Unmanned aerial vehicle pollution adaptive sampling flight control method and system

Abstract

This invention provides an adaptive sampling flight control method and system for pollutants from unmanned aerial vehicles (UAVs), relating to the field of UAV technology. The method includes the following steps: constructing a three-level heterogeneous grid system and establishing the correlation between grids through cross-level data topology mapping; collecting pollutant concentration data and dynamically activating and / or dormant grids of different precision; calculating a three-dimensional concentration gradient vector using a multi-mode gradient reconstruction mechanism, and combining gradient direction identification and dynamic altitude adjustment strategies to trace the core area of ​​pollutants or track the diffusion boundary of pollutants; selecting different interpolation algorithms according to the system load state and generating a pollution concentration field isosurface model based on the moving cube algorithm; constructing a weighted cost map, integrating real-time concentration change information and historical sampling data to generate a spatially weighted cost value, and dividing different priority areas; implementing hierarchical trajectory planning according to the cost value distribution, and configuring differentiated sampling density and flight speed in different priority areas.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to an adaptive sampling flight control method and system for UAV pollutants. Background Technology

[0002] With the widespread application of drone technology in the field of environmental monitoring, using drones for pollutant sampling has become an important means of efficiently obtaining spatial distribution data.

[0003] However, traditional UAV sampling systems typically employ a fixed flight altitude and fixed route cruising mode, combining pre-set sampling points to complete data collection. This approach reveals significant limitations in complex polluted environments. Fixed-altitude cruising cannot respond to the vertical distribution characteristics of pollutants, which often exhibit three-dimensional non-uniform distribution in the atmosphere or space, such as vertical concentration gradient changes. Traditional methods lack real-time concentration gradient sensing capabilities and rely on manually pre-set routes, failing to integrate real-time concentration field data with historical sampling experience. This results in rigid sampling point distribution, insufficient sampling targeting, and low data validity. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive sampling flight control method and system for pollutants from unmanned aerial vehicles (UAVs) to solve the problems mentioned in the background art, such as the lack of real-time concentration gradient perception capability, reliance on manually preset flight paths, failure to integrate real-time concentration field data and historical sampling experience, resulting in rigid distribution of sampling points, insufficient sampling targeting, and low data effectiveness.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an adaptive sampling flight control method for pollutants from unmanned aerial vehicles (UAVs), comprising the following steps: constructing a three-level heterogeneous grid system including coarse, medium, and fine grids, and establishing the correlation between grids through cross-level data topology mapping; collecting pollutant concentration data and dynamically activating and / or dormant grids of different precisions by evaluating concentration fluctuation characteristics and system load status in real time; calculating a three-dimensional concentration gradient vector based on the pollutant concentration data in the grid using a multi-mode gradient reconstruction mechanism, and combining gradient direction identification and dynamic altitude adjustment strategies to trace the core area of ​​pollutants along the direction of increasing concentration or to track the diffusion boundary of pollutants along the direction of decreasing concentration; selecting a high-precision interpolation algorithm or a lightweight interpolation algorithm according to the system load status, and generating a pollution concentration field isosurface model based on the moving cube algorithm; constructing a weighted cost map, integrating real-time concentration change information and historical sampling data to generate a spatially weighted cost value, and dividing the pollution concentration field into different priority areas; implementing hierarchical trajectory planning according to the cost value distribution, and configuring differentiated sampling density and flight speed in different priority areas.

[0006] Optionally, the step of dynamically activating and / or suspending grids of different precisions by real-time evaluation of concentration fluctuation characteristics and system load status specifically includes: automatically activating the medium and fine grids in the corresponding area when the standard deviation of concentration from multiple consecutive samples within the coarse grid is detected to be ≥8ppm; increasing the activation ratio of the medium grid when the system power is >70%; forcing the fine grid to suspend when the system power is <30%; suspending the fine grid, downgrading the medium grid, and limiting the UAV's flight speed when emergency obstacle avoidance is triggered; and achieving seamless data transfer during grid switching through hierarchical topology mapping.

[0007] Optionally, the step of calculating the three-dimensional concentration gradient vector using the multi-mode gradient reconstruction mechanism specifically includes: the basic differential mode calculates the gradient components for sampling points within the grid by using the concentration values ​​of adjacent points, and the gradient calculation period is synchronized with the sampling frequency; the noise reduction enhancement mode is activated when the IMU detects that the UAV vibration acceleration exceeds a preset threshold, and uses a sliding window for smoothing filtering and calculates the gradient components; the sensor fusion mode introduces an IMU attitude compensation term, and calculates the gradient components by determining the fusion weight through offline calibration; the spatial step size is dynamically adjusted based on the mode difference rate δ, maintaining the maximum step size when the mode difference rate δ < 10%, linearly reducing the step size when the mode difference rate 10% ≤ δ < 30%, and triggering a three-level response and recording the sampling points when the mode difference rate δ ≥ 30%.

[0008] Optionally, the step of combining gradient direction recognition and dynamic height adjustment strategy to trace the core area of ​​pollutants along the direction of increasing concentration or to track the diffusion boundary of pollutants along the direction of decreasing concentration specifically includes: if it is necessary to trace the core area of ​​pollutants, then when the vertical gradient component exceeds the gradient threshold, a gradient ascent mode is adopted to drive the UAV to travel towards the high concentration area; if it is necessary to track the diffusion boundary of pollutants, then when the vertical gradient component is lower than the gradient threshold, a gradient descent mode is adopted to drive the UAV to travel towards the low concentration boundary.

[0009] Optionally, the step of selecting a high-precision interpolation algorithm or a lightweight interpolation algorithm based on the system load state and generating a pollution concentration field isosurface model based on the moving cube algorithm specifically includes: when the CPU utilization is <40%, calling the Kriging interpolation algorithm; when the CPU utilization is ≥40%, switching to the inverse distance weighted interpolation algorithm; using the improved moving cube algorithm to generate an initial triangular surface model, analyzing the local curvature characteristics of the initial surface, performing dynamic mesh subdivision for high curvature regions, and performing mesh simplification for low curvature regions.

[0010] Optionally, the step of integrating real-time concentration change information and historical sampling data to generate a spatially weighted cost value and dividing the pollution concentration field into different priority areas specifically includes: extracting the gradient magnitude through Sobel gradient amplitude, extracting the Laplacian edge score through the Laplacian operator, constructing a historical data timeliness evaluation function, calculating historical weights through historical sampling variance and time decay factor, and linearly integrating the gradient magnitude, Laplacian edge score, and historical weights according to a preset weight ratio to form a cost value; dividing different priority control areas: when the current value is ≥0.9, it is classified as a Level S red zone; when 0.7≤cost value<0.9, it is classified as a Level A orange zone; when 0.4≤cost value<0.7, it is classified as a Level B yellow zone; and when the current value<0.4, it is classified as a Level C green zone; and adding an emergency flag weighting coefficient to gradient change areas to increase sampling priority.

[0011] Optionally, the step of implementing hierarchical trajectory planning based on cost distribution and configuring differentiated sampling density and flight speed in different priority areas specifically includes: configuring a sampling density of 1 point / 0.5m³ and a cruise speed of 2m / s in the S-level area; configuring a sampling density of 1 point / 2m³ and a cruise speed of 5m / s in the A-level area; configuring a sampling density of 1 point / 5m³ and a cruise speed of 8m / s in the B-level area; and configuring a sampling density of 1 point / 10m³ and a cruise speed of 10m / s in the C-level area.

[0012] On the other hand, the present invention also provides an adaptive sampling flight control system for pollutants from unmanned aerial vehicles (UAVs), comprising: a heterogeneous grid system construction module for constructing a three-level heterogeneous grid system including coarse, medium, and fine grids, and establishing the correlation between grids through cross-level data topology mapping; a grid dynamic activation and dormancy module for collecting pollutant concentration data and dynamically activating and / or dormant grids of different precisions by evaluating concentration fluctuation characteristics and system load status in real time; and a pollutant concentration identification module for calculating a three-dimensional concentration gradient vector based on the pollutant concentration data in the grid using a multi-mode gradient reconstruction mechanism, combined with gradient direction identification and dynamic altitude adjustment strategies. The system traces the core area of ​​pollutants along the direction of increasing concentration or tracks the diffusion boundary of pollutants along the direction of decreasing concentration; the isosurface model generation module is used to select Kriging interpolation or inverse distance weighting algorithm according to the system load status, and generate the isosurface model of the pollution concentration field based on the moving cube algorithm; the weighted cost map construction module is used to construct a weighted cost map, integrate real-time concentration change information and historical sampling data to generate spatial weighted cost value, and divide the pollution concentration field into high-value area, medium-value area and low-value area; the hierarchical trajectory planning module is used to implement hierarchical trajectory planning according to the cost value distribution, and configure differentiated sampling density and flight speed in different priority areas.

[0013] On the other hand, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described UAV pollutant adaptive sampling flight control method.

[0014] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described UAV pollutant adaptive sampling flight control method.

[0015] Compared with the prior art, the beneficial effects of the present invention are:

[0016] Traditional drones, when cruising at a fixed altitude, cannot adjust their flight altitude according to the vertical distribution characteristics of pollutants, easily leading to missed sampling in high-concentration areas or ineffective dwelling in low-value areas. This application obtains a three-dimensional concentration gradient vector in real time by calculating the local concentration gradient. Combined with gradient direction identification and dynamic altitude adjustment strategies based on multimodal elevation control, the drone can actively trace to the core area of ​​the pollution source along the direction of increasing concentration, or precisely track the pollution diffusion boundary along the direction of decreasing concentration, achieving three-dimensional perception and dynamic adaptation of the spatial distribution of pollution.

[0017] This application generates a 3D spatially weighted cost map by fusing gradient magnitude, Laplacian edge score, and historical weights, dividing the contaminated field into different priority value regions (S / A / B / C levels). Through hierarchical trajectory planning, sampling density and flight speed are dynamically adjusted: high-value regions employ dense sampling points and low-speed cruising to ensure data accuracy in critical areas; low-value regions extend sampling intervals and increase flight speed to reduce ineffective dwell time. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the method steps of the present invention.

[0019] Figure 2 This is a schematic diagram of the preprocessing method steps of the present invention.

[0020] In the diagram: 10 - Heterogeneous grid system construction module, 20 - Grid dynamic activation and dormancy module, 30 - Pollutant concentration identification module, 40 - Isosurface model generation module, 50 - Weighted cost map construction module, 60 - Hierarchical trajectory planning module. Detailed Implementation

[0021] The present invention will now be clearly and completely described in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] Those skilled in the art will understand that, unless explicitly stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in the specification of this application means the presence of features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0024] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0025] It should be understood that the sequence number and size of each step in this embodiment do not imply the order of execution. The execution order of each process is determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application embodiment.

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0027] Please refer to Figure 1This invention discloses an adaptive sampling flight control method for pollutants from unmanned aerial vehicles (UAVs). The method includes the following steps: constructing a three-level heterogeneous grid system comprising coarse, medium, and fine grids; establishing relationships between grids through cross-level data topology mapping; collecting pollutant concentration data and dynamically activating and / or suspending grids of different accuracies based on real-time evaluation of concentration fluctuation characteristics and system load status; calculating a three-dimensional concentration gradient vector based on the pollutant concentration data in the grid using a multi-mode gradient reconstruction mechanism; combining gradient direction identification and dynamic altitude adjustment strategies to trace the core pollutant region along the concentration increase direction or track the pollutant diffusion boundary along the concentration decrease direction; selecting a high-precision interpolation algorithm or a lightweight interpolation algorithm according to the system load status; generating a pollution concentration field isosurface model based on the moving cube algorithm; constructing a weighted cost map; integrating real-time concentration fluctuation information and historical sampling data to generate a spatially weighted cost value; dividing the pollution concentration field into different priority regions; and implementing hierarchical trajectory planning based on the cost value distribution, configuring differentiated sampling density and flight speed in different priority regions.

[0028] Specifically, this embodiment uses volatile organic compound (VOCs) monitoring in an urban industrial area as the application scenario. A quadcopter drone equipped with a multispectral gas sensor, inertial measurement unit (IMU), high-precision positioning module, and edge computing terminal is used to achieve pollutant sampling and dynamic tracking in a 500m×500m×100m three-dimensional space. After system startup, a three-level grid is first constructed according to the preset range (500m×500m×100m) of the task area: Coarse grid: unit size 5.0m×5.0m×5.0m, used for rapid scanning and global situational awareness. Medium grid: unit size 1.0m×1.0m×1.0m, nested within the coarse grid, with an initial activation quantity of 1 / 5 of the coarse grid. Fine grid: unit size 0.2m×0.2m×0.2m, dynamically activated only in high-concentration areas, with a maximum activation quantity not exceeding 10% of the total grid. This hierarchical grid structure enables cross-level grid association, ensuring seamless data transfer during grid switching. A multi-mode gradient reconstruction mechanism is employed to calculate the three-dimensional concentration gradient field, including a basic differential mode, a noise-enhancing mode, and a multi-source fusion mode. The spatial sampling step size is dynamically adjusted based on the difference rate between modes. The three-dimensional concentration gradient vector is obtained in real time by calculating local concentration gradients. Combined with gradient direction identification and dynamic altitude adjustment strategies based on multi-modal elevation control, the UAV can actively trace the pollution source core area along the concentration increase direction or precisely track the pollution diffusion boundary along the concentration decrease direction, achieving three-dimensional perception and dynamic adaptation of the pollution spatial distribution. A three-dimensional spatial weighted cost map is generated by fusing gradient magnitude, Laplacian edge score, and historical weights, dividing the pollution field into different priority value areas (S-level / A-level / B-level / C-level). Sampling density and flight speed are dynamically adjusted through hierarchical trajectory planning: dense sampling points and low-speed cruising are used in high-value areas to ensure data accuracy in key areas; the sampling interval is extended and the flight speed is increased in low-value areas to reduce ineffective dwell time.

[0029] In some embodiments, the step of dynamically activating and / or suspending grids of different precisions by real-time evaluation of concentration fluctuation characteristics and system load status specifically includes: automatically activating the medium and fine grids in the corresponding area when the standard deviation of concentration from multiple consecutive samples within the coarse grid is detected to be ≥8ppm; increasing the activation ratio of the medium grid when the system power is >70%; forcing the fine grid to suspend when the system power is <30%; suspending the fine grid, downgrading the medium grid, and limiting the UAV's flight speed when emergency obstacle avoidance is triggered; and achieving seamless data transfer during grid switching through hierarchical topology mapping.

[0030] Specifically, when the standard deviation of the concentration from three consecutive samples within a coarse grid cell is ≥8 ppm, the medium and fine grids in that area are automatically activated with an activation delay of <100ms. Battery remaining power is monitored in real time; when the power is >70%, the activation ratio of the medium grid increases to 30%; when the power is <30%, the fine grid automatically goes into sleep mode. Upon triggering the emergency obstacle avoidance mode, all fine grids go into sleep mode within 10ms, the medium grid switches to coarse grid precision, and the flight speed is limited to 3m / s.

[0031] The dynamic grid activation mechanism achieves a dynamic balance between computational resources and sampling accuracy through dual judgments of concentration fluctuations and system state. The power-correlated grid activation strategy effectively extends system endurance, while grid degradation during emergency obstacle avoidance ensures flight safety as a priority. Cross-level data topology mapping technology eliminates data gaps during grid switching, guaranteeing the spatiotemporal continuity of concentration field modeling. This flexible grid management approach enables the system to cope with both sudden pollution events and the resource constraints of long-term monitoring tasks.

[0032] In some embodiments, the step of calculating the three-dimensional concentration gradient vector using a multi-mode gradient reconstruction mechanism specifically includes: In the basic differential mode, for sampling points within the grid, gradient components are calculated using the concentration values ​​of adjacent points, and the gradient calculation period is synchronized with the sampling frequency; In the noise reduction enhancement mode, the IMU is activated when it detects that the UAV vibration acceleration exceeds a preset threshold, and a sliding window smoothing filter is used to calculate the gradient components; In the sensor fusion mode, an IMU attitude compensation term is introduced, and the fusion weights are determined through offline calibration to calculate the gradient components; The spatial step size is dynamically adjusted based on the mode difference rate δ. When the mode difference rate δ < 10%, the maximum step size is maintained; when the mode difference rate 10% ≤ δ < 30%, the step size is linearly reduced; and when the mode difference rate δ ≥ 30%, a three-level response is triggered and sampling points are recorded.

[0033] Specifically, pattern difference rate When δ < 10%, the step size Δs = 1.0m (medium grid) or 0.2m (fine grid); when 10% ≤ δ < 30%, the step size is linearly reduced according to the confidence level; when δ ≥ 30%, a level 3 response is triggered and the sampling point information is recorded.

[0034] The multi-mode gradient reconstruction mechanism addresses the reliability issues of single-difference algorithms in complex environments through the organic combination of three modes: basic, anti-noise, and fusion. The vibration-triggered anti-noise mode significantly improves data quality in mobile sampling scenarios, while attitude compensation fusion technology eliminates measurement biases caused by UAV maneuvers. A step-size control strategy based on the mode difference rate achieves an adaptive balance between computational accuracy and real-time performance, providing a quantifiable gradient confidence index for subsequent control decisions. This hierarchical response mechanism ensures computational efficiency under normal conditions while automatically triggering protective measures in abnormal situations.

[0035] In some embodiments, the step of combining gradient direction recognition and dynamic height adjustment strategies to trace the core area of ​​pollutants along the direction of increasing concentration or to track the diffusion boundary of pollutants along the direction of decreasing concentration specifically includes: if it is necessary to trace the core area of ​​pollutants, then when the vertical gradient component exceeds the gradient threshold, a gradient ascent mode is adopted to drive the UAV to travel towards the high concentration area; if it is necessary to track the diffusion boundary of pollutants, then when the vertical gradient component is below the gradient threshold, a gradient descent mode is adopted to drive the UAV to travel towards the low concentration boundary.

[0036] Specifically, when a positive vertical concentration gradient is detected, the system automatically triggers a gradient ascent mode, calculates the altitude change through a control gain adaptive mechanism, and drives the drone to travel towards the high concentration area; conversely, it travels in the opposite direction of the gradient, achieving three-dimensional perception and dynamic adaptation of the spatial distribution of pollution. Compared with fixed-altitude cruising, this greatly improves the targeted sampling effect.

[0037] Gradient orientation recognition technology enables UAVs to possess three-dimensional situational awareness capabilities in pollution fields, allowing for autonomous switching between source tracing and tracking modes through sign discrimination of vertical gradient components. A threshold-triggered dual-mode altitude adjustment strategy ensures rapid response in strong gradient regions while avoiding over-adjustment in weak gradient regions. This directional tracking mechanism overcomes the blindness of fixed-altitude sampling, enabling UAVs to navigate autonomously along concentration gradients like "olfactory organs," achieving three-dimensional tracking of pollution migration paths.

[0038] In some embodiments, the step of selecting a high-precision interpolation algorithm or a lightweight interpolation algorithm based on the system load state and generating a pollution concentration field isosurface model based on the moving cube algorithm specifically includes: when the CPU utilization is <40%, calling the Kriging interpolation algorithm; when the CPU utilization is ≥40%, switching to the inverse distance weighted interpolation algorithm; using the improved moving cube algorithm to generate an initial triangular surface model, analyzing the local curvature characteristics of the initial surface, performing dynamic mesh subdivision for high curvature regions, and performing mesh simplification for low curvature regions.

[0039] Specifically, the edge computing terminal monitors CPU utilization in real time. When CPU utilization is <40%, a high-precision interpolation algorithm, such as Kriging interpolation, is used; when CPU utilization is ≥40%, a lightweight interpolation algorithm, such as inverse distance weighted interpolation, is switched to. The curvature calculation of triangular facets uses the vector cross product method; when curvature >0.8m... -1 At that time, perform subdivision operation; perform vertex redundancy deletion on small-area patches with low curvature.

[0040] The load-aware interpolation algorithm dynamic selection mechanism achieves the optimal trade-off between computational accuracy and system stability. The improved moving cube algorithm, combined with curvature-driven mesh optimization, significantly reduces geometric complexity while maintaining the topological correctness of isosurfaces. This adaptive modeling technique satisfies the detail preservation requirements in high-curvature regions while reducing unnecessary computational overhead through simplification in low-curvature regions, providing a lightweight solution for real-time 3D concentration field visualization.

[0041] In some embodiments, the step of fusing real-time concentration change information and historical sampling data to generate a spatially weighted cost value and dividing the pollution concentration field into different priority areas specifically includes: extracting the gradient magnitude through Sobel gradient amplitude, extracting the Laplacian edge score through the Laplacian operator, constructing a historical data timeliness evaluation function, calculating historical weights through historical sampling variance and time decay factor, and linearly fusing the gradient magnitude, Laplacian edge score, and historical weights according to a preset weight ratio to form a cost value; dividing different priority control areas: when the current value is ≥0.9, it is classified as a Level S red zone; when 0.7≤cost value<0.9, it is classified as a Level A orange zone; when 0.4≤cost value<0.7, it is classified as a Level B yellow zone; and when the current value<0.4, it is classified as a Level C green zone; and adding an emergency flag weighting coefficient to gradient abrupt change areas to increase sampling priority.

[0042] Specifically, the multi-feature fusion cost map construction technology achieves quantitative assessment of the value regions of contaminated fields through a linear combination of gradient magnitude, edge features, and historical weights. The design of a time-sensitivity decay function enables the system to dynamically update the regional importance assessment, avoiding excessive influence from historical data. A hierarchical control mechanism, combined with emergency marker weighting, forms a sampling strategy that highlights key areas while considering the overall picture, preventing both hotspot omissions and resource waste. This spatial value assessment system provides a scientific basis for subsequent trajectory planning.

[0043] In some embodiments, the step of implementing hierarchical trajectory planning based on cost distribution and configuring differentiated sampling density and flight speed in different priority areas specifically includes: configuring a sampling density of 1 point / 0.5m³ and a cruise speed of 2m / s in Class S areas; configuring a sampling density of 1 point / 2m³ and a cruise speed of 5m / s in Class A areas; configuring a sampling density of 1 point / 5m³ and a cruise speed of 8m / s in Class B areas; and configuring a sampling density of 1 point / 10m³ and a cruise speed of 10m / s in Class C areas.

[0044] Specifically, the value-driven hierarchical trajectory planning technology achieves synergistic optimization of monitoring efficiency and data quality through the inverse correlation configuration of sampling density and flight speed. The speed-density coupling adjustment mechanism forms a fine scanning mode of "low speed and high density" in the S-level region, while adopting a fast-passing strategy of "high speed and low density" in the C-level region. This differentiated resource allocation allows limited sampling capacity to be concentrated on key areas, improving the overall time utilization of the system and providing technical support for rapid response in emergency monitoring scenarios.

[0045] Please refer to Figure 2 On the other hand, the present invention also provides an adaptive sampling flight control system for pollutants from unmanned aerial vehicles (UAVs), comprising: a heterogeneous grid system construction module for constructing a three-level heterogeneous grid system including coarse, medium, and fine grids, and establishing the correlation between grids through cross-level data topology mapping; a grid dynamic activation and dormancy module for collecting pollutant concentration data and dynamically activating and / or dormant grids of different precisions by evaluating concentration fluctuation characteristics and system load status in real time; and a pollutant concentration identification module for calculating a three-dimensional concentration gradient vector based on the pollutant concentration data in the grid using a multi-mode gradient reconstruction mechanism, combined with gradient direction identification and dynamic altitude adjustment strategies. The system traces the core area of ​​pollutants along the direction of increasing concentration or tracks the diffusion boundary of pollutants along the direction of decreasing concentration; the isosurface model generation module is used to select Kriging interpolation or inverse distance weighting algorithm according to the system load status, and generate the isosurface model of the pollution concentration field based on the moving cube algorithm; the weighted cost map construction module is used to construct a weighted cost map, integrate real-time concentration change information and historical sampling data to generate spatial weighted cost value, and divide the pollution concentration field into high-value area, medium-value area and low-value area; the hierarchical trajectory planning module is used to implement hierarchical trajectory planning according to the cost value distribution, and configure differentiated sampling density and flight speed in different priority areas.

[0046] On the other hand, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described UAV pollutant adaptive sampling flight control method.

[0047] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described UAV pollutant adaptive sampling flight control method.

[0048] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0049] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, database, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0050] The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention's specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for adaptive sampling flight control of pollutants by unmanned aerial vehicles (UAVs), characterized by the following steps: include: A three-level heterogeneous grid system comprising coarse, medium, and fine grids is constructed, and the relationships between grids are established through cross-level data topology mapping; Collect pollutant concentration data and dynamically activate and / or suspend grids of different precision by evaluating concentration fluctuation characteristics and system load status in real time; Based on pollutant concentration data in the grid, a multi-mode gradient reconstruction mechanism is used to calculate the three-dimensional concentration gradient vector. Combined with gradient direction identification and dynamic height adjustment strategy, the pollutant core area is traced along the direction of increasing concentration, or the pollutant diffusion boundary is tracked along the direction of decreasing concentration. Select a high-precision interpolation algorithm or a lightweight interpolation algorithm based on the system load status, and generate an isosurface model of the pollution concentration field based on the moving cube algorithm; Construct a weighted cost map, integrate real-time concentration change information and historical sampling data to generate spatial weighted cost value, and divide the pollution concentration field into different priority areas; Based on the value distribution, hierarchical trajectory planning is implemented, and differentiated sampling density and flight speed are configured in different priority areas.

2. The UAV pollutant adaptive sampling flight control method according to claim 1, characterized in that, The steps of dynamically activating and / or suspending grids of different precision by real-time evaluation of concentration fluctuation characteristics and system load status specifically include: When the standard deviation of the concentration from multiple consecutive samples within the coarse grid is detected to be ≥8 ppm, the medium and fine grids in the corresponding area are automatically activated. When the system battery level is >70%, increase the activation ratio of the medium grid; when the system battery level is <30%, force the fine grid to hibernate. When emergency obstacle avoidance is triggered, the fine grid will be put into hibernation, the medium grid will be downgraded, and the drone's flight speed will be limited; Seamless data transfer during grid switching is achieved through hierarchical topology mapping.

3. The UAV pollutant adaptive sampling flight control method according to claim 1, characterized in that, The steps for calculating the three-dimensional concentration gradient vector using the multi-mode gradient reconstruction mechanism specifically include: The basic differential mode calculates gradient components for sampling points within the medium grid by using the concentration values ​​of adjacent points, and the gradient calculation period is synchronized with the sampling frequency. The noise-enhancing mode IMU is activated when it detects that the drone's vibration acceleration exceeds a preset threshold. It uses a sliding window smoothing filter and calculates gradient components. The sensor fusion mode introduces an IMU attitude compensation term, and calculates gradient components by determining the fusion weights through offline calibration. The spatial step size is dynamically adjusted based on the mode difference rate δ. When the mode difference rate δ < 10%, the maximum step size is maintained. When the mode difference rate 10% ≤ δ < 30%, the step size is linearly reduced. When the mode difference rate δ ≥ 30%, a three-level response is triggered and the sampling points are recorded.

4. The UAV pollutant adaptive sampling flight control method according to claim 1, characterized in that, The steps of combining gradient direction identification and dynamic height adjustment strategies to trace the core region of pollutants along the direction of increasing concentration or to track the diffusion boundary of pollutants along the direction of decreasing concentration specifically include: If it is necessary to trace the core area of ​​pollutants, when the vertical gradient component exceeds the gradient threshold, the gradient ascent mode is adopted to drive the drone to the high concentration area. If tracking the boundary of pollutant diffusion, when the vertical gradient component is below the gradient threshold, a gradient descent mode is used to drive the drone toward the low concentration boundary.

5. The UAV pollutant adaptive sampling flight control method according to claim 1, characterized in that, The steps of selecting a high-precision interpolation algorithm or a lightweight interpolation algorithm based on the system load state, and generating a pollution concentration field isosurface model based on the moving cube algorithm, specifically include: When CPU utilization is less than 40%, the Kriging interpolation algorithm is called; when CPU utilization is greater than or equal to 40%, the inverse distance weighted interpolation algorithm is switched to. An improved moving cube algorithm is used to generate an initial triangular surface model. The local curvature characteristics of the initial surface are analyzed, dynamic mesh subdivision is performed on high curvature regions, and mesh simplification is performed on low curvature regions.

6. The UAV pollutant adaptive sampling flight control method according to claim 1, characterized in that, The step of integrating real-time concentration change information and historical sampling data to generate a spatially weighted value and dividing the pollution concentration field into different priority regions specifically includes: Gradient magnitude is extracted using Sobel gradient magnitude, Laplacian edge score is extracted using the Laplacian operator, a historical data timeliness evaluation function is constructed, historical weights are calculated using historical sampling variance and time decay factor, and gradient magnitude, Laplacian edge score and historical weights are linearly fused according to preset weight ratio to form cost value. Different priority control areas are divided into S-level red zones when the present value is ≥0.9, A-level orange zones when the present value is ≤0.7 and <0.9, B-level yellow zones when the present value is ≤0.4 and <0.7, and C-level green zones when the present value is <0.

4. An emergency flag weighting coefficient is added to the gradient abrupt change region to increase the sampling priority.

7. The UAV pollutant adaptive sampling flight control method according to claim 1, characterized in that, The step of implementing hierarchical trajectory planning based on cost distribution and configuring differentiated sampling density and flight speed in different priority areas specifically includes: The S-class area is equipped with a sampling density of 1 point / 0.5m³ and a cruising speed of 2m / s; Class A areas are configured with a sampling density of 1 point / 2m³ and a cruising speed of 5m / s; Class B areas are configured with a sampling density of 1 point / 5m³ and a cruising speed of 8m / s. Class C areas are configured with a sampling density of 1 point / 10m³ and a cruising speed of 10m / s.

8. An adaptive sampling flight control system for pollutants from an unmanned aerial vehicle (UAV), characterized in that, include: The heterogeneous grid system construction module is used to construct a three-level heterogeneous grid system containing coarse, medium, and fine grids, and establishes the relationship between grids through cross-level data topology mapping; The grid dynamic activation and dormancy module is used to collect pollutant concentration data and dynamically activate and / or suspend grids of different precisions by evaluating concentration fluctuation characteristics and system load status in real time. The pollutant concentration identification module is used to calculate the three-dimensional concentration gradient vector based on the pollutant concentration data in the grid using a multi-mode gradient reconstruction mechanism. Combined with gradient direction identification and dynamic height adjustment strategy, it can trace the core area of ​​pollutants along the direction of increasing concentration or track the diffusion boundary of pollutants along the direction of decreasing concentration. The isosurface model generation module is used to select either Kriging interpolation or inverse distance weighting algorithm based on the system load status, and to generate an isosurface model of the pollution concentration field based on the moving cube algorithm. The weighted cost map construction module is used to build a weighted cost map, which integrates real-time concentration change information and historical sampling data to generate spatial weighted cost value, and divides the pollution concentration field into high-value areas, medium-value areas and low-value areas. The hierarchical trajectory planning module is used to implement hierarchical trajectory planning based on the cost distribution, and to configure differentiated sampling density and flight speed in different priority areas.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the UAV pollutant adaptive sampling flight control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the UAV pollutant adaptive sampling flight control method according to any one of claims 1 to 7.

Images