A multi-component gas data acquisition method and system based on a drone

Abstract

This invention proposes a method and system for acquiring multi-component gas data based on unmanned aerial vehicles (UAVs), belonging to the field of gas data acquisition technology. The method includes: controlling a UAV equipped with multi-component gas sensors to fly according to the preset boundary coordinates of a monitoring area, and hovering and sampling at each spatial grid node to obtain raw gas concentration data. Simultaneously, a digital elevation model is generated using an onboard positioning module and lidar, and component separation and mass spectrometry analysis are performed using a miniaturized gas chromatography-mass spectrometry module to obtain gas component fingerprint data. Finally, the gas data and the digital elevation model are spatiotemporally correlated and fused to generate a multi-component gas distribution field with three-dimensional geographic information annotation. This invention achieves on-site multi-component gas identification and, by fusing gas data with a three-dimensional terrain model, significantly improves the spatial resolution and data dimensionality of gas pollution monitoring in large-scale complex terrain.

Description

Technical Field

[0001] This invention belongs to the field of gas data acquisition technology, and in particular relates to a method and system for acquiring multi-component gas data based on unmanned aerial vehicles (UAVs). Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Gas pollution monitoring is a crucial link in ecological environment governance, industrial safety production, and public health protection. The demand for high-resolution, large-scale, and real-time acquisition of multi-component gas data is becoming increasingly urgent. Traditional gas data acquisition methods mainly rely on the deployment of fixed monitoring stations and manual on-site inspections. The former is limited by deployment costs and geographical conditions, resulting in limited monitoring coverage, blind spots, and difficulty in reflecting the spatial continuous distribution characteristics of gas pollution. The latter is not only inefficient and labor-intensive, but also faces problems such as inaccessibility in complex terrains (such as mountains, ravines, and complex structures in chemical industrial parks) and threats to personnel safety in high-risk environments. It is difficult to meet the actual needs in high-resolution, dynamic multi-component gas monitoring scenarios.

[0004] With the rapid development of drone technology, its mobility, flexibility, and ease of deployment have provided a new technological path for gas data acquisition, making drone gas acquisition an important direction for industry research and application. However, existing drone gas acquisition systems still have significant technical shortcomings. Most systems can only acquire the concentration of a single gas or a few other gases, lacking the ability to simultaneously detect multiple components of gases in complex environments, and thus failing to obtain complete information on the composition of gas pollution.

[0005] Meanwhile, existing technologies generally fail to effectively integrate gas monitoring data with three-dimensional geographic information. The collected data is only associated with simple planar coordinates, without incorporating key geographic information such as terrain elevation and geomorphological features. This makes it difficult to accurately reflect the impact of terrain undulations and spatial structure on gas diffusion, resulting in inaccurate depictions of the spatial distribution characteristics of gas pollution and low spatial resolution and fusion of the data. Furthermore, existing UAV sampling strategies mostly employ fixed-resolution grids, making it impossible to dynamically adjust sampling density based on the location of suspected pollution sources and terrain complexity. This easily leads to insufficient data in key areas and redundant data in non-key areas, further restricting the practicality of monitoring data and hindering the provision of comprehensive and accurate technical support for environmental pollution source tracing, risk assessment, and governance decisions. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, the present invention provides a method and system for acquiring multi-component gas data based on unmanned aerial vehicles (UAVs), aiming to solve the technical problems of existing solutions that can only acquire the concentration of a single gas, cannot achieve accurate analysis of multi-component gases, and are not effectively integrated with three-dimensional geographic information and have fixed sampling resolution.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a method for acquiring multi-component gas data based on an unmanned aerial vehicle (UAV); A method for acquiring multi-component gas data based on unmanned aerial vehicles (UAVs) includes: Step S1: Control the UAV equipped with a multi-component gas sensor to fly according to the preset monitoring area boundary coordinates, and hover in the monitoring area according to the preset spatial grid nodes one by one; during the hovering period of each spatial grid node, start the multi-component gas sensor to continuously sample and obtain the original gas concentration data of the corresponding node. Step S2: When the UAV hovers over each spatial grid node, it scans the area around the node using the onboard positioning module and lidar to obtain the three-dimensional geographic coordinates and terrain point cloud data of the local area where the node is located. Based on the terrain point cloud data collected from all spatial grid nodes, a digital elevation model of the monitoring area is generated. Step S3: When the UAV hovers and samples at each spatial grid node, it simultaneously starts the miniaturized gas chromatography-mass spectrometry module that integrates multi-component gas sensors to perform component separation and mass spectrometry analysis on the collected gas samples and obtain gas component fingerprint data. Step S4: Spatiotemporally correlate and fuse the original gas concentration data, three-dimensional geographic coordinates, and gas component fingerprint data corresponding to each spatial grid node with the digital elevation model to generate a multi-component gas distribution field with three-dimensional geographic information annotation.

[0008] As a further technical solution, in step S1, the UAV flies according to the boundary coordinates of the monitoring area, specifically as follows: Based on the boundary coordinates of the monitoring area, an adaptive variable resolution spatial grid covering the monitoring area is constructed; The node density of the adaptive variable resolution spatial grid is dynamically adjusted based on the preset coordinates of suspected pollution sources and terrain complexity data; The drone is controlled to traverse the spatial sequence of the spatial grid nodes and to execute a hovering command when it arrives at each node.

[0009] As a further technical solution, the traversal flight according to the spatial sequence of spatial grid nodes specifically includes: During the flight of the drone, real-time wind field data is acquired through airborne wind speed and direction sensors; The flight path of the UAV to the next spatial grid node is dynamically corrected based on real-time wind field data, so that the UAV is always in the upwind position of the current sampling node before hovering and sampling.

[0010] As a further technical solution, the digital elevation model generation process in step S2 includes: The terrain point cloud data acquired at each spatial grid node is preprocessed, including point cloud denoising, point cloud registration and point cloud fusion. Based on the preprocessed overall point cloud data, an irregular triangular mesh model is constructed using a triangular meshing algorithm. Rasterization interpolation is performed on the irregular triangular mesh model to generate digital elevation model grid data with a specified spatial resolution.

[0011] As a further technical solution, the component separation and mass spectrometry analysis process in step S3 includes: During drone hovering sampling, ambient gas is loaded into the chromatographic column of a miniaturized gas chromatography module using a micro sampling pump; The volatile organic compounds in a gas sample are separated sequentially using a chromatographic column. The separated components are sequentially introduced into the ion source of the micro mass spectrometry module for ionization, and the mass-to-charge ratio of each component is detected by the mass analyzer to form gas component fingerprint data containing retention time and characteristic mass spectrometry peaks.

[0012] As a further technical solution, the multi-component gas distribution field generation process in step S4 includes: Using the three-dimensional grid of the digital elevation model as the spatial reference framework, the three-dimensional geographic coordinates corresponding to each spatial grid node are used as spatial indexes; The raw gas concentration data and gas component fingerprint data collected at the same timestamp from each node are mapped to the corresponding three-dimensional grid vertices of the digital elevation model through spatial indexing. A three-dimensional spatial interpolation algorithm is used to interpolate the gas concentration and component data at the vertices of the three-dimensional grid that are not directly covered by nodes, thereby generating a multi-component gas concentration distribution field covering the entire monitoring area in three-dimensional space.

[0013] As a further technical solution, the three-dimensional spatial interpolation algorithm is an inverse distance weighted interpolation method. When calculating the interpolation weight at the three-dimensional mesh vertex that is not directly covered by the node, it also considers the difference in horizontal Euclidean distance and elevation dimension between the vertex and the known data node.

[0014] A second aspect of the present invention provides a multi-component gas data acquisition system based on an unmanned aerial vehicle (UAV).

[0015] A multi-component gas data acquisition system based on unmanned aerial vehicles (UAVs) includes: The flight control module is used to control the UAV equipped with a multi-component gas sensor to fly according to the preset boundary coordinates of the monitoring area and hover in the monitoring area according to the preset spatial grid nodes one by one; during the hovering of each spatial grid node, the multi-component gas sensor is activated to continuously sample and obtain the raw gas concentration data of the corresponding node. The geographic information module is used to scan the area around each spatial grid node by means of the airborne positioning module and lidar when the UAV hovers over it, to obtain the three-dimensional geographic coordinates and terrain point cloud data of the local area where the node is located, and to generate a digital elevation model of the monitoring area based on the terrain point cloud data collected from all spatial grid nodes. The gas analysis module is used to simultaneously activate a miniaturized gas chromatography-mass spectrometry module integrating multi-component gas sensors when the UAV hovers and samples at each spatial grid node. This module performs component separation and mass spectrometry analysis on the collected gas samples to obtain gas component fingerprint data. The data fusion module is used to perform spatiotemporal correlation and data fusion with the original gas concentration data, three-dimensional geographic coordinates, and gas component fingerprint data corresponding to each spatial grid node and the digital elevation model to generate a multi-component gas distribution field with three-dimensional geographic information annotation.

[0016] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of a multi-component gas data acquisition method based on an unmanned aerial vehicle as described in the first aspect of the present invention.

[0017] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of a multi-component gas data acquisition method based on an unmanned aerial vehicle as described in the first aspect of the present invention.

[0018] The above one or more technical solutions have the following beneficial effects: (1) By constructing an adaptive variable resolution spatial grid, the present invention dynamically adjusts the sampling node density according to the location of suspected pollution sources and the complexity of terrain. The grid is automatically densified in areas with high pollution risk and complex terrain, and relatively sparsely arranged in flat areas. This concentrates limited UAV resources in key areas, significantly improving the spatial resolution and operational efficiency of large-scale gas monitoring, and overcoming the shortcomings of traditional fixed grid sampling resolution being uniform and data being insufficient in key areas.

[0019] (2) This invention integrates a miniaturized gas chromatography-mass spectrometry module on the UAV platform, and performs gas component separation and mass spectrometry analysis at each sampling node to obtain gas component fingerprint data including retention time and characteristic mass spectrometry peaks. This breaks through the limitation of existing technologies that can only collect the concentration of a single or a few types of gases, and realizes the synchronous, accurate identification and qualitative analysis of multiple components in complex gas mixtures, providing rich chemical information dimensions for pollution source tracing.

[0020] (3) This invention uses airborne lidar to acquire terrain point cloud data and generate a digital elevation model as a three-dimensional spatial reference framework. The original gas concentration data and gas component fingerprint data are mapped to the grid vertices of the digital elevation model through spatial indexing. A three-dimensional inverse distance weighted interpolation algorithm that considers both horizontal distance and elevation difference is used for spatial interpolation to generate a multi-component gas distribution field with three-dimensional geographic information annotation, which truly reflects the three-dimensional diffusion characteristics of pollutants under complex terrain conditions.

[0021] (4) This invention acquires real-time wind field data through an airborne wind speed and direction sensor, dynamically corrects the flight path, and ensures that the UAV approaches the sampling node from the upwind direction, avoiding interference from its own rotor airflow on the sampling results. At the same time, a steady-state flow controller compensates for the sampling flow rate in real time based on air pressure and temperature feedback, ensuring a constant column injection flow rate and improving the repeatability and quantitative accuracy of chromatography-mass spectrometry analysis. The above design together ensures the accuracy of sampling and analysis, and the resulting three-dimensional gas distribution field provides high-quality data support for pollution source tracing, diffusion simulation, and governance decision-making.

[0022] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0024] Figure 1 This is a flowchart of the method in the first embodiment.

[0025] Figure 2 This is a system structure diagram of the second embodiment. Detailed Implementation

[0026] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0027] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0028] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0029] Example 1 This embodiment discloses a method for acquiring multi-component gas data based on unmanned aerial vehicles (UAVs). Using UAVs as a carrier, an adaptive variable resolution sampling grid is built and the flight path is dynamically corrected in conjunction with the wind field. A digital elevation model is constructed using lidar, and a micro gas chromatography-mass spectrometry module is integrated to obtain gas component fingerprints. An improved three-dimensional inverse distance weighted interpolation method is used to fuse multi-source data to generate a multi-component gas distribution field with three-dimensional geographic information, thereby improving the spatial resolution and data fusion of the monitoring.

[0030] Specifically, such as Figure 1 As shown, a method for acquiring multi-component gas data based on unmanned aerial vehicles includes: Step S1: Control the UAV equipped with a multi-component gas sensor to fly according to the preset boundary coordinates of the monitoring area, and hover in the monitoring area according to the preset spatial grid nodes one by one; during the hovering period of each spatial grid node, start the multi-component gas sensor to continuously sample and obtain the original gas concentration data of the corresponding node.

[0031] In this embodiment, the specific implementation process of the UAV's flight control based on the boundary coordinates of the monitored area is as follows: First, based on the user-preset boundary coordinates of the monitoring area, an adaptive variable-resolution spatial grid that fully covers the monitoring area is dynamically constructed using computational geometry methods. This grid is not fixed in advance; the spatial distribution density of its nodes is dynamically calculated and adjusted according to the preset coordinates of suspected pollution sources and pre-loaded terrain complexity data.

[0032] In areas surrounding suspected pollution source coordinates and regions with dramatic terrain variations, higher-density grid nodes are automatically generated to improve sampling resolution. In flat areas far from the point of interest, relatively sparse grid nodes are generated to improve inspection efficiency. Ultimately, an adaptive variable-resolution spatial grid node sequence with spatially non-uniform node density, balancing high resolution in key areas with overall inspection efficiency, is obtained. This sequence will serve as the waypoints for UAV flight and sampling.

[0033] The drone is controlled to traverse the generated spatial grid nodes. The flight control module plans an optimal traversal path that connects all grid nodes and controls the drone to fly along this path. When the drone determines that its position has reached the preset coordinate tolerance range of a certain grid node through the onboard positioning module, the flight controller executes a hovering command to keep the drone hovering stably at that node's spatial position.

[0034] As the UAV flies to the next target grid node according to the spatial sequence, it acquires real-time airspace wind field data, including wind speed vectors and wind direction, through onboard wind speed and direction sensors. Based on this real-time wind field data, the flight control module dynamically corrects the flight path to the target grid node online. The goal of path correction is to plan a path that allows the UAV to approach the target node from upwind and eventually hover over it. Specifically, the system calculates and guides the UAV to a nearby preparatory point upwind of the target sampling node. After arriving at this upwind preparatory point, the UAV adjusts its course and moves along the wind direction to directly above the target grid node or a preset sampling position, then performs hovering and sampling operations.

[0035] After the UAV hovers at each spatial grid node, it activates a multi-component gas sensor for continuous sampling. During sampling, the sensor measures the surrounding gas concentration at a constant frequency and collects raw concentration readings with a time series during single-point hovering. After filtering, these readings are output as the raw gas concentration data corresponding to that spatial grid node.

[0036] Step S2: When the UAV hovers over each spatial grid node, it scans the area around the node using the onboard positioning module and lidar to obtain the three-dimensional geographic coordinates and terrain point cloud data of the local area where the node is located. Based on the terrain point cloud data collected from all spatial grid nodes, a digital elevation model of the monitoring area is generated.

[0037] In this embodiment, the digital elevation model generation process includes the following stages. First, the raw terrain point cloud data acquired by the airborne lidar scanning when the UAV hovers at each spatial grid node is preprocessed.

[0038] The preprocessing operations sequentially include point cloud denoising, point cloud registration, and point cloud fusion. Point cloud denoising is performed on the raw point cloud acquired by LiDAR, employing a statistical outlier removal algorithm. This algorithm searches for the K nearest neighbors of each point in the point cloud and calculates the average distance between that point and all K neighbors. The algorithm then calculates the mean and standard deviation of the average distances of all points in the entire point cloud dataset. The average distance of each point is compared to the global mean; if the average distance of a point exceeds a preset standard deviation threshold, that point is identified as an outlier and filtered out of the point cloud dataset.

[0039] After denoising, point cloud registration is performed on multi-site cloud data from different spatial grid nodes. The registration process is implemented using an iterative nearest-point algorithm. This algorithm calculates and minimizes the mean square error of the distance between corresponding points in two point cloud sets to find the optimal spatial transformation matrix, thus unifying the point cloud data collected by all nodes into the same global coordinate system. Point cloud fusion then merges the registered multi-site cloud data into a complete overall point cloud dataset for the monitoring area.

[0040] Next, based on the preprocessed overall point cloud data of the monitoring area, an irregular triangular mesh model is constructed using a triangular meshing algorithm. This process employs the Delaunay triangulation algorithm, which uses each data point in the point cloud as a vertex of a triangle. According to the empty circumcircle criterion, the scattered point set is triangulated to generate a triangular mesh surface model composed of continuous but non-overlapping triangular facets, representing the continuous undulation and change of the terrain in the monitoring area.

[0041] Finally, the generated irregular triangular mesh model is subjected to rasterization interpolation calculations to produce digital elevation model grid data with a specified spatial resolution. The rasterization process divides the monitoring area into regularly arranged grid cells on the horizontal plane. For the center point of each grid cell, its elevation value is calculated using a linear interpolation algorithm from the elevation values ​​of the three vertices of the triangle patch in which its location falls. This linear interpolation is based on the principle of barycentric coordinates, determining the weights according to the positional relationship between the point to be interpolated and the three vertices of the triangle, thereby calculating the elevation value of the center point of the grid.

[0042] After traversing all grid cells and completing the interpolation calculations, a digital elevation model stored in a regular grid format is obtained. Each grid cell contains its planar coordinates and its corresponding elevation value. Simultaneously, when the UAV hovers at each node, it acquires and records the three-dimensional geographic coordinates of that node, including latitude, longitude, and altitude, in real time by fusing ranging information from the positioning module and LiDAR.

[0043] Step S3: When the UAV hovers and samples at each spatial grid node, it simultaneously starts the miniaturized gas chromatography-mass spectrometry module that integrates multi-component gas sensors to perform component separation and mass spectrometry analysis on the collected gas samples and obtain gas component fingerprint data.

[0044] In this embodiment, the component separation and mass spectrometry analysis process of the miniaturized gas chromatography-mass spectrometry module specifically includes three consecutive stages: gas sampling and injection, gas chromatography separation, and mass spectrometry detection. After the UAV arrives at the preset spatial grid node and enters a stable hovering state, the system synchronously triggers the gas analysis process. In the gas sampling and injection stage, the miniature sampling pump starts working. The gas inlet of the miniature sampling pump is connected to a steady-state flow controller through a gas pipeline.

[0045] Throughout the drone's hovering sampling phase, the steady-state flow controller continuously receives real-time atmospheric pressure and ambient temperature data from the onboard barometer and temperature sensor. The controller's built-in microprocessor dynamically calculates the volumetric flow rate compensation value required to maintain a constant mass flow rate at the column inlet under current environmental conditions, based on the ideal gas law and a preset standard flow rate setpoint, combined with the real-time collected pressure and temperature data. The controller then outputs corresponding control signals to the miniature sampling pump, adjusting its motor speed or valve opening to achieve closed-loop real-time compensation control of the gas extraction flow rate. This ensures that, during the drone's hovering phase, despite potential fluctuations in external air pressure and temperature, the gas sample flow rate entering the subsequent miniaturized gas chromatography module column remains at a constant setpoint.

[0046] A gas sample with controlled flow is loaded into the column of a miniaturized gas chromatography module. The column is filled with a stationary phase coating of specific polarity. As the mixed gas sample flows through the column under the propulsion of the carrier gas, different volatile organic compounds (VOCs) in the sample undergo repeated adsorption and desorption with the stationary phase. Due to the differences in the partition coefficients of each component between the gas and liquid phases, their migration rates in the column differ. Components with smaller partition coefficients have shorter residence times in the stationary phase and elute from the column earlier; components with larger partition coefficients have longer residence times and elute later. After separation by a sufficiently long column, the originally mixed VOCs are expanded over time, forming a sequence of components that elute from the column according to their different retention times, thus achieving time-sequential separation of the VOCs.

[0047] The separated gas components flowing from the end of the chromatographic column are sequentially introduced into the ion source of the miniature mass spectrometry module. Within the ion source, the component molecules lose electrons under the influence of high-energy electron beam bombardment or chemical ionization, forming positively charged molecular ions. Some of these molecular ions further fragment, producing a series of fragment ions with characteristic mass-to-charge ratios. These ions form an ion beam under the accelerating electric field of the ion source. The ion beam is introduced into the mass analyzer. In the electromagnetic field generated by the mass analyzer, the ions are subjected to the Lorentz force, causing their trajectories to deflect. Ions with different mass-to-charge ratios have different radii of curvature due to the difference in their mass-to-charge ratios, thus being separated spatially or temporally.

[0048] The detector scans and detects the intensity of arriving ion currents in order of mass-to-charge ratio, recording the spectrum of each component's ion current intensity as a function of its mass-to-charge ratio. The system correlates and records the chromatographic retention time of each component at a specific time point from the column with the corresponding characteristic mass spectral peak information. Each characteristic mass spectral peak contains the mass-to-charge ratio information of the component's molecular ion peak and the mass-to-charge ratio and relative abundance information of its main fragment ion peaks.

[0049] Finally, for each gas sample collected from a spatial grid node, the system generates and outputs a set of data containing the chromatographic retention times of each volatile organic compound component and its corresponding characteristic mass spectra. This set of data is a gas component fingerprint data characterizing the chemical composition of the gas at that location.

[0050] Step S4: Spatiotemporally correlate and fuse the original gas concentration data, three-dimensional geographic coordinates, and gas component fingerprint data corresponding to each spatial grid node with the digital elevation model to generate a multi-component gas distribution field with three-dimensional geographic information annotation.

[0051] In this embodiment, the generation process of the multi-component gas distribution field includes two stages: data association mapping and three-dimensional spatial interpolation. First, the three-dimensional regular grid of the digital elevation model is used as a unified spatial reference framework for the entire monitoring area. The digital elevation model consists of grid vertices arranged at fixed intervals on a horizontal plane. Each vertex contains its planar coordinates and the elevation value calculated by interpolation, resulting in a three-dimensional terrain surface covering the monitoring area.

[0052] In the data association and mapping phase, the system associates the spatiotemporally synchronized data collected by the UAV at each spatial grid node with the corresponding spatial reference framework. The system reads the three-dimensional geographic coordinates corresponding to each spatial grid node. These coordinates, calculated by fusing navigation satellite system positioning data and lidar altimetry data, include longitude, latitude, and altitude, and are used as a unique spatial index. Within the three-dimensional grid of the digital elevation model, the system searches for the grid vertex with the closest spatial index coordinates in three-dimensional space and marks this vertex as the mapping target location for the corresponding sampled data. Subsequently, the system maps the raw gas concentration data collected at the same time point at that spatial grid node, along with the gas component fingerprint data obtained from analysis by a miniaturized gas chromatography-mass spectrometry module, to the corresponding marked three-dimensional grid vertices in the digital elevation model using the established spatial index relationship. The raw gas concentration data is associated as a scalar field, while the gas component fingerprint data is associated as a multidimensional data field containing multiple component characteristics.

[0053] After completing the data mapping of all sampling nodes, the process enters the three-dimensional spatial interpolation stage, generating a gas distribution field that continuously covers the entire monitoring area in three dimensions. Since the UAV only accessed the preset spatial grid nodes, some three-dimensional grid vertices in the digital elevation model lack gas data because they are not directly covered by the nodes. To estimate the gas concentration and composition information at these unknown vertices, a three-dimensional spatial interpolation algorithm is used. In this embodiment, the three-dimensional spatial interpolation algorithm uses the inverse distance weighted interpolation method. For any three-dimensional grid vertex in the digital elevation model that is not directly covered by the sampling nodes, the interpolation is performed. Its gas concentration value or fingerprint characteristic value of a specific gas component The interpolation calculation process is as follows.

[0054] The algorithm first searches for vertices in three-dimensional space. Centered on, with a preset search radius All known 3D mesh vertices of the associated gas data within the range, these known vertices are denoted as each The vertices have been associated with gas data values ​​through data mapping. For each known vertex The algorithm calculates its relationship with the vertex to be interpolated. The three-dimensional spatial difference between them. This difference consists of two parts: one is the Euclidean distance on the horizontal plane. , i.e., vertex and The straight-line distance on a horizontal plane ignoring elevation differences; and the absolute difference in elevation dimension. , i.e., vertex and The absolute value of the difference in altitude. Vertex For vertex interpolation weights By this vertex and Horizontal distance of a point and elevation difference The decision was made jointly, and the calculation formula is as follows:

[0055] In the formula, the exponent parameter and This embodiment is used to control the decay rate of the influence of horizontal distance and elevation difference on the weight. and The value is 2. Parameter The elevation difference influence factor is a coefficient greater than 0, used to adjust the contribution ratio of elevation difference in the total weight calculation, ensuring that horizontal distance and elevation difference are comparable in terms of dimensions and degree of influence.

[0056] Calculation yields all The weights corresponding to the known vertices Then, these weights are normalized to obtain the normalized weights for each known vertex. :

[0057] Finally, the vertex to be interpolated Gas data values ​​at The following formula can be used to calculate:

[0058] This calculation is essentially a weighted average of all known vertex data values ​​with their normalized weights as coefficients. Weights The influence of the data on the interpolation result is inversely proportional to the comprehensive spatial distance from the known vertex to the vertex to be interpolated. The closer the known vertex is to the known vertex and the closer its elevation is to the known vertex, the greater its influence on the interpolation result.

[0059] The search, weight calculation, and interpolation processes described above are repeated for all 3D grid vertices in the digital elevation model that are not directly covered by the sampling nodes. Finally, each 3D grid vertex is assigned an estimated gas concentration value and component fingerprint feature value, thereby generating a multi-component gas concentration distribution field covering the entire monitoring area in three-dimensional space with continuous distribution characteristics. This distribution field uses the 3D grid of the digital elevation model as its spatial carrier, and each grid vertex contains geographical location coordinates, elevation information, and multi-component gas data fused with them.

[0060] Example 2 This invention provides a method for acquiring multi-component gas data based on unmanned aerial vehicles (UAVs). It also includes dynamically adjusting the node density of an adaptive variable resolution spatial grid, calculating the Euclidean distance from any point in the grid to the coordinates of each suspected pollution source, calculating the basic density coefficient based on the distance value using an inverse proportional function, and then weighting and fusing it with a terrain complexity factor to finally generate a spatially continuously varying node density distribution field.

[0061] When calculating the inverse distance weights, the horizontal Euclidean distance and the elevation difference in the 3D spatial interpolation algorithm are controlled by independent exponential parameters in the weighting formula. The contribution of the two to the interpolation results is balanced by an adjustable elevation difference influence factor, so as to achieve data fusion that is more in line with the characteristics of 3D geospatial space.

[0062] Example 3 This embodiment discloses a multi-component gas data acquisition system based on an unmanned aerial vehicle (UAV); like Figure 2 As shown, a multi-component gas data acquisition system based on an unmanned aerial vehicle (UAV) includes: The flight control module is used to control the UAV equipped with a multi-component gas sensor to fly according to the preset boundary coordinates of the monitoring area and hover in the monitoring area according to the preset spatial grid nodes one by one; during the hovering of each spatial grid node, the multi-component gas sensor is activated to continuously sample and obtain the raw gas concentration data of the corresponding node. The geographic information module is used to scan the area around each spatial grid node by means of the airborne positioning module and lidar when the UAV hovers over it, to obtain the three-dimensional geographic coordinates and terrain point cloud data of the local area where the node is located, and to generate a digital elevation model of the monitoring area based on the terrain point cloud data collected from all spatial grid nodes. The gas analysis module is used to simultaneously activate a miniaturized gas chromatography-mass spectrometry module integrating multi-component gas sensors when the UAV hovers and samples at each spatial grid node. This module performs component separation and mass spectrometry analysis on the collected gas samples to obtain gas component fingerprint data. The data fusion module is used to perform spatiotemporal correlation and data fusion with the original gas concentration data, three-dimensional geographic coordinates, and gas component fingerprint data corresponding to each spatial grid node and the digital elevation model to generate a multi-component gas distribution field with three-dimensional geographic information annotation.

[0063] Example 4 The purpose of this embodiment is to provide a computer-readable storage medium.

[0064] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a UAV-based multi-component gas data acquisition method as described in Example 1.

[0065] Example 5 The purpose of this embodiment is to provide an electronic device.

[0066] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in a multi-component gas data acquisition method based on an unmanned aerial vehicle as described in Embodiment 1.

[0067] The steps and methods involved in the apparatuses of Embodiments 2, 3, 4, and 5 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0068] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0069] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for collecting multi-component gas data based on a UAV, characterized in that, include: Step S1: Control the drone equipped with a multi-component gas sensor to fly according to the preset boundary coordinates of the monitoring area, and hover in the monitoring area one by one according to the preset spatial grid nodes; During the hovering period at each spatial grid node, a multi-component gas sensor is activated to continuously sample and obtain the raw gas concentration data of the corresponding node. Step S2: When the UAV hovers over each spatial grid node, it scans the area around the node using the onboard positioning module and lidar to obtain the three-dimensional geographic coordinates and terrain point cloud data of the local area where the node is located. Based on the terrain point cloud data collected from all spatial grid nodes, a digital elevation model of the monitoring area is generated. Step S3: When the UAV hovers and samples at each spatial grid node, it simultaneously starts the miniaturized gas chromatography-mass spectrometry module that integrates multi-component gas sensors to perform component separation and mass spectrometry analysis on the collected gas samples and obtain gas component fingerprint data. Step S4: Spatiotemporally correlate and fuse the original gas concentration data, three-dimensional geographic coordinates, and gas component fingerprint data corresponding to each spatial grid node with the digital elevation model to generate a multi-component gas distribution field with three-dimensional geographic information annotation.

2. The multi-component gas data collection method based on the unmanned aerial vehicle according to claim 1, wherein, In step S1, the UAV flies according to the boundary coordinates of the monitoring area, specifically as follows: Based on the boundary coordinates of the monitoring area, an adaptive variable resolution spatial grid covering the monitoring area is constructed; The node density of the adaptive variable resolution spatial grid is dynamically adjusted based on the preset coordinates of suspected pollution sources and terrain complexity data; The drone is controlled to traverse the spatial sequence of the spatial grid nodes and to execute a hovering command when it arrives at each node.

3. The method for acquiring multi-component gas data based on an unmanned aerial vehicle (UAV) as described in claim 2, characterized in that, The traversal flight according to the spatial sequence of spatial grid nodes specifically includes: During the flight of the drone, real-time wind field data is acquired through airborne wind speed and direction sensors; The flight path of the UAV to the next spatial grid node is dynamically corrected based on real-time wind field data, so that the UAV is always in the upwind position of the current sampling node before hovering and sampling.

4. The method for acquiring multi-component gas data based on an unmanned aerial vehicle (UAV) as described in claim 1, characterized in that, The digital elevation model generation process in step S2 includes: The terrain point cloud data acquired at each spatial grid node is preprocessed, including point cloud denoising, point cloud registration and point cloud fusion. Based on the preprocessed overall point cloud data, an irregular triangular mesh model is constructed using a triangular meshing algorithm. Rasterization interpolation is performed on the irregular triangular mesh model to generate digital elevation model grid data with a specified spatial resolution.

5. The method for acquiring multi-component gas data based on an unmanned aerial vehicle (UAV) as described in claim 1, characterized in that, The component separation and mass spectrometry analysis process in step S3 includes: During drone hovering sampling, ambient gas is loaded into the chromatographic column of a miniaturized gas chromatography module using a micro sampling pump; The volatile organic compounds in a gas sample are separated sequentially using a chromatographic column. The separated components are sequentially introduced into the ion source of the micro mass spectrometry module for ionization, and the mass-to-charge ratio of each component is detected by the mass analyzer to form gas component fingerprint data containing retention time and characteristic mass spectrometry peaks.

6. The method for acquiring multi-component gas data based on an unmanned aerial vehicle (UAV) as described in claim 1, characterized in that, The multi-component gas distribution field generation process in step S4 includes: Using the three-dimensional grid of the digital elevation model as the spatial reference framework, the three-dimensional geographic coordinates corresponding to each spatial grid node are used as spatial indexes; The raw gas concentration data and gas component fingerprint data collected at the same timestamp from each node are mapped to the corresponding three-dimensional grid vertices of the digital elevation model through spatial indexing. A three-dimensional spatial interpolation algorithm is used to interpolate the gas concentration and component data at the vertices of the three-dimensional grid that are not directly covered by nodes, thereby generating a multi-component gas concentration distribution field covering the entire monitoring area in three-dimensional space.

7. The method for acquiring multi-component gas data based on an unmanned aerial vehicle (UAV) as described in claim 6, characterized in that, The three-dimensional spatial interpolation algorithm is the inverse distance weighted interpolation method. When calculating the interpolation weight at the three-dimensional mesh vertex that is not directly covered by the node, it is based on the difference in horizontal Euclidean distance and elevation dimension between the vertex and the known data node.

8. A multi-component gas data acquisition system based on an unmanned aerial vehicle (UAV), characterized in that, The system is used to implement a method for acquiring multi-component gas data based on an unmanned aerial vehicle (UAV) as described in any one of claims 1 to 7, comprising: The flight control module is used to control the UAV equipped with a multi-component gas sensor to fly according to the preset boundary coordinates of the monitoring area and hover in the monitoring area according to the preset spatial grid nodes one by one; during the hovering of each spatial grid node, the multi-component gas sensor is activated to continuously sample and obtain the raw gas concentration data of the corresponding node. The geographic information module is used to scan the area around each spatial grid node by means of the airborne positioning module and lidar when the UAV hovers over it, to obtain the three-dimensional geographic coordinates and terrain point cloud data of the local area where the node is located, and to generate a digital elevation model of the monitoring area based on the terrain point cloud data collected from all spatial grid nodes. The gas analysis module is used to simultaneously activate a miniaturized gas chromatography-mass spectrometry module integrating multi-component gas sensors when the UAV hovers and samples at each spatial grid node. This module performs component separation and mass spectrometry analysis on the collected gas samples to obtain gas component fingerprint data. The data fusion module is used to perform spatiotemporal correlation and data fusion with the original gas concentration data, three-dimensional geographic coordinates, and gas component fingerprint data corresponding to each spatial grid node and the digital elevation model to generate a multi-component gas distribution field with three-dimensional geographic information annotation.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the UAV-based multi-component gas data acquisition method as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the UAV-based multi-component gas data acquisition method as described in any one of claims 1-7.

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