Unmanned aerial vehicle cooperative complex terrain monitoring system

Abstract

The invention relates to the technical field of unmanned aerial vehicle cooperative monitoring and environmental perception, discloses an unmanned aerial vehicle cooperative complex terrain monitoring system, and aims to solve the problems that a single unmanned aerial vehicle is insufficient in complex terrain coverage and poor in terrain adaptability, and monitoring data are easily interfered. The system comprises a master unmanned aerial vehicle unit, a slave unmanned aerial vehicle cluster and terrain adaptation module, a mounting adaptive adjustment module, an obstacle avoidance unit, a data module and the like. Wherein the main unmanned aerial vehicle plans a path and allocates tasks based on DEM data, the slave unmanned aerial vehicles comprise mountain / land and water adaptive models, stable operation of different terrains is achieved through the terrain adaptive module, the mounting mechanism can dynamically adjust the angle of monitoring equipment, and the obstacle avoidance unit fuses prior and real-time data to achieve accurate obstacle avoidance. The data module can correct the spectrum and fuse multi-source data. The system is suitable for atmosphere, soil and hydrology multi-dimensional cooperative monitoring of complex terrains such as mountainous regions and waterfront, the monitoring range, the data precision and the operation safety can be effectively improved, and meanwhile the real-time performance of synchronous traceability is remarkably enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle cooperative monitoring and environmental perception, and particularly relates to a complex terrain monitoring system based on unmanned aerial vehicle cooperation. BACKGROUND

[0002] The environmental monitoring of complex terrain areas (such as mountain slopes, lake and river waterfront areas) is an important part of ecological assessment and resource exploration. However, the current single unmanned aerial vehicle monitoring mode has significant limitations: the endurance and load capacity of a single unmanned aerial vehicle are limited, making it difficult to achieve full coverage of complex terrain, and different terrains (such as steep slopes and shallow water areas) have different requirements for the terrain adaptability of unmanned aerial vehicles. A single model cannot meet the stable operation requirements of multiple scenarios. At the same time, the mounting position and angle of the monitoring equipment are usually fixed, which is easily affected by airflow disturbance and terrain obstruction in complex terrain, resulting in a decrease in the accuracy and effectiveness of the monitoring data.

[0003] Existing multi-unmanned aerial vehicle cooperative monitoring schemes mainly focus on path planning for simple terrain, lack a "terrain-model-task" adaptation mechanism for complex terrain, and do not integrate and optimize multi-source monitoring equipment (such as spectral monitoring instruments and hydrological sensors) for anti-interference: on the one hand, unmanned aerial vehicle obstacle avoidance only relies on real-time sensing data without the fusion of terrain prior information, which is prone to collision in complex terrain due to sensing delay; on the other hand, spectral monitoring data is easily disturbed by atmospheric extinction and terrain obstruction, and existing schemes lack targeted correction algorithms, resulting in insufficient accuracy of atmospheric and soil spectral monitoring results, which cannot meet the demand for high-precision environmental assessment. SUMMARY

[0004] Therefore, the present application aims to provide a complex terrain monitoring system based on unmanned aerial vehicle cooperation, which realizes real-time and accurate interaction between weak electric system physical entities and virtual models, and improves the intelligent level and operation and maintenance efficiency of the system.

[0005] The technical scheme of the embodiment of the present application is as follows: A complex terrain monitoring system based on unmanned aerial vehicle cooperation, comprising a master unmanned aerial vehicle unit, a slave unmanned aerial vehicle cluster, a terrain adaptation module, a mounting adaptive adjustment mechanism, a multi-source monitoring unit, an obstacle avoidance fusion unit and a data processing module; The master unmanned aerial vehicle unit is equipped with a central control module and a global communication unit, and is used for performing global path planning, slave unmanned aerial vehicle task allocation and data summarization; The slave unmanned aerial vehicle cluster is in communication connection with the master unmanned aerial vehicle unit, and is used for performing regional monitoring tasks; The terrain adaptation module is configured in the slave unmanned aerial vehicle cluster, and is used for adapting stable operation in different terrains; The mounting adaptive adjustment mechanism is arranged on the main unmanned aerial vehicle unit and the unmanned aerial vehicle cluster, and is used for dynamically adjusting the position and angle of the monitoring device. The multi-source monitoring unit is arranged on the main unmanned aerial vehicle unit and the unmanned aerial vehicle cluster, and is used for collecting multi-dimensional monitoring data. The obstacle avoidance fusion unit is in communication connection with the main unmanned aerial vehicle unit, and is used for fusing the terrain information and real-time sensing data to realize obstacle avoidance control. The data processing module is in communication connection with the main unmanned aerial vehicle unit, and is used for anti-interference processing and fusion output of the monitoring data.

[0006] Preferably, the unmanned aerial vehicle cluster includes mountain-adapted unmanned aerial vehicles and water-land-adapted unmanned aerial vehicles; the mountain-adapted unmanned aerial vehicles correspond to slope terrain monitoring tasks, and the water-land-adapted unmanned aerial vehicles correspond to waterfront terrain monitoring tasks.

[0007] Preferably, the terrain adaptation module includes a vacuum suction disc assembly and a pressure sensing assembly arranged on the mountain-adapted unmanned aerial vehicle, and a folding inflatable float assembly arranged on the water-land-adapted unmanned aerial vehicle; the vacuum suction disc assembly adjusts the adsorption force through the detection data of the pressure sensing assembly, and the folding inflatable float assembly can be pre-started to adapt to water operation.

[0008] Preferably, the mounting adaptive adjustment mechanism includes a motorized gimbal, an angle sensor and a control sub-module; the angle sensor is used for collecting real-time air flow velocity components, and the control sub-module calculates the mounting angle based on the real-time air flow velocity components and the field of view angle requirement of the monitoring device through the formula and controls the motorized gimbal to adjust the position and angle of the monitoring device. wherein, is the air flow component perpendicular to the monitoring field of view, is the field of view angle of the monitoring device, is the distance of the monitoring target.

[0009] Preferably, the obstacle avoidance fusion unit is used for fusing monitoring area digital elevation model (DEM) prior data, real-time laser radar obstacle distance data and terrain texture features of visual sensing ; the main unmanned aerial vehicle unit generates local path correction instructions based on the output data of the obstacle avoidance fusion unit through the formula and issues the instructions to the unmanned aerial vehicle cluster; wherein, is the initial local path, is the path correction amount, is the safety distance threshold.

[0010] ​Preferably, the multi-source monitoring unit comprises a non-contact spectral monitor, a hydrological water sampling-flow rate sensor and a terrain slope sensor. The non-contact spectral monitor is used to collect atmospheric and soil spectral data, the hydrological water sampling-flow rate sensor is used to collect water sample and water flow rate data, and the terrain slope sensor is used to collect terrain slope data.

[0011] Preferably, the data processing module is built-in with a spectral anti-interference correction algorithm and a multi-source data fusion model. The spectral anti-interference correction algorithm is through the formula The original spectral intensity I is corrected to obtain the corrected spectral intensity ; Wherein, k is an atmospheric extinction coefficient, h is a monitoring height, is a solar elevation angle.

[0012] Preferably, the global path planning of the master unmanned aerial vehicle unit is realized through a global path cost function . Wherein, is a weight coefficient, is a terrain slope cost, is an obstacle distance cost, is a monitoring point coverage density cost.

[0013] Preferably, the master unmanned aerial vehicle unit issues instructions to the slave unmanned aerial vehicle cluster according to the terrain type determined by the global path planning; for slope terrain, the vacuum suction cup assembly and the pressure sensing assembly of the mountain adaptive slave unmanned aerial vehicle are activated; for waterfront terrain, the folding inflatable float assembly of the water-land adaptive slave unmanned aerial vehicle is activated.

[0014] Preferably, the operation division of the multi-source monitoring unit is that the non-contact spectral monitor of the master unmanned aerial vehicle unit collects atmospheric spectral data, the non-contact spectral monitor of the mountain adaptive slave unmanned aerial vehicle is started synchronously to collect soil spectral data after auxiliary light source calibration, and the hydrological water sampling-flow rate sensor of the water-land adaptive slave unmanned aerial vehicle collects water sample and water flow rate data.

[0015] The embodiment of the application has the following advantages due to the adoption of the above technical solutions: I. The problem of insufficient coverage of a single unmanned aerial vehicle is solved: through master-slave hierarchical cooperation and terrain adaptive models, global coverage of complex terrain is realized, and the integrity of the monitoring range is improved. II. The problem of influence of mounting position on data accuracy is solved: the mounting self-adaptive adjustment mechanism dynamically optimizes the angle of the monitoring equipment through airflow-field of view linkage algorithm, and reduces the influence of external interference on data. Third, it solves the problem of high collision risk in complex terrain: the obstacle avoidance strategy that integrates DEM prior and real-time sensing improves the safety of UAV operations in complex terrain. Fourth, it solves the problem of limited adaptability of a single model to various scenarios: the modular design of the drone enables stable operation in diverse terrains; Fifth, it solves the problem of interference with spectral monitoring data: the terrain occlusion correction algorithm effectively reduces the impact of atmospheric extinction and terrain occlusion on spectral data, thus improving monitoring accuracy; VI. Solves the problem of cumbersome switching between multiple tasks: The multi-source monitoring unit is integrated into the master and slave UAVs to achieve synchronous collection of atmospheric, soil and hydrological data, greatly improving monitoring efficiency.

[0016] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a diagram of the overall system architecture of the present invention; Figure 2 This is a flowchart illustrating the core workflow of the present invention; Figure 3 This is a diagram showing the association of the core modules of the present invention; Figure 4 This is a diagram showing the division of labor of the multi-source monitoring unit in this invention. Detailed Implementation

[0019] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0020] It should be noted that the terms "first", "second", "symmetric", "array" and the like are only used for distinguishing description and position description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "symmetric" and the like can be explicitly or implicitly included one or more of the features; similarly, for some features that are not limited in number by the words "two", "three" and the like, it should be noted that the features also belong to explicitly or implicitly include one or more feature quantities. In the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "fixing" and the like should be understood in a broad sense; for example, it can be fixedly connected, or it can be detachably connected, or it can be integrally formed; it can be mechanically connected, it can be directly connected, it can be welded, it can be indirectly connected through an intermediate medium, or it can be the communication or interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specification and drawings in combination with specific circumstances.

[0021] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0022] As shown in Figures 1-4 The present application provides a complex terrain monitoring system cooperated with unmanned aerial vehicles, comprising a master unmanned aerial vehicle unit, a slave unmanned aerial vehicle cluster, a terrain adaptation module, a mounting self-adaptive adjustment mechanism, a multi-source monitoring unit, an obstacle avoidance fusion unit and a data processing module. The master unmanned aerial vehicle unit is equipped with a central control module and a global communication unit, which is used for performing global path planning, slave unmanned aerial vehicle task allocation and data summarization. The central control module specifically includes a path planning algorithm submodule and a task scheduling submodule, which can automatically divide a plurality of monitoring sub-regions according to the terrain characteristics of the monitoring region. The global communication unit adopts a multi-band anti-interference communication protocol, which can maintain stable signal transmission in complex terrain environments such as valleys and dense forests with weak signals. The advantage of this design is that it can realize fine allocation of monitoring tasks, avoid single-region task overload, and effectively improve the communication reliability between the master and slave unmanned aerial vehicles, reduce the risk of data transmission interruption. The slave unmanned aerial vehicle cluster is in communication connection with the master unmanned aerial vehicle unit, and is used for performing sub-regional monitoring tasks. The number of the drone cluster can be flexibly adjusted according to actual monitoring requirements, and each cluster supports 2-5 drones to work cooperatively, each drone is equipped with an independent local communication subunit, which can realize real-time information interaction with the master drone and other drones in the cluster. The advantage of this setting is that it can cover a wider and more complex terrain area, and through multi-drone information interaction, it can complete dynamic position compensation, avoid monitoring blind area caused by single drone failure, and the terrain adaptation module is configured in the drone cluster, which is used for stable operation adaptation to different terrains. The terrain adaptation module adopts a modular and detachable structure, which can quickly replace components according to the specific task type of the drone, and its advantage is to improve the equipment reuse rate of the drone, without the need to configure a dedicated drone for different terrains, thereby reducing the hardware cost of the monitoring system. The mounting self-adaptive adjustment mechanism is configured in the master drone unit and the drone cluster, which is used for dynamically adjusting the position and angle of the monitoring device. The control sub-module of the mounting self-adaptive adjustment mechanism is in real-time linkage with the central control module of the master drone, and can synchronously receive the position change information of the monitoring target. The advantage is that it can realize dynamic and accurate adjustment of the angle of the monitoring device, and avoid invalid monitoring data caused by target movement or change of the attitude of the drone. The multi-source monitoring unit is configured in the master drone unit and the drone cluster, which is used for collecting multi-dimensional monitoring data.

[0023] The data synchronization trigger module is arranged between the sensors of the multi-source monitoring unit, which can ensure that the collection time stamps of different types of monitoring data remain consistent. The advantage is that it provides a unified time reference for the multi-dimensional data fusion of the subsequent data processing module, and improves the accuracy of the fusion result. The obstacle avoidance fusion unit is in communication connection with the master drone unit, which is used for fusing terrain information and real-time sensing data to realize obstacle avoidance control. The data verification sub-module built-in the obstacle avoidance fusion unit can cross-verify the DEM prior data and real-time sensing data. The advantage is that it can filter invalid sensing data and avoid decision-making errors caused by single data error. The data processing module is in communication connection with the master drone unit, which is used for anti-interference processing and fusion output of the monitoring data.

[0024] The data processing module supports edge computing function, which can complete real-time preprocessing of part of the data at the master drone end, without the need to transmit all data to the ground end. The advantage is to reduce the bandwidth pressure of data transmission, improve the output efficiency of monitoring results, and facilitate quick access to preliminary feedback of terrain monitoring. The terrain adaptation module includes a vacuum suction disc assembly and a pressure sensing assembly configured in the mountain adaptation type drone, and a folding inflatable float assembly configured in the water-land adaptation type drone. The vacuum suction disc assembly adjusts the adsorption force through the detection data of the pressure sensing assembly, and the folding inflatable float assembly can be pre-started to adapt to water operation. The vacuum suction cup assembly is made of highly wear-resistant silicone material, and the surface of the suction cup has a microstructure anti-slip texture. The pressure sensing component has a sampling frequency of up to 10Hz, which can provide real-time feedback on the contact pressure value between the suction cup and the slope. This enhances the adsorption stability on rough slope surfaces and reduces the risk of the drone slipping when operating on slopes. It also enables rapid dynamic adjustment of the adsorption force, avoiding damage to slope vegetation due to excessive adsorption force or adsorption failure due to insufficient adsorption force. The inflation process of the foldable inflatable float assembly is triggered by the mission command of the main drone, and the inflation time does not exceed 30 seconds. The buoyancy of the float after it is unfolded can support the drone and the onboard monitoring equipment (total weight ≤ 5kg) to float stably on the water surface. This allows for a quick switch from aerial flight to water surface operation, adapting to the rapid response monitoring needs of waterfront areas.

[0025] The main UAV unit issues commands to the swarm of slave UAVs based on the terrain type determined by the global path planning. For sloping terrain, it activates the vacuum suction cup and pressure sensing components of the mountain-adaptive slave UAVs; for waterfront terrain, it activates the foldable inflatable float components of the water-adaptive slave UAVs.

[0026] Before issuing terrain adaptation commands, the main UAV unit will first confirm the terrain type by using pre-collected data from the terrain slope sensor. This has the advantage of avoiding errors in the activation of adaptation components due to delays in updating prior DEM data, thus improving the accuracy of terrain adaptation. The adaptive adjustment mechanism includes a motorized pan-tilt unit, an angle sensor, and a control submodule. The angle sensor collects real-time airflow velocity components, and the control submodule, based on the real-time airflow velocity components and the field-of-view requirements of the monitoring equipment, uses a formula... Calculate the mounting angle And control the electric pan-tilt unit to adjust the position and angle of the monitoring equipment; in, The airflow component perpendicular to the monitoring field of view. To monitor the field of view of the equipment, To monitor the distance to the target; The angle sensor has a measurement accuracy of up to 0.1°, which can accurately capture subtle changes in the airflow velocity component. The parameters of the formula can be customized according to the model of the monitoring equipment. It has a built-in parameter preset library for commonly used equipment. The advantage is that it provides high-precision basic data for the calculation of the mounting angle, ensuring that the field of view of the monitoring equipment always covers the target area. At the same time, it improves the system's compatibility with different monitoring equipment, eliminating the need to develop separate control algorithms for each type of equipment.

[0027] The obstacle avoidance fusion unit is used to fuse prior data from the digital elevation model (DEM) of the monitoring area and obstacle distance data from real-time lidar. and visual sensing of terrain texture features .

[0028] The master UAV unit generates a local path correction instruction based on the output data of the obstacle avoidance fusion unit, and the formula is The local path correction instruction is sent to the slave UAV cluster; wherein, is the initial local path, is the path correction amount, is the safety distance threshold.

[0029] The DEM prior data can be imported in advance through the on-board storage module of the master UAV, or the latest terrain data can be obtained through the real-time network; the detection distance of the real-time laser radar can reach 50m, and the detection accuracy is ±0.2m; the value range of the path correction amount ΔP can be set according to the maneuverability of the slave UAV, and the default value is 0.5-2m, which has the advantages of adapting to different update frequency of terrain monitoring demand, improving the environmental adaptability of the system; At the same time, the long-distance obstacles in complex terrain are perceived in advance, and sufficient reaction time is reserved for path correction; It can also avoid the loss of control of the slave UAV attitude caused by too large path correction amplitude, and improve the stability of the obstacle avoidance process; The multi-source monitoring unit includes a non-contact spectral monitor, a hydrological water sampling-flow rate sensor, and a terrain slope sensor; The non-contact spectral monitor is used to collect atmospheric and soil spectral data, the hydrological water sampling-flow rate sensor is used to collect water sample and flow rate data, and the terrain slope sensor is used to collect terrain slope data; The spectral detection range of the non-contact spectral monitor covers 200-1000nm, including ultraviolet-visible-near infrared band; The water sampling capacity of the hydrological water sampling-flow rate sensor is 50mL, and the flow rate measurement range is 0.01-5m / s; The measurement range of the terrain slope sensor is 0-90°, and the measurement accuracy is ±0.5°, which has the advantages of collecting more rich atmospheric and soil spectral information, and improving the accuracy of material composition identification; It can obtain enough water samples for subsequent laboratory analysis, and can cover the common flow rate range of waterfront area; It can also accurately capture the slope change of complex terrain, provide data support for path planning and terrain adaptation, and the spectral anti-interference correction algorithm and multi-source data fusion model are built-in in the data processing module; The spectral anti-interference correction algorithm corrects the original spectral intensity I to obtain the corrected spectral intensity ; wherein, k is the atmospheric extinction coefficient, h is the monitoring height, is the solar elevation angle; ​The algorithm can also dynamically correct the atmospheric extinction coefficient k based on the atmospheric humidity data of the monitoring area. This further reduces the interference of atmospheric environmental factors on the spectral data and improves the reliability of the corrected data. The division of labor among the multi-source monitoring units is as follows: the main UAV unit's non-contact spectral monitoring instrument collects atmospheric spectral data; the mountain-adapted unit's non-contact spectral monitoring instrument simultaneously activates auxiliary light source calibration to collect soil spectral data; and the land-water adapted unit collects water samples and water flow velocity data from the UAV's hydrological sampling and flow velocity sensor. The calibration time for the auxiliary light source is no more than 10 seconds, and the error of the spectral data after calibration can be controlled within ±2%. The advantage is that it can quickly complete the preparation work for soil spectral acquisition while ensuring the accuracy of the acquired data. The global path planning of the main UAV unit is achieved through a global path cost function. To achieve; among which, These are the weighting coefficients. As a result of the terrain slope, The cost of obstacle distance, This comes at the cost of monitoring point coverage density.

[0030] The weighting coefficients ω1, ω2, and ω3 can be adjusted according to the priority of the monitoring task. For example, the weight of Coverage can be increased for ecological monitoring tasks, and the weight of Cobstacle can be increased for security inspection tasks. The benefit is that it can achieve task-oriented optimization of path planning and improve the system's adaptability to different monitoring needs.

[0031] In this embodiment, the present invention operates as follows: First, system initialization and global path planning are performed: the main UAV unit imports prior data of the digital elevation model (DEM) of the monitoring area, and then performs global path cost function... The system calculates path costs, divides monitoring sub-areas such as slopes and waterfronts, and issues task commands to the drone swarm. At the same time, the pre-activated components of the terrain adaptation module enter the standby state. The mountain-adaptive type prepares vacuum suction cups and pressure sensing components from the drone, while the water-adaptive type prepares foldable inflatable float components from the drone.

[0032] Subsequently, terrain adaptation and deployment of the drones are carried out: the main drone unit issues activation commands to the corresponding slave drones according to the terrain type of the sub-area. For sloping areas, the mountain-adaptive slave drone activates the vacuum suction cup component, and the pressure sensing component collects the contact pressure at a frequency of 10Hz and adjusts the suction force to complete the stable deployment on the slope. For waterfront areas, the land-water-adaptive slave drone triggers the folding inflatable float component to inflate (inflation time ≤30 seconds), floats on the water surface to complete the hydrological monitoring deployment. At the same time, the adaptive adjustment mechanism of the main and slave drones completes the angle sensor calibration and synchronously monitors the field of view parameters of the equipment.

[0033] Then carry out cooperative monitoring operation and dynamic adjustment: the non-contact spectral monitor of the main unmanned aerial vehicle unit collects atmospheric spectrum data; the spectral monitor of the mountain adaptive slave unmanned aerial vehicle collects soil spectrum data after starting auxiliary light source calibration (calibration time ≤10 seconds); the hydrology water sampling-flow rate sensor of the water-land adaptive slave unmanned aerial vehicle collects water body sample and flow velocity data; the self-adaptive adjustment mechanism mounted collects real-time air flow velocity component through an angle sensor , according to the formula , the mounting angle of the monitoring equipment is adjusted; the obstacle avoidance fusion unit fuses DEM prior data, real-time laser radar distance data and visual texture features , the main unmanned aerial vehicle unit generates local path correction instructions through the formula and issues them to the slave unmanned aerial vehicle cluster to avoid obstacles.

[0034] Finally, data processing and result output are performed: the data processing module calls the spectral anti-interference correction algorithm, corrects the original spectrum intensity I through the formula ; at the same time, atmospheric spectrum, soil spectrum, hydrology data and terrain slope data are fused to generate a multi-dimensional monitoring report of complex terrain; users can adjust the weight coefficient of the global path cost function according to actual needs , restart the cooperative monitoring operation to obtain more accurate target area data.

[0035] The system is suitable for multi-dimensional cooperative monitoring of atmosphere, soil and hydrology in complex terrains such as mountains and waterfronts, and effectively improves the monitoring range, data accuracy, operation safety and real-time of synchronous traceability.

[0036] The following are several specific embodiments of the application: Embodiment one: ecological monitoring scene of mountain complex terrain First, system initialization and forest path planning are performed: the main unmanned aerial vehicle unit imports DEM data and vegetation height distribution prior data of the monitoring area, optimizes the global path cost function as (add as vegetation density weight, as vegetation sheltering cost), divides "crown layer atmospheric monitoring area, understory soil monitoring area"; at the same time, 2 small folding mountain adaptive slave unmanned aerial vehicles are deployed, and the miniature vegetation avoidance mechanical arm assembly is pre-started.

[0037] Subsequently, the dense forest terrain adaptive deployment is carried out: the master unmanned aerial vehicle issues instructions to the forest operation slave unmanned aerial vehicle, activates the composite terrain adaptive module of the vacuum suction cup assembly + micro mechanical arm, the mechanical arm first pushes away the low shrubs, the vacuum suction cup assembly adjusts the adsorption force through the pressure sensing assembly, and the stable deployment is completed by adhering to the tree trunk / slope; the tree crown layer operation slave unmanned aerial vehicle keeps hovering, and the mounting angle is pre-adjusted to avoid the tree crown shelter.

[0038] Then, the dense forest cooperative monitoring is carried out: the master unmanned aerial vehicle collects the tree crown layer atmospheric spectrum data; the spectrum monitoring instrument of the forest slave unmanned aerial vehicle starts the near-infrared auxiliary light source, collects the soil spectrum data through the forest shadow, and adjusts the monitoring angle according to the tree crown shelter angle through the formula (newly added compensation angle for tree crown shelter) adjustment monitoring angle; the obstacle avoidance fusion unit synchronously fuses the vegetation height data and real-time visual sensing data to avoid sudden branches.

[0039] Finally, the ecological data fusion output is carried out: the data processing module carries out "shadow-vegetation shelter" double correction on the forest spectrum data, fuses the atmospheric data and soil data to generate the ecological factor distribution report of "dense forest crown layer-forest undergrowth"; the user can adjust the coverage density of the forest monitoring point based on the report, and restart the local cooperative monitoring.

[0040] Embodiment two: water quality-terrain joint monitoring scene of river-lake interlaced waterfront wetland First, the system initialization and wetland path planning are carried out: the master unmanned aerial vehicle unit imports the river and lake distribution DEM data and the wetland ecological sensitive area boundary data of the monitoring area, adjusts the (monitoring point coverage density weight) of the global path cost function to the ecological sensitive area exclusive weight, divides "river water area, shallow muddy area, wetland vegetation area", and deploys 1 water-land adaptive slave unmanned aerial vehicle + 1 light mountain adaptive slave unmanned aerial vehicle, and pre-starts the float anti-mud coating assembly and shallow track assembly.

[0041] Subsequently, the wetland terrain adaptive deployment is carried out: the master unmanned aerial vehicle issues instructions to the water-land slave unmanned aerial vehicle, activates the folding inflatable float (the float surface is covered with anti-mud coating), floats on the river water surface to complete the deployment; issues instructions to the shallow slave unmanned aerial vehicle, retracts the vacuum suction cup assembly and expands the shallow track assembly, detects the bearing capacity of the muddy area through the pressure sensing assembly, and completes the stable deployment of the shallow.

[0042] Then carry out wetland joint monitoring: water from the unmanned aerial vehicle's hydrological water flow sensor synchronously collects water body pH, dissolved oxygen and flow rate data; from the unmanned aerial vehicle's terrain slope sensor, collect the slope data of the muddy area, and the spectral monitoring instrument collects the spectral data of the wetland vegetation; the adaptive adjustment mechanism mounted adjusts the immersion depth of the hydrological sensor according to the water surface ripple disturbance; the obstacle avoidance fusion unit fuses the bearing capacity data of the shallow muddy area to avoid the unmanned aerial vehicle from sinking into soft terrain.

[0043] Finally, carry out wetland health assessment output: the data processing module fuses water quality data, terrain data and vegetation spectral data to generate a “river and lake water quality-wetland terrain-vegetation health” correlation assessment report; users can issue repeated water sampling instructions for pollution risk areas to obtain accurate data.

[0044] Example Three: Geological-atmospheric joint monitoring scene of plateau permafrost terrain First, system initialization and permafrost path planning: the main unmanned aerial vehicle unit imports the DEM data and permafrost crack distribution priori data of the monitoring area, adds low-temperature environment weight, low-temperature operation cost) to the global path cost function, and divides “permafrost crack area, permafrost layer atmosphere area”; deploy 2 cold-resistant slave unmanned aerial vehicles and pre-start the low-temperature insulation component.

[0045] Subsequently, permafrost terrain adaptive deployment: the main unmanned aerial vehicle issues instructions to the slave unmanned aerial vehicle operating in the crack area, activates the vacuum suction cup component + ultrasonic crack detection component, the ultrasonic sensor identifies the crack position first, and the vacuum suction cup component avoids the crack to complete the deployment on the permafrost layer; the slave unmanned aerial vehicle operating in the atmospheric area maintains hovering, and the low-temperature insulation component maintains the working temperature of the monitoring equipment.

[0046] Then carry out permafrost cooperative monitoring: the main unmanned aerial vehicle collects plateau atmospheric spectral data (synchronously corrects the atmospheric extinction coefficient k as a special value for plateau thin atmosphere); the crack area slave unmanned aerial vehicle's terrain slope sensor collects permafrost slope data, and the spectral monitoring instrument collects permafrost layer soil spectral data; the adaptive adjustment mechanism mounted adjusts the rotation rate of the electric pan according to the rigidity change of the equipment under low temperature; the obstacle avoidance fusion unit fuses the permafrost crack priori data and real-time laser radar data to avoid the crack area.

[0047] Finally, carry out frozen-thawed state analysis output: the data processing module fuses soil spectral data, slope data and atmospheric data to generate a permafrost freeze-thaw state evaluation report; users can issue multiple monitoring instructions for suspected thawing areas to track freeze-thaw changes.

[0048] ​The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A complex terrain monitoring system in collaboration with unmanned aerial vehicles (UAVs), characterized in that, It includes a main UAV unit, a swarm of slave UAVs, a terrain adaptation module, an adaptive adjustment mechanism, a multi-source monitoring unit, an obstacle avoidance fusion unit, and a data processing module; The main UAV unit is equipped with a central control module and a global communication unit, which are used to perform global path planning, UAV task allocation and data aggregation. The slave drone cluster is communicatively connected to the master drone unit and is used to perform regional monitoring tasks; The terrain adaptation module is configured in the drone cluster to adapt to stable operation in different terrains; The mounting adaptive adjustment mechanism is configured on the main UAV unit and the slave UAV cluster, and is used to dynamically adjust the position and angle of the monitoring equipment. The multi-source monitoring unit is configured in the main UAV unit and the slave UAV cluster to collect multi-dimensional monitoring data; The obstacle avoidance fusion unit is communicatively connected to the main UAV unit and is used to fuse terrain information and real-time sensor data to achieve obstacle avoidance control. The data processing module is communicatively connected to the main UAV unit and is used to perform anti-interference processing and fusion output of the monitoring data.

2. The complex terrain monitoring system in collaboration with unmanned aerial vehicles (UAVs) according to claim 1, characterized in that: The drone swarm includes mountain-adaptive drones and water-adaptive drones; the mountain-adaptive drones are used for slope terrain monitoring tasks, and the water-adaptive drones are used for waterfront terrain monitoring tasks.

3. The complex terrain monitoring system in collaboration with unmanned aerial vehicles (UAVs) according to claim 2, characterized in that: The terrain adaptation module includes a vacuum suction cup assembly and a pressure sensing assembly configured for a mountain-adaptive slave drone, and a foldable inflatable float assembly configured for a water-adaptive slave drone; the vacuum suction cup assembly adjusts the suction force based on the detection data from the pressure sensing assembly, and the foldable inflatable float assembly can be pre-inflated to adapt to water surface operations.

4. The complex terrain monitoring system in collaboration with unmanned aerial vehicles (UAVs) according to claim 1, characterized in that: The mounted adaptive adjustment mechanism includes a motorized gimbal, an angle sensor, and a control submodule. The angle sensor is used to collect real-time airflow velocity components, and the control submodule, based on the real-time airflow velocity components and the field-of-view requirements of the monitoring equipment, uses a formula... Calculate the mounting angle And control the electric pan-tilt unit to adjust the position and angle of the monitoring equipment; in, The airflow component perpendicular to the monitoring field of view. To monitor the field of view of the equipment, To monitor the distance to the target.

5. A complex terrain monitoring system in collaboration with unmanned aerial vehicles (UAVs) according to claim 1, characterized in that: The obstacle avoidance fusion unit is used to fuse prior data from the digital elevation model (DEM) of the monitoring area and obstacle distance data from real-time lidar. and visual sensing of terrain texture features The main UAV unit, based on the output data of the obstacle avoidance fusion unit, uses the formula... Generate local path correction instructions and send them to the drone cluster; among which, For the initial local path, This is the path correction amount. This is the safe distance threshold.

6. A complex terrain monitoring system in collaboration with unmanned aerial vehicles (UAVs) according to claim 1, characterized in that: The multi-source monitoring unit includes a non-contact spectral monitoring instrument, a hydrological water sampling-flow velocity sensor, and a topographic slope sensor. The non-contact spectral monitoring instrument is used to collect atmospheric and soil spectral data, the hydrological water sampling-flow velocity sensor is used to collect water body samples and water flow velocity data, and the terrain slope sensor is used to collect terrain slope data.

7. A complex terrain monitoring system in collaboration with unmanned aerial vehicles (UAVs) according to claim 6, characterized in that: The data processing module incorporates a spectral anti-interference correction algorithm and a multi-source data fusion model. The spectral anti-interference correction algorithm is obtained through the formula The original spectral intensity I is corrected to obtain the corrected spectral intensity. ; Where k is the atmospheric extinction coefficient and h is the monitoring altitude. This is the solar altitude angle.

8. A complex terrain monitoring system in collaboration with unmanned aerial vehicles (UAVs) according to claim 1, characterized in that: The global path planning of the main UAV unit is achieved through a global path cost function. accomplish; in, These are the weighting coefficients. As a result of the terrain slope, The cost of obstacle distance, This comes at the cost of monitoring point coverage density.

9. A complex terrain monitoring system in collaboration with unmanned aerial vehicles (UAVs) according to claim 2, characterized in that: The master UAV unit issues commands to the slave UAV cluster based on the terrain type determined by the global path planning; for sloping terrain, it activates the vacuum suction cup component and pressure sensing component of the mountain-adaptive slave UAV; for waterfront terrain, it activates the foldable inflatable float component of the water-adaptive slave UAV.

10. A complex terrain monitoring system in collaboration with unmanned aerial vehicles (UAVs) according to claim 6, characterized in that: The division of labor among the multi-source monitoring units is as follows: the non-contact spectrometer of the main UAV unit collects atmospheric spectral data; the mountain-adapted unit collects soil spectral data after the auxiliary light source is calibrated simultaneously by the non-contact spectrometer of the UAV; and the land-water-adapted unit collects water samples and water flow velocity data from the hydrological sampling and flow velocity sensor of the UAV.

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