drones to determine multiple monitoring sub-regions can be achieved through the following steps, which may specifically include: The signal enhancement coverage radius of the signal-boosting drone can be determined, and the signal coverage area on the dam slope area can be determined according to the preset hovering height and the signal enhancement coverage radius; based on the stored historical signal quality distribution data, multiple weak signal distribution aggregation points are determined in the dam slope area, where each weak signal distribution aggregation point corresponds to a GNSS signal receiver; aiming at covering all weak signal distribution aggregation points with the fewest signal coverage areas, the target signal coverage areas at multiple different positions are determined, and the target signal coverage areas are used as monitoring sub-regions.

[0072] Among them, the signal enhancement coverage radius refers to the maximum horizontal projection distance of the ground receiving device that can be effectively covered by the enhanced drone at the 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 stronger 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 decays faster. The signal enhancement coverage radius at least satisfies that after the GNSS signal reaches the ground receiver on the enhanced transmission path, its signal-to-noise ratio is greater than the minimum recognizable threshold of the receiver and the link bit error rate is maintained within a stable range.

[0073] After obtaining the signal enhancement coverage radius of the enhanced drone, 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. The signal coverage area can be abstracted as a cone with the enhanced drone hovering point as the vertex and the overlooking direction as the axis. Its intersection on the terrain projection surface is the effective signal action boundary. Due to the terrain characteristics of the dam slope area, such as ups and downs and changes in slope direction, the above-mentioned cone can be projected and intersected with the digital elevation model in actual calculations to form an irregular polygonal coverage area, and the edge buffering and expansion of the boundary are performed based on the principle of spatial proximity to improve the continuous coverage capability.

[0074] 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 gathered as key observation locations for signal enhancement for layout driving. Weak signal distribution clusters refer to the set of spatial points in the dam slope area where the GNSS signal has long shown 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.

[0075] In this embodiment, the layout strategy can be executed using a multi-circle site selection model based on coverage optimization with the goal of covering all weak signal distribution aggregation points with the least signal coverage area. For example, the minimum circle coverage algorithm, genetic algorithm, or discrete grid coverage approximation method can be used to iteratively generate multiple candidate signal coverage area sets in the search space, and finally select the combination of monitoring sub-regions that completely contains the weak signal aggregation points and uses the smallest number of coverage areas as the deployment result. To ensure that the deployment structure is consistent with the actual terrain conditions, terrain adaptability judgment can be performed after the coverage points are generated. For example, check whether there is sufficient airspace above the target hover point of the enhanced UAV and whether the minimum overhead occlusion requirement is met, and fine-tune the layout points if necessary.

[0076] The finally determined target signal coverage area is used as the monitoring sub-region, which not only meets the task requirements of covering weak signal aggregation points, but also has characteristics such as adjustable deployment redundancy, minimizing the number of nodes, and maximizing the enhanced coverage efficiency, facilitating the execution of precise enhanced UAV scheduling and signal enhancement network construction strategies in subsequent steps.

[0077] Optionally, when determining the target signal coverage areas at multiple different positions and using the target signal coverage areas as monitoring sub-regions, the target signal coverage areas with the distance between the center points less than or equal to the preset interval distance can be merged to obtain multiple monitoring sub-regions.

[0078] Among them, the center point refers to the geometric center of each signal coverage area or the spatial projection coordinates of the actual predetermined deployment point of the enhanced UAV, usually represented in a geographic coordinate system (such as WGS84) or a projection coordinate system (such as 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 the set preset interval distance. The preset interval distance is the minimum recognizable spacing threshold set by the system according to the UAV coverage radius, signal enhancement overlap tolerance, and deployment cost. Its value is usually slightly less than the signal interference boundary caused by the simultaneous deployment of two UAVs. For example, it can be set to 80% to 100% of the signal coverage radius. Of course, it can be custom-set according to the actual application situation, and this embodiment is not limited thereto.

[0079] 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 these two target signal coverage areas. At this time, to reduce the number of UAV deployments, avoid signal superposition interference, and improve network coordination, the fusion and merging mechanism can be triggered. Fusion and merging means merging two or more target signal coverage areas into one monitoring sub-area, and expanding its boundary to the joint contour that can completely cover all the original target points. To achieve efficient fusion, a geometric union algorithm or a 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 is repositioned with the goal of maximizing joint coverage.

[0080] The fusion and merging process can also take into account coverage integrity and deployment flexibility. In some slope areas, the terrain changes violently or the GNSS signal environment is complex, which may cause some weak signal aggregation points to be located at the boundary positions of two or more signal coverage areas. At this time, 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, preferably introducing a boundary buffer zone, and the buffer width can be set to 10% to 20% of the enhanced UAV coverage radius to ensure that the receiving device at the boundary can also obtain a continuous and stable enhanced signal. At the same time, to avoid resource waste caused by boundary expansion, the buffer strategy can be adjusted in combination with the task priority or channel interference estimation.

[0081] By fusing and merging overlapping monitoring sub-areas, the number of redundant nodes can be effectively reduced, the interference problem caused by the signal enhancement overlapping area can be reduced, and at the same time, the maintainability and scalability of the signal relay network structure can be improved, which is suitable for realizing high-density GNSS signal enhancement deployment in complex slope environments with limited space resources or large differences in task levels.

[0082] In an exemplary embodiment of the present disclosure, it can be achieved by Figure 3 the steps in to schedule the first UAV cluster to hover and stay at the preset hovering height in each signal area to be enhanced, forming a signal enhancement network. Referring to Figure 3 shown, it can specifically include: Step S310, based on the preset hovering height, perform spatial grid division in the upper space of the signal area to be enhanced, and determine the signal transmission stability degree of the enhanced UAV at each spatial grid according to the UAV parameters of the enhanced UAV; Step S320, screen at least two hovering and staying points according to the signal transmission stability degree; Step S330: Adjust the hovering stop points of each of the enhanced drones in the corresponding signal enhancement area to be enhanced in real time according to the received GNSS signal quality fed back by each of the enhanced drones in the first drone cluster, so as to ensure the quality of the enhanced GNSS satellite signals received by all GNSS signal receivers within the coverage of the signal enhancement network.

[0083] Among them, a three-dimensional space grid can be constructed in the space above the signal enhancement area to be enhanced to assist in determining the appropriate hovering stop points of the drones. The three-dimensional space grid refers to a set of space coordinates formed above the space of the signal enhancement area to be enhanced according to the set height levels and horizontal division rules, and each grid cell represents a potential hovering candidate point. The grid division can be generated based on regular cube grids, equidistant spherical grids or terrain contour-driven grids. It is also possible to generate a grid layout plane at a preset hovering height with a spacing of 5% - 10% of the coverage redundancy rate to ensure that the signal enhancement areas between the deployed drone nodes minimize overlapping interference while maintaining coverage continuity.

[0084] Based on the three-dimensional space grid, the signal transmission stability of each space grid cell can be evaluated by combining the flight parameters, attitude control capabilities, antenna beam width, antenna directivity of the enhanced drones, and the transmission power curve of the GNSS signal enhancement module. The signal transmission stability refers to the power stability, noise suppression ability and multipath interference tolerance of the GNSS signal from space to ground in this link path when the enhanced drone performs the relay task at a specific position. The evaluation can be calculated based on a combination of the free space path loss model (Free Space Path Loss, FSPL) and the terrain occlusion correction model, while considering the trade-off relationship between the main lobe directional attenuation and the edge side lobe noise enhancement of the enhanced signal at different azimuth angles.

[0085] Simulation link propagation simulation can be introduced, combined with the DEM height data of each grid point position, to predict the path loss distribution of the signal transmitted from the drone to the ground target area, and accordingly generate the signal transmission stability for each grid point. This is a common technical means for those skilled in the art and will not be elaborated here. The higher the signal transmission stability, the more suitable the point is as a hovering stop point to perform the enhancement task. Of course, a channel gain - perturbation prediction function can also be constructed by combining parameters such as the drone task load, environmental wind field, and ground reflection interference coefficient to further improve the discrimination accuracy of the performance of different deployment points.

[0086] After obtaining the signal transmission stability degree, at least two grid points with relatively high stability scores can be selected from the grid points with relatively high stability scores as the final hovering and staying points of the current signal to be enhanced area. Of course, in some alternative embodiments, the screening process can also consider the mutual influence between all signal areas to be enhanced. For example, a greedy optimization algorithm or a multi-objective function scheduling strategy can also be adopted to preferentially select a combination of points with the minimum deployment interval, the minimum channel interference, and the optimal redundant coverage between each other as the deployment output. If multiple signal areas to be enhanced are adjacent to each other, local regulation logic can also be introduced during the deployment process to form a continuous signal link for cross-regional layout, which is beneficial to the fusion handover between the subsequent blind spot filling network and the enhancement network.

[0087] After the initial deployment, to ensure the stability and timeliness of the network, the GNSS signal quality data fed back by each enhancement UAV in the first UAV cluster can be continuously received. The signal quality data can include various parameters such as the real-time received signal-to-noise ratio, satellite geometric distribution, carrier phase continuity, and relay signal modulation degree. To improve the system response speed and avoid unnecessary frequent position adjustments, a dynamic update period and trigger conditions can be set. If the signal-to-noise ratio continuously decreases, the GDOP continuously increases, or the multipath interference continuously rises within several consecutive periods, a position adjustment command can be triggered at this time to switch the hovering and staying points.

[0088] In the adjustment strategy, it is carried out in the form of local grid repositioning, that is, a hovering and staying point with better signal stability is selected from the set of feasible hovering and staying points preset near the current hovering and staying point for switching, ensuring the improvement of the enhancement coverage ability while maintaining the network connectivity. When necessary, the self-organizing network reconstruction mechanism can also be used to guide adjacent nodes to move collaboratively to fill vacancies or optimize the link structure, so as to keep the GNSS signal receivers within the coverage range of the entire signal enhancement network always in the area of high-quality enhancement signals.

[0089] Through the above deployment mechanism, while improving the spatial layout accuracy, the adaptability of the network under terrain mutations, environmental disturbances, or mission changes can be enhanced, providing a highly robust and highly controllable aerial signal enhancement support for the high-precision GNSS monitoring system in complex slope areas.

[0090] In an exemplary embodiment of the present disclosure, the second UAV cluster can be scheduled to the dynamic blind spot filling position corresponding to the signal blind spot through the following steps to form a signal blind spot filling network, which can specifically include: The blind spot compensation deployment point can be determined according to the current hovering position of the enhanced UAVs in the first UAV cluster corresponding to each signal blind spot, in combination with the signal enhancement coverage radius of the blind spot compensation UAVs and the preset blind spot compensation height; each blind spot compensation UAV in the second UAV cluster is scheduled to fly to the blind spot compensation deployment point and hover and stay at a preset blind spot compensation height higher than the corresponding enhanced UAV to construct a signal blind spot compensation network.

[0091] Among them, after the signal blind spot is located, to ensure that the position where the blind spot compensation UAV is deployed can fully cover the blind spot, the blind spot compensation deployment point can be determined by comprehensively considering the signal enhancement coverage radius of the blind spot compensation UAV, its hovering flight height, and the positional relationship of adjacent enhanced UAVs. The signal enhancement coverage radius of the blind spot compensation UAV refers to the stable signal enhancement range that can be formed under the conditions of set transmission power, antenna directivity parameters, and specific hovering height. This radius can be obtained through joint simulation calculation of the channel propagation model or by field measurement to obtain typical parameters. This embodiment is not limited thereto. The size of the coverage radius determines the number and residence range of enhanced UAVs that a single blind spot compensation UAV can assist in signal enhancement. Its optimization goal is to cover as many enhanced UAVs in signal blind spots as possible with as few blind spot compensation nodes as possible.

[0092] During the process of determining the blind spot compensation deployment point, a spatial candidate deployment point set can be constructed first. This deployment point set is a three-dimensional equidistant grid point set generated with the center point of the signal blind spot as the origin according to the coverage radius of the blind spot compensation UAV. Each point is a possible blind spot compensation deployment point for the blind spot compensation UAV. To further ensure the effectiveness of the deployment, visibility analysis can be performed on the candidate blind spot compensation deployment point set to evaluate whether there is an occlusion path to the boundary of the target blind spot, and at the same time analyze the possibility of interconnection and signal redundancy with surrounding enhanced UAVs. When necessary, elimination and screening are carried out in combination with the topological requirements of the communication link between nodes.

[0093] The height of the blind spot compensation deployment point is generally higher than the preset hovering height of the corresponding enhanced UAV. For example, the height of the blind spot compensation deployment point can be set in the range of 10 meters to 30 meters above the hovering height of the enhanced node, which specifically depends on the terrain undulation characteristics, airspace control requirements, and signal propagation attenuation characteristics of the current mission area. By increasing the hovering height of the blind spot compensation node, a larger signal propagation angle and a clearer sky view can be obtained, thereby increasing the effective action range for ground GNSS receivers and reducing the risk of link failure caused by terrain occlusion or edge interference.

[0094] After the above blind spot filling deployment points are determined, suitable blind spot filling UAVs can be called from the second UAV cluster according to the task priority, flight resource scheduling table, and power status to perform flight path planning and target instruction issuing. For flight path planning, high-precision flight trajectory planning strategies based on the 3D A-star algorithm or Bezier curve fitting can be used to ensure that the blind spot filling UAVs can safely avoid obstacles and efficiently reach the target blind spot filling deployment points in complex airspace environments. This is a common technical means in this field and will not be elaborated here.

[0095] After the blind spot filling UAVs reach the deployment points, they can stably hover at the preset blind spot filling altitude and start the GNSS signal receiving, enhancing, and forwarding devices to form new aerial relay nodes and complete the original signal blind spots. All the deployed blind spot filling nodes together constitute a signal blind spot filling network. As the upper space signal supplement layer for the first enhancement network, this network not only realizes the dynamic blocking of the blind spot range but also has the ability to perform redundant compensation and structural reconstruction when a node fails, thus greatly enhancing the space adaptability, system robustness, and monitoring data continuity of the overall GNSS signal enhancement system in the complex terrain of the dam slope.

[0096] In an exemplary embodiment of the present disclosure, when there is only one enhancement UAV within the signal coverage space formed by the signal enhancement coverage radius of the blind spot filling UAV, the blind spot filling deployment points can be determined through the following steps, which specifically may include: Multiple candidate deployment points can be determined such that the current hovering and staying position of the enhancement UAV corresponding to the signal blind spot is within the signal enhancement coverage radius of the blind spot filling UAV; with the preset blind spot filling altitude as a limiting condition, at least one blind spot filling deployment point is selected from the multiple candidate deployment points.

[0097] Among them, the current hovering and staying position refers to the stable hovering and staying position of the enhancement UAV during the signal relaying task, which is usually the fusion positioning result mainly based on real-time GNSS data and supplemented by the output of the inertial navigation system in three-dimensional space. This position not only determines the central axis of its signal enhancement but also constitutes the spatial reference point for the blind spot filling path planning.

[0098] The candidate deployment points refer to a set of spatial points in three-dimensional space that meet certain geometric constraints and propagation conditions. The geometric constraints mainly manifest as: 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 spot filling UAV, and the staying point of this enhancement UAV should be at the edge or effective coverage boundary area of the signal action area of the blind spot filling UAV to avoid central redundancy and inability to expand the blind spot coverage range.

[0099] Specifically, the stationing point of the enhanced drone can be used as the center, combined with the preset signal enhancement coverage radius and vertical blind spot height, and multiple evenly distributed candidate points can be generated in the upper space through spherical geometry modeling to form a spherical shell of candidate deployment points. In the calculation process, the propagation characteristics and interference tolerance of GNSS signals can be considered. For example, by setting the lower limit of the transmission elevation angle, the terrain occlusion tolerance range, the upper limit of the path loss and other standards, the points that do not meet the propagation conditions can be eliminated from all candidate points, thereby improving the effectiveness of deployment and channel stability.

[0100] In order to further ensure that the blind spot deployment point does not interfere with the existing link and can maximize the signal quality of the target blind spot, a link gain evaluation function can also be introduced. The link gain evaluation function uses the channel gain prediction value from the blind spot candidate point to the blind spot centroid, the propagation path occlusion risk and the enhanced link intersection as the main input indicators, and assigns a weight score to each candidate point. The item with the highest score in the candidate point set is selected as the final blind spot deployment point. Of course, an improved particle swarm algorithm or simulated annealing strategy can also be used for global search to improve search efficiency and deployment accuracy. This example embodiment does not make special restrictions on this.

[0101] The preset blind spot filling height refers to the specific height range above the current residence height of the enhanced UAV, where the blind spot filling UAV should perform hovering operations. The height difference can generally be set to 10 to 30 meters. The purpose of setting the preset blind spot filling height is to expand the signal cone angle when looking down, improve the visual coverage range of each enhanced UAV in the signal blind area, and avoid co-frequency resonance or reverse reflection link disturbance with the enhanced node signal interference path. The preset blind spot filling height can be dynamically adjusted according to the terrain undulation amplitude, target coverage radius and airspace restrictions, and can also be customized in combination with the simulation modeling optimization algorithm. This embodiment does not specifically limit the preset blind spot filling height.

[0102] The blind spot deployment points determined by the above deployment logic have the characteristics of clear structure, low interference and high gain, which can enable the deployed blind spot drone to achieve directional enhancement compensation for the signal blind spot when facing only a single enhancement node, ensuring that the ground GNSS receiving equipment in the area covered by the blind spot receives the enhanced signal that meets the positioning calculation requirements, thereby ensuring the monitoring continuity and signal integrity of key parts of the dam slope area.

[0103] In an exemplary embodiment of the present disclosure, when there are two or more enhanced drones within the signal coverage space formed by the signal enhancement coverage radius of the blind spot drone, Figure 4 Determine the blind spot deployment points according to the steps in Figure 4 As shown, it may specifically include: Step S410: Determine the offset ratio for each of the enhanced UAVs according to the signal-to-noise ratio of the GNSS satellite signals received by the enhanced UAVs. Among them, the smaller the signal-to-noise ratio of the enhanced UAV, the larger the offset ratio, and the closer the blind spot compensation deployment point will be to the enhanced UAV. Step S420: Determine multiple candidate deployment points such that the current hovering and staying positions of each of the enhanced UAVs are within the signal enhancement coverage radius of the blind spot compensation UAV. Step S430: Screen at least one blind spot compensation deployment point from the multiple candidate deployment points with the offset ratio and the preset blind spot compensation height as limiting conditions.

[0104] Among them, the offset ratio is a relative proportional factor indicating the degree of spatial offset in the direction of each enhanced UAV from the geometric center of the blind spot compensation UAV deployment point. The calculation of the offset ratio can be achieved by using a normalized inverse proportional function or a fuzzy weighting strategy. For example, the offset ratio can be determined through the following relational expression: ; Among them, can represent the offset ratio of the x-th enhanced UAV, can represent the signal-to-noise ratio measured by the x-th enhanced UAV, can represent the signal-to-noise ratio measured by the y-th enhanced UAV, can represent the total number of enhanced UAVs within the signal coverage space range.

[0105] After obtaining the offset ratio, multiple candidate blind spot compensation deployment points can be determined in the candidate deployment area. Each candidate blind spot compensation deployment point satisfies the following conditions: the signal enhancement coverage radius can simultaneously cover the hovering and staying positions of all target enhanced UAVs, and has good line-of-sight in the air propagation path and tolerance to link interference.

[0106] The optimal blind spot compensation deployment point can be screened from the set of candidate deployment points with the offset ratio and the preset blind spot compensation height as joint limiting conditions. The preset blind spot compensation height refers to the minimum vertical height condition that the blind spot compensation UAV needs to meet when covering multiple enhanced nodes, usually 10 to 30 meters higher than any target enhanced UAV, to ensure that its signal path can perform signal enhancement coverage on multiple enhanced network nodes below from a higher angle, avoiding terrain occlusion, mutual interference, and path interference overlap.

[0107] After completing the screening of the blind spot filling deployment points, the blind spot filling drones in the second drone cluster can be scheduled to fly to their respective corresponding blind spot filling deployment points along the optimal path, and hover and stay at the specified blind spot filling altitude to perform signal reception, enhancement, and directional forwarding tasks. Through this deployment method, not only can the air blind spots between multiple enhancement drones be repaired, but also the dynamic compensation ability for signal fluctuation regions under complex link structures can be enhanced through the offset scheduling strategy guided by signal strength differences, improving the robustness and spatial redundancy regulation level of the overall GNSS signal enhancement network in high-complexity slope regions.

[0108] In an exemplary embodiment of the present disclosure, the drone flight platform structures of the enhancement drones or the blind spot filling drones can be the same and may include: a flight control module for controlling the drone to fly to the signal enhancement area or the deployment position corresponding to the signal blind spot and stably hover and stay at the set hover 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 the GNSS signal receivers on the ground through a relay transmitting antenna; a signal quality perception module for real-time detecting the signal-to-noise ratio, the number of visible satellites, or the multipath interference characteristics of the GNSS signals received by the drone and sending the signal quality data to the ground control center; a communication module for communication interaction between the drone and other drone nodes and between the drone and the ground control system to realize the collaborative construction and topology optimization of the signal enhancement network or the signal blind spot filling network; and an energy management module for managing the power supply system of the drone and sending a replacement request to the ground control center or enabling a standby drone node to fill in when the power supply level is lower than a preset power threshold.

[0109] Among them, the flight control module is the core control unit for performing trajectory navigation, fixed-point hovering, and attitude stabilization. Its main function is to realize the full-process control of the drone from takeoff to hovering according to the flight task instructions issued by the ground control center, in combination with the autonomous navigation system and the flight state feedback. The flight control module can be composed of a flight control main control chip, an attitude calculation unit, a navigation fusion unit, a GPS receiving module, a gyroscope, an accelerometer, a barometer, and a magnetometer, etc.

[0110] The GNSS signal receiving module refers to a high-sensitivity RF receiving unit configured on a flight platform for receiving multi-source GNSS satellite signals. The GNSS signal receiving module supports at least multi-system compatible reception in frequency bands such as L1, L2 or B1, B2, including but not limited to systems such as GPS, GLONASS, Galileo and Beidou. The GNSS signal receiving module may include an antenna receiving unit, a RF 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 capabilities, and is optionally configured with an RTK solution engine to improve positioning accuracy and signal acquisition capabilities in high-occlusion scenarios.

[0111] The signal enhancement and relay module refers to a dedicated signal processing unit deployed on an unmanned aerial vehicle, with GNSS signal enhancement, filtering, power amplification and directional retransmission capabilities. The module includes a receiving channel controller, a programmable gain amplifier (PGA), a band-pass filter, a transmit modulator, a power amplifier and a directional transmitting antenna system. During operation, the signal enhancement and relay module performs low-noise amplification and intermediate-frequency filtering on the received GNSS signals, removes background interference and spurious frequency components, and then can introduce the signals into the transmit path, and performs beam control through a high-gain directional antenna to achieve directional compensation projection for the enhanced unmanned aerial vehicle or GNSS signal receiver below.

[0112] The signal quality perception module refers to a real-time signal monitoring device deployed on a flight platform for detecting the strength, integrity and noise level of the currently received GNSS signals. The signal quality perception module can be composed of a spectrum collector, an analog-to-digital converter, a digital signal processor and a parameter evaluation unit, and can extract key indicators such as the SNR, carrier-to-noise ratio, number of visible satellites, channel utilization rate, multi-path interference characteristics of the signals. In implementation, the signal quality perception module can periodically send the detection results to the ground control center as the decision basis for subsequent blind spot compensation judgment, path adjustment and network topology optimization. To adapt to the changes in the slope occlusion environment, the signal quality perception module can also support high-frequency refresh and dynamic window detection strategies to identify short-term occlusion events and path occlusion trends.

[0113] The communication module refers to the communication subsystem used to achieve information interaction between the flight platform and the ground control system, as well as between other UAV nodes. The communication module usually includes a wireless communication interface, a data link manager, an encryption 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 building a Mesh ad-hoc network structure between the enhanced UAV and the blind-compensating UAV, facilitating the dynamic coordination of signal enhancement links and the automatic replacement of faulty nodes. At the same time, the communication module can also be responsible for the task of real-time transmitting the node position, energy consumption status, and signal quality indicators to the ground dispatching center to ensure the global controllability and dispatching response efficiency of the signal enhancement network.

[0114] The energy management module refers to the power supply scheduling and monitoring system integrated on the flight platform, which is used to monitor the overall energy supply of the UAV in real-time and adjust the load distribution. The energy management module can include a power detector, an energy consumption calculation unit, a power supply 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 predicts the remaining available working duration in combination with the flight time estimation and the current mission intensity. When it detects that the power supply is lower than the preset threshold (e.g., 20%), the energy management module can actively send a low-power alarm signal to the ground control system, and at the same time trigger the takeoff of the backup UAV or guide the current node to return to the recovery point. In addition, the energy management module can also be configured with a solar panel auxiliary charging unit or a wireless energy receiving device to extend the in-air residence time and improve the sustainability and autonomy of the signal enhancement system under long-term monitoring tasks.

[0115] By integrating the above modules into the enhanced UAV or the blind-compensating UAV in a coordinated manner, such aircraft not only have the ability of independent flight and stable hovering, but also can complete tasks such as GNSS signal enhancement, network reconstruction, and channel self-recovery in complex occlusion environments, thereby significantly improving the availability, stability, and spatial distribution balance of GNSS signals in the dam slope area, and ensuring the high-precision and full-coverage positioning requirements of the underlying GNSS monitoring terminals.

[0116] Figure 5 Fig. shows a schematic diagram of an application scenario of GNSS signal enhancement based on UAV signal relaying in an embodiment of the present disclosure.

[0117] Reference Figure 5As shown in the figure, in the dam slope area 501, by setting multiple GNSS signal receivers 502, the slope deformation in the dam slope area 501 is monitored, so as to realize the geological disaster monitoring in the dam slope area 501. The GNSS signal receiver 502 can receive the GNSS satellite signals transmitted 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 signals, so as to generate the deformation trend monitoring results.

[0118] However, limited by the complex terrain environment in the dam slope area 501, especially in areas such as canyons, steep slopes or severely blocked areas, the attenuation problem of GNSS satellite signals is relatively serious, and even signal blind spots appear, affecting the accuracy and continuity of the acquisition of positioning data. Through the GNSS signal enhancement method based on UAV signal relay in the embodiments of the present disclosure, multiple enhancement UAVs 504 in the first UAV cluster can be deployed to form a signal enhancement network 505, so that after enhancing the GNSS satellite signals transmitted by the GNSS satellite system 503 through the signal enhancement network 505, the enhanced GNSS satellite signals are relayed to the GNSS signal receiver 502, thereby effectively improving the quality of the GNSS satellite signals received by the GNSS signal receiver 502 and ensuring the accuracy and stability of the deformation trend monitoring results.

[0119] For the enhancement UAVs 504 in the signal enhancement network 505, due to their different positions in the dam slope area 501, the GNSS satellite signals received by some enhancement UAVs 504 are still very weak, and they cannot complete the enhancement and relay of the GNSS satellite signals, or the signal quality of the enhanced GNSS satellite signals cannot meet the requirements. At this time, multiple blind spot compensation UAVs 506 in the second UAV cluster can be deployed to form a signal blind spot compensation network 507, so as to realize the preliminary enhancement of the GNSS satellite signals in a higher space, and transmit the preliminarily enhanced GNSS satellite signals to some enhancement UAVs 504 in the signal blind spots selected in the signal enhancement network 505 for further enhancement, and then transmit them to the GNSS signal receiver 502. Through the multi-layer signal enhancement and relay network, the availability, stability and spatial distribution balance of GNSS satellite signals can be improved in the dam slope area 501 with complex terrain environment, and the high-precision and full-coverage positioning requirements of the underlying GNSS signal receiver 502 can be guaranteed.

[0120] Figure 6 The figure shows a schematic diagram of another application scenario of GNSS signal enhancement based on UAV signal relay in the embodiments of the present disclosure.

[0121] Reference Figure 6As shown, in the embodiments of the present disclosure, a two-layer signal enhancement and relay network is proposed. However, those skilled in the art can understand that in some application scenarios, the enhancement drones 504 in the signal enhancement network 505 can directly complete the signal enhancement and relay tasks, without the need for a signal blind spot compensation network composed of multiple blind spot compensation drones in the second drone cluster. Only the multiple enhancement drones 504 in the first drone cluster can form the signal enhancement network 505 to complete the signal enhancement and relay tasks, which is also within the protection scope of the embodiments of the present disclosure.

[0122] In the embodiments of the present disclosure, a GNSS signal enhancement device based on drone signal relay is also provided. Refer to Figure 7 As shown, the GNSS signal enhancement device 700 based on drone signal relay may include a region determination module 710, a signal enhancement network construction module 720, a signal blind spot screening module 730, a signal blind spot compensation network construction module 740, and a signal enhancement module 750, where: The region determination module 710 is configured to obtain GNSS signal feature data and terrain feature data corresponding to a pre-collected dam slope region, and determine a plurality of signal regions to be enhanced according to the GNSS signal feature data and the terrain feature data; The signal enhancement network construction module 720 is configured to dispatch the first drone cluster to hover and stay at a preset hovering height in each of the signal regions to be enhanced, forming a signal enhancement network, and each enhancement drone in the first drone cluster forms a network node of the signal enhancement network; The signal blind spot screening module 730 is configured to determine 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, the signal region to be enhanced corresponding to the target network node is determined as a signal blind spot; The signal blind spot compensation network construction module 740 is configured to dispatch the second drone cluster to a dynamic blind spot compensation position corresponding to the signal blind spot, forming a signal blind spot compensation network, and each blind spot compensation drone in the second drone cluster forms a network node of the signal blind spot compensation network, and the dynamic blind spot compensation position is determined based on the distribution of the enhancement drones in each of the signal blind spots updated in real time; The signal enhancement module 750 is configured to enhance the received GNSS satellite signals based on the constructed signal enhancement network and the signal blind spot compensation network, and direct and transmit them to GNSS signal receivers arranged at each slope deformation monitoring point in the dam slope region, so as to realize signal enhancement of the GNSS signal receivers.

[0123] In an exemplary embodiment of the present disclosure, the region determination module 710 is configured as: Divide the dam slope area according to the signal coverage parameters of the enhanced unmanned aerial vehicle to determine multiple monitoring sub-areas; Determine the GNSS signal strength of each of the monitoring sub-areas according to the GNSS signal characteristic data, and determine the terrain occlusion degree of each of the monitoring sub-areas according to the terrain characteristic data; Determine the signal quality scores of each of the monitoring sub-areas based on the GNSS signal strength and the terrain occlusion degree; Mark the monitoring sub-areas with signal quality scores less than or equal to a preset score threshold as the signal to be enhanced areas, and obtain multiple signal to be enhanced areas.

[0124] In an exemplary embodiment of the present disclosure, the area determination module 710 is configured to: Determine the signal enhancement coverage radius of the enhanced unmanned aerial vehicle, and determine the signal coverage area on the dam slope area according to the preset hovering height and the signal enhancement coverage radius; Based on the stored historical signal quality distribution data, determine multiple weak signal distribution aggregation points in the dam slope area, where each of the weak signal distribution aggregation points corresponds to a GNSS signal receiver; Aim at covering all the weak signal distribution aggregation points with the fewest signal coverage areas, determine target signal coverage areas at multiple different positions, and use the target signal coverage areas as the monitoring sub-areas.

[0125] In an exemplary embodiment of the present disclosure, the area determination module 710 is configured to: Fuse and merge the target signal coverage areas with the interval distance between the center points less than or equal to the preset interval distance to obtain multiple monitoring sub-areas.

[0126] In an exemplary embodiment of the present disclosure, the signal enhancement network construction module 720 is configured to: Taking the preset hovering height as a reference, perform spatial grid division in the upper space of the signal to be enhanced area, and determine the signal transmission stability degree of the enhanced unmanned aerial vehicle at each spatial grid according to the unmanned aerial vehicle parameters of the enhanced unmanned aerial vehicle; Screen at least two hovering stop points according to the signal transmission stability degree; Adjust the hovering stop points of each of the enhanced unmanned aerial vehicles in the corresponding signal to be enhanced area in real time according to the received GNSS signal quality fed back by each of the enhanced unmanned aerial vehicles in the first unmanned aerial vehicle cluster, so as to ensure the enhanced GNSS satellite signal quality received by all GNSS signal receivers within the coverage range of the signal enhancement network.

[0127] In an exemplary embodiment of the present disclosure, the signal blind spot compensation network construction module 740 is configured to: Determine blind spot compensation deployment points according to the current hovering positions of the enhanced drones in the first drone cluster corresponding to each signal blind spot, in combination with the signal enhancement coverage radius of the blind spot compensation drones and a preset blind spot compensation height; Dispatch each blind spot compensation drone in the second drone cluster to fly to the blind spot compensation deployment points, and hover and stay at a preset blind spot compensation height higher than the corresponding enhanced drone to construct the signal blind spot compensation network.

[0128] In an exemplary embodiment of the present disclosure, when there is only one enhanced drone within the signal coverage space formed by the signal enhancement coverage radius of the blind spot compensation drones, the signal blind spot compensation network construction module 740 is configured to: Determine multiple candidate deployment points that enable the current hovering position of the enhanced drone corresponding to the signal blind spot to be within the signal enhancement coverage radius of the blind spot compensation drone; Screen at least one blind spot compensation deployment point from the multiple candidate deployment points with the preset blind spot compensation height as a limiting condition.

[0129] In an exemplary embodiment of the present disclosure, when there are two or more enhanced drones within the signal coverage space formed by the signal enhancement coverage radius of the blind spot compensation drones, the signal blind spot compensation network construction module 740 is configured to: Determine the offset ratio for each enhanced drone according to the signal-to-noise ratio of the GNSS satellite signals received by the enhanced drone, where the smaller the signal-to-noise ratio of the enhanced drone, the larger the offset ratio, and the closer the blind spot compensation deployment point is to the enhanced drone; Determine multiple candidate deployment points that enable the current hovering positions of all the enhanced drones to be within the signal enhancement coverage radius of the blind spot compensation drone; Screen at least one blind spot compensation deployment point from the multiple candidate deployment points with the offset ratio and the preset blind spot compensation height as limiting conditions.

[0130] In an exemplary embodiment of the present disclosure, the enhanced drone or the blind spot compensation drone includes: A flight control module for controlling the drone to fly to the signal area to be enhanced or the deployment position corresponding to the signal blind spot, and stably hovering and staying at a set hovering height; 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, is configured to amplify and filter the received GNSS satellite signals, and directionally transmit the enhanced GNSS satellite signals to the GNSS signal receivers on the ground through a relay transmitting antenna; A signal quality perception module is used to detect in real time the signal-to-noise ratio, the number of visible satellites or the multipath interference characteristics of the GNSS signals received by the drone, and send the signal quality data to the ground control center; A communication module is used for communication interaction between the drone and other drone nodes and between the drone and the ground control system, so as to realize the collaborative construction and topology optimization of a signal enhancement network or a signal blind spot compensation network; An energy management module is used to manage the power supply system of the drone, and send a replacement request to the ground control center or enable a standby drone node to take its place when the power supply of the power supply system is lower than a preset power threshold. The specific details of each module of the above GNSS signal enhancement device based on drone signal relay have been described in detail in the corresponding GNSS signal enhancement method based on drone signal relay, so they will not be elaborated here.

Claims

1. A GNSS signal enhancement method based on UAV signal relaying, characterized in that Including: Obtain GNSS signal feature data and terrain feature data corresponding to a pre-acquired dam slope area, and determine multiple signal enhancement areas based on the GNSS signal feature data and the terrain feature data; Dispatch a first UAV cluster to hover and stay at a preset hovering height in each of the signal enhancement areas to form a signal enhancement network, and each enhancement UAV in the first UAV cluster constitutes a network node of the signal enhancement network; Determine 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 a target network node is less than or equal to a preset signal-to-noise ratio threshold, determine the signal enhancement area corresponding to the target network node as a signal blind area; Dispatch a second UAV cluster to a dynamic blind area compensation position corresponding to the signal blind area to form a signal blind area compensation network, and each blind area compensation UAV in the second UAV cluster constitutes a network node of the signal blind area compensation network. The dynamic blind area compensation position is determined based on the distribution of the enhancement UAVs in each of the signal blind areas updated in real time; Based on the constructed signal enhancement network and the signal blind area compensation network, enhance the received GNSS satellite signals and transmit them directionally 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.

2. The GNSS signal enhancement method according to claim 1, characterized in that The determining multiple signal enhancement areas based on the GNSS signal feature data and the terrain feature data includes: Divide the dam slope area according to the signal coverage parameters of the enhancement UAVs to determine multiple monitoring sub-areas; Determine the GNSS signal strength of each monitoring sub-area according to the GNSS signal feature data, and determine the terrain occlusion degree of each monitoring sub-area according to the terrain feature data; Determine the signal quality score of each monitoring sub-area through the GNSS signal strength and the terrain occlusion degree; Mark the monitoring sub-areas with a signal quality score less than or equal to a preset score threshold as the signal enhancement areas to obtain multiple signal enhancement areas.

3. The GNSS signal enhancement method according to claim 2, wherein The dividing the dam slope area according to the signal coverage parameters of the enhancement UAVs to determine multiple monitoring sub-areas includes: Determine the signal enhancement coverage radius of the enhancement UAV, and determine the signal coverage area on the dam slope area according to the preset hovering height and the signal enhancement coverage radius; Determine multiple weak signal distribution aggregation points 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; Aim at covering all the weak signal distribution aggregation points with the fewest signal coverage areas, determine target signal coverage areas at multiple different positions, and use the target signal coverage areas as the monitoring sub-areas.

4. The GNSS signal enhancement method according to claim 3, wherein The determining target signal coverage areas at multiple different positions and using the target signal coverage areas as the monitoring sub-areas includes: Fuse and merge the target signal coverage areas where the interval distance between the center points is less than or equal to the preset interval distance to obtain multiple monitoring sub-areas.

5. The GNSS signal enhancement method according to claim 1, wherein Scheduling the first UAV cluster to hover and stay at the preset hovering height in each of the signal areas to be enhanced to form a signal enhancement network, including: Taking the preset hovering height as a reference, performing spatial grid division in the upper space of the signal area to be enhanced, and determining the signal transmission stability degree of the enhanced UAV at each spatial grid according to the UAV parameters of the enhanced UAV; Selecting at least two hovering and staying points according to the signal transmission stability degree; Adjusting the hovering and staying points of each enhanced UAV in the corresponding signal area to be enhanced in real time according to the GNSS signal quality feedback received by each enhanced UAV in the first UAV cluster, so as to ensure the quality of the enhanced GNSS satellite signals 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, wherein Scheduling the second UAV cluster to the dynamic blind spot compensation position corresponding to the signal blind area to form a signal blind spot compensation network, including: Determining the blind spot compensation deployment points according to the current hovering and staying positions of the enhanced UAVs in the first UAV cluster corresponding to each signal blind area, and combining the signal enhancement coverage radius of the blind spot compensation UAVs and the preset blind spot compensation height; Scheduling each blind spot compensation UAV in the second UAV cluster to fly to the blind spot compensation deployment points and hover and stay at a preset blind spot compensation height higher than the corresponding enhanced UAV to construct the signal blind spot compensation network.

7. The GNSS signal enhancement method according to claim 6, wherein When there is only one enhanced UAV within the signal coverage space formed by the signal enhancement coverage radius of the blind spot compensation UAV, the determination of the blind spot compensation deployment points includes: Determining multiple candidate deployment points that enable the current hovering and staying position of the enhanced UAV corresponding to the signal blind area to be within the signal enhancement coverage radius of the blind spot compensation UAV; Selecting at least one blind spot compensation deployment point from the multiple candidate deployment points with the preset blind spot compensation height as a limiting condition.

8. The GNSS signal enhancement method according to claim 6, characterized in that, When there are two or more enhanced UAVs within the signal coverage space formed by the signal enhancement coverage radius of the blind spot compensation UAV, the determination of the blind spot compensation deployment points includes: Determining the offset ratio for each enhanced UAV according to the signal-to-noise ratio of the GNSS satellite signals received by the enhanced UAV, where the smaller the signal-to-noise ratio of the enhanced UAV, the larger the offset ratio, and the closer the blind spot compensation deployment point is to the enhanced UAV; Determining multiple candidate deployment points that enable the current hovering and staying positions of all the enhanced UAVs to be within the signal enhancement coverage radius of the blind spot compensation UAV; Selecting at least one blind spot compensation deployment point from the multiple candidate deployment points with the offset ratio and the preset blind spot compensation height as limiting conditions.

9. The GNSS signal enhancement method according to claim 1, wherein The enhanced UAV or the blind spot compensation UAV includes: A flight control module for controlling the UAV to fly to the deployment position corresponding to the signal area to be enhanced or the signal blind area and stably hovering and staying at the set hovering height; 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, is configured to amplify and filter the received GNSS satellite signals, and directionally transmit the enhanced GNSS satellite signals to the GNSS signal receivers on the ground through a relay transmitting antenna; A signal quality perception module is used to detect in real time the signal-to-noise ratio, the number of visible satellites or the multipath interference characteristics of the GNSS signals received by the drone, and send the signal quality data to the ground control center; A communication module is used for communication interaction between the drone and other drone nodes and between the drone and the ground control system, so as to realize the collaborative construction and topology optimization of a signal enhancement network or a signal blind spot compensation network; An energy management module is used to manage the power supply system of the drone, and send a replacement request to the ground control center or enable a standby drone node to take its place when the power supply of the power supply system is lower than a preset power threshold; 10. A GNSS signal enhancement device based on UAV signal relay, characterized in that, It includes: A region determination module is used to obtain the GNSS signal feature data and terrain feature data corresponding to the pre-acquired dam slope region, and determine a plurality of signal regions to be enhanced according to the GNSS signal feature data and the terrain feature data; A signal enhancement network construction module is used to dispatch a first drone cluster to hover and stay at a preset hovering height in each of the signal regions to be enhanced, forming a signal enhancement network, and each enhancement drone in the first drone cluster constitutes a network node of the signal enhancement network; A signal blind spot screening module is used to determine 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 a target network node is less than or equal to a preset signal-to-noise ratio threshold, the signal region to be enhanced corresponding to the target network node is determined as a signal blind spot; A signal blind spot compensation network construction module is used to dispatch a second drone cluster to a dynamic blind spot compensation position corresponding to the signal blind spot, forming a signal blind spot compensation network. Each blind spot compensation drone in the second drone cluster constitutes a network node of the signal blind spot compensation network, and the dynamic blind spot compensation position is determined based on the distribution of the enhancement drones in each signal blind spot updated in real time; A signal enhancement module is used to enhance the received GNSS satellite signals based on the constructed signal enhancement network and the signal blind spot compensation network, and directionally transmit them to the GNSS signal receivers arranged at each slope deformation monitoring point in the dam slope region, so as to realize signal enhancement for the GNSS signal receivers.

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