A method and system for synchronous monitoring of atmospheric ecological environment data

Abstract

The present invention discloses a method and system for synchronous monitoring of atmospheric ecological environment data, which relates to the field of atmospheric environment monitoring technology. The method includes: building a sky-air-ground global monitoring network to achieve global data collection and unification of spatiotemporal benchmarks; based on a hybrid interpolation model, performing hybrid interpolation and residual analysis on the atmospheric ecological environment monitoring data collected across the entire region, identifying weak monitoring areas and performing location annotation; based on the location distribution of weak monitoring areas, enabling drones to perform weak area inspection tasks, collecting weak area monitoring data through on-site monitoring, and achieving monitoring-prediction closed-loop verification; fusing measured and predicted data to reconstruct the global monitoring network, and constructing a digital twin platform through three-dimensional situation mapping to achieve global perception and synchronous monitoring of the atmospheric ecological environment. By constructing a sky-air-ground coordinated three-dimensional monitoring network, the present invention achieves the synchronous collection and dynamic integration of all elements of atmospheric pollution data, providing a seamless data base for pollution source tracing and trend prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric environment monitoring, and in particular to a method and system for synchronously monitoring atmospheric ecological environment data. Background Art

[0002] Atmospheric ecological and environmental data are the core basis for assessing air quality and pollution control effectiveness. They cover a wide range of indicators, including particulate matter (PM2.5, PM), gaseous pollutants (SO, NOx, O), volatile organic compounds (VOCs), and meteorological parameters (temperature, humidity, wind direction and speed). These data are obtained through physical, chemical, and remote sensing techniques. For example, beta-ray methods are used to measure particulate matter concentrations, while chemiluminescence and ultraviolet fluorescence methods are used to detect NOx and SO, respectively. Satellite remote sensing uses aerosol optical depth (AOD) inversion to monitor large-scale pollution distribution. These data not only reflect the temporal and spatial variations in pollutant concentrations but also enable the tracing of pollution sources through compositional analysis (such as the proportion of silicon and aluminum in PM2.5), supporting targeted measures such as coal combustion control and vehicle exhaust emissions control.

[0003] Atmospheric ecological and environmental data are the core basis for assessing environmental quality and formulating pollution control strategies. Its dynamic nature, multi-source nature, and spatial heterogeneity place higher demands on monitoring technology. Current atmospheric environmental monitoring relies primarily on a combination of ground-based monitoring stations, satellite remote sensing, and mobile monitoring equipment. Data on pollutant concentrations and meteorological parameters are acquired through physical and chemical analysis, sensor networks, and model prediction. While traditional ground-based monitoring stations can achieve continuous observations at fixed points, they are limited by insufficient spatial coverage and struggle to capture the cross-regional diffusion patterns of pollutants. While satellite remote sensing technology can provide large-scale periodic scanning, it suffers from issues such as low temporal resolution and cloud interference, limiting real-time monitoring capabilities.

[0004] With the application of the Internet of Things and artificial intelligence technologies, emerging means such as drones and micro-sensors have gradually become popular. However, problems such as inconsistent spatiotemporal benchmarks for multi-source heterogeneous data and low efficiency in identifying monitoring blind spots still restrict the overall effectiveness of the monitoring system. For example, differences in the coordinate systems of ground-based sensor networks and satellite data may cause spatial matching deviations, while differences in sampling frequencies between different devices make it difficult to synchronize and fuse time series data, affecting the accuracy of pollutant diffusion models. In addition, spatial prediction models based on a single interpolation algorithm often produce large residuals when faced with complex terrain or sudden pollution incidents due to ignoring the dynamic changes of pollution sources. Traditional residual analysis methods lack the ability to accurately locate weak monitoring areas, making it difficult to guide the optimal allocation of monitoring resources.

[0005] Although current technological development has achieved multi-dimensional data collection, there are still significant bottlenecks in data synchronization, system adaptability and real-time decision support. There is an urgent need to design a synchronous monitoring method and system for atmospheric ecological environment data. Summary of the Invention

[0006] Based on this, it is necessary to provide a method and system for synchronous monitoring of atmospheric ecological environment data to address the above technical issues.

[0007] In a first aspect, the present invention provides a method for synchronously monitoring atmospheric ecological environment data, comprising:

[0008] S1. Build a global monitoring network of space, air, and ground to achieve global data collection and unified spatiotemporal benchmarks;

[0009] S2. Based on the hybrid interpolation model, perform hybrid interpolation and residual analysis on the atmospheric ecological environment monitoring data collected across the entire region to identify weak monitoring areas and perform location annotation.

[0010] S3. Based on the location distribution of weak monitoring areas, drones are used to perform inspection tasks in weak areas. Through on-site monitoring, weak area monitoring data is collected to achieve closed-loop verification of monitoring and prediction.

[0011] S4. Integrate measured and predicted data to reconstruct the global monitoring network, build a digital twin platform through three-dimensional situation mapping, and achieve global perception and synchronous monitoring of the atmospheric ecological environment.

[0012] Furthermore, we will build a global space-air-ground monitoring network to achieve global data collection and unified spatiotemporal benchmarks, including:

[0013] S11. Build a space-based monitoring network based on remote sensing satellites, achieving full coverage and periodic scanning through multispectral, thermal infrared, and aperture radar payloads;

[0014] S12. Use mixed drone formations to achieve multi-region hierarchical monitoring and form an air-based monitoring network;

[0015] S13. Deploy distributed ground monitoring stations and use ground-based sensors to form a ground-based monitoring network;

[0016] S14. Build a hierarchical transmission and multi-source data fusion framework based on the Beidou space-time benchmark to achieve the unification of space-time benchmarks and dynamic correction of global atmospheric ecological environment monitoring data.

[0017] Furthermore, based on the hybrid interpolation model, hybrid interpolation and residual analysis are performed on the atmospheric ecological environment monitoring data collected over the entire region to identify weak monitoring areas and perform location annotation, including:

[0018] S21. Perform sparse processing on the monitoring data and divide it into several spatial grids. Each grid retains an optimal monitoring point, and marks the area not covered by the ground monitoring station as a verification area.

[0019] S22. Optimizing the range parameter and smoothness parameter of the Kriging interpolation algorithm based on the adaptive mutation chaotic particle swarm algorithm to construct a hybrid interpolation model;

[0020] S23. Perform mixed interpolation calculations on the monitoring data of the verification area using a mixed interpolation model to generate a global pollutant concentration field and confidence distribution;

[0021] S24. Perform residual analysis based on the interpolation data prediction results, dynamically identify weak monitoring areas, and mark the location information of the weak monitoring areas and the corresponding area types.

[0022] Furthermore, based on the adaptive mutation chaotic particle swarm algorithm, the range parameter and smoothness parameter of the Kriging interpolation algorithm are optimized to construct a hybrid interpolation model including:

[0023] S221, using sinusoidal chaos mapping to generate the initial position of the particle swarm and initialize the population size;

[0024] S222, mapping the range parameter and smoothness parameter of the Kriging interpolation to the two-dimensional position vector of the particle swarm, and converting the chaotic sequence value into the parameter range by linear scaling;

[0025] S223, using the cross-validated root mean square error as the fitness function, calculating the particle fitness value;

[0026] S224, setting an adaptive update strategy for the inertia weight, adjusting the dynamic weight and learning factor;

[0027] S225. When the global optimal solution has not been updated for six consecutive generations, perform Gaussian mutation on the global optimal solution;

[0028] S226. When the maximum number of iterations is reached or the convergence threshold is met, the iteration is stopped and the globally optimal parameter combination is output.

[0029] Furthermore, the mixed interpolation model is used to perform mixed interpolation calculations on the monitoring data of the verification area to generate the global pollutant concentration field and confidence distribution, including:

[0030] S231, calling the range parameter and the smoothness parameter to initialize the hybrid interpolation model, and loading the preprocessed data set, including the monitoring data of the ground monitoring station, the inverted value of the satellite data and the covariate;

[0031] S232. Generate a basic field based on ground monitoring station monitoring data, calculate the baseline value of pollutant concentration at each spatial grid point for interpolation, convert the satellite data inversion value into the spatial distribution trend of pollutants, superimpose it on the basic field, and use covariates to perform anisotropic correction on the pollution diffusion direction;

[0032] S233. Apply a Gaussian noise generation parameter combination to the range parameter and the smoothness parameter, perform interpolation calculations respectively, and count the quantile intervals of the pollutant concentration values ​​at each grid point to calculate the confidence index.

[0033] Furthermore, residual analysis is performed based on the interpolation data prediction results to dynamically identify weak monitoring areas and mark their location information and corresponding area types, including:

[0034] S241. Extract the predicted concentration of the global interpolation grid into the coordinate system of the surface monitoring station, calculate the residual of the predicted data of the grid point by presetting the differentiated trigger conditions, and mark the potential weak points;

[0035] S242, using a clustering algorithm to perform spatial clustering, performing a mathematical morphological expansion operation on the initial clustering results, synthesizing fragmented areas, and outputting a monitoring weak area containing a boundary through terrain constraints;

[0036] S243. Use terrain analysis tools to identify the location coordinates and terrain information of each weak monitoring area, and divide the area type of the weak monitoring area into drone-allowed flying areas and drone-restricted flying areas based on the terrain information.

[0037] Furthermore, by presetting differentiated trigger conditions, the residuals of the grid point prediction data are calculated, and potential weak points are marked, including:

[0038] Set the sliding time window and residual calculation period, and calculate the interpolation prediction value sequence and sliding standard deviation of N consecutive periods for the non-measured grid points in the verification area;

[0039] The interpolation prediction value sequence is filtered and divided into trend term and period term, and the ratio of the difference between the interpolation prediction value and the trend term is set as the relative residual;

[0040] Set the differentiated trigger condition. When the relative residual for M consecutive periods is greater than the preset threshold and the sliding standard deviation is greater than the preset threshold, the grid point is marked as a potential weak area point.

[0041] Furthermore, based on the location distribution of monitored weak areas, drones are enabled to perform weak area inspection tasks. Through on-site monitoring, weak area monitoring data is collected to achieve monitoring-prediction closed-loop verification, including:

[0042] S31. Based on the location coordinates and area type of the monitored weak area, generate a UAV weak area inspection task and output a waypoint coordinate set and a time-altitude profile;

[0043] S32: Dynamically configure monitoring payloads based on pollutant types in weak monitoring areas, activate drone inspections, and conduct on-site monitoring and data quality verification in weak monitoring areas;

[0044] S33. Transmitting the measured data packet through encryption, and using the measured weak area monitoring data to correct and replace the interpolation data of the grid points.

[0045] Furthermore, the monitoring payload is dynamically configured according to the pollutant type in the weak monitoring area, and the drone is launched for inspection. On-site monitoring and data quality verification of the weak monitoring area include:

[0046] S321. Configure laser scattering sensors and PID sensors on the UAV payload end, access the WRF real-time wind speed field, and dynamically adjust the flight altitude.

[0047] S322. Perform multi-point hovering monitoring within the monitoring range set by the center coordinates of the weak monitoring area, and collect minute-level environmental monitoring data in real time;

[0048] S323. Compare the measured weak zone monitoring data with the interpolation prediction data to calculate the measured relative deviation. When the measured relative deviation exceeds the preset threshold, it is marked as a high deviation area, triggering the lidar vertical profile scan to assist in pollution tracing.

[0049] In a second aspect, the present invention further provides a system for synchronously monitoring atmospheric ecological environment data, the system comprising:

[0050] The global monitoring module is used to build a global monitoring network of space, air and ground, realizing global data collection and unified space-time benchmarks;

[0051] The interpolation prediction module is used to perform mixed interpolation and residual analysis on the atmospheric ecological environment monitoring data collected across the entire region based on a mixed interpolation model, identify weak monitoring areas, and predict pollutant concentrations in weak areas;

[0052] The closed-loop verification module is used to monitor the location distribution of weak areas, enable drones to perform weak area inspection tasks, collect weak area monitoring data through on-site monitoring, and realize monitoring-prediction closed-loop verification;

[0053] The synchronous perception module is used to integrate measured and predicted data to reconstruct the global monitoring network, build a digital twin platform through three-dimensional situation mapping, and realize global perception and synchronous monitoring of the atmospheric ecological environment.

[0054] The beneficial effects of the present invention are:

[0055] 1. By building a three-dimensional monitoring network that coordinates space, air, and ground, the simultaneous collection and dynamic integration of all elements of atmospheric pollution data are achieved. Satellite remote sensing provides periodic scanning data across the entire area, drone fleets perform real-time dynamic verification of weak areas, and ground-based sensor networks complete minute-level high-frequency monitoring. Based on a unified framework of time and space benchmarks, timestamp alignment and spatial coordinate correction of multi-source data ensure the temporal and spatial consistency of cross-platform data, breaking through the temporal and spatial limitations of traditional single-point monitoring. Thanks to the synchronization mechanism of "wide-area coverage-dynamic verification-fixed-point high frequency", the core issue of temporal and spatial mismatch of monitoring data during pollutant diffusion is effectively resolved, providing a seamless data foundation for pollution tracing and trend prediction.

[0056] 2. By comparing the predictions of the hybrid interpolation model with the drone's measured data in real time, a synchronous closed-loop verification system of "prediction-verification-correction" was established. The drone dynamically adjusted its flight path and payload configuration based on the location of weak spots, performing fixed-point hovering monitoring. The measured data was transmitted back in real time via an encrypted link to drive interpolation model parameter updates. For high-deviation areas, LiDAR vertical profile scanning and pollution source correlation analysis were simultaneously triggered, forming a complete feedback chain of "spatial anomaly identification-field verification-model iteration." This enabled the two-way simultaneous optimization of monitoring data and prediction models, improving the dynamic prediction accuracy of pollutant concentration fields and the reliability of weak spot identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0058] Figure 1 This is a flow chart of a method for synchronously monitoring atmospheric ecological environment data according to an embodiment of the present invention;

[0059] Figure 2 The present invention provides a system principle block diagram of a system for synchronously monitoring atmospheric ecological environment data according to an embodiment of the present invention.

[0060] Figure numbers: 1. Global monitoring module; 2. Interpolation prediction module; 3. Closed-loop verification module; 4. Synchronous perception module. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0062] See also Figure 1 , provides a method for synchronous monitoring of atmospheric ecological environment data, including:

[0063] S1. Build a space-air-ground full-area monitoring network to achieve full-area data collection and unification of time and space benchmarks.

[0064] In the description of the present invention, building a global space-air-ground monitoring network to achieve global data collection and unified spatiotemporal references includes:

[0065] S11. Build a space-based monitoring network based on remote sensing satellites, and achieve full-area coverage and periodic scanning through multispectral, thermal infrared, and aperture radar payloads.

[0066] Specifically, high-resolution remote sensing satellites equipped with multispectral, thermal infrared, and synthetic aperture radar (SAR) sensors will enable multi-dimensional environmental data collection. The satellite's orbital parameters are optimized for a 500-700 km sun-synchronous orbit with a revisit period of ≤12 hours and a resolution of 0.3 m (optical) and 1 m (radar).

[0067] The space-based monitoring network constructed using remote sensing satellites collects data including: 1. Multispectral data: collecting blue (450nm), green (560nm), red (650nm), near-infrared (860nm) and short-wave infrared (1650nm) bands for vegetation index (NDVI), land use classification and pollutant diffusion trend analysis; 2. SAR data: using the microwave's cloud penetration characteristics to obtain surface deformation, soil moisture and flood inundation range; 3. Thermal infrared data: monitoring surface temperature anomalies, identifying thermal pollution in industrial areas and illegal discharge at night.

[0068] S12. Use mixed formations of drones to achieve multi-region hierarchical monitoring and form an air-based monitoring network.

[0069] Specifically, a mixed drone fleet can use fixed-wing and multi-rotor drones to achieve hierarchical monitoring. For example, a fixed-wing drone equipped with LiDAR can perform grid scanning to generate a digital surface model (DSM) and point cloud data for surface settlement and earthwork volume calculation, enabling wide-area screening. Multi-rotor drones equipped with multispectral cameras (such as the MicaSense RedEdge-P) and PID sensors can then be used to perform sub-meter-level refined monitoring of abnormal areas identified by satellites, enabling key verification within the area.

[0070] Drones can collect PM2.5, VOCs and SO2 concentration data through onboard sensors; achieve three-dimensional pollution source positioning through RTK / PPK positioning; the onboard Jetson Nano node performs real-time data compression and outlier filtering, and transmits key data back through the 5G-MEC edge node.

[0071] S13. Deploy distributed ground monitoring stations and use ground-based sensors to form a ground-based monitoring network.

[0072] Specifically, the ground-based monitoring network needs to integrate multiple types of sensors, covering atmospheric pollution, meteorological elements and auxiliary parameters, including the following aspects: 1. Pollutant monitoring: PM2.5 / PM10 sensor (laser scattering principle), VOCs detector (photoionization PID technology), SO2 / NO2 electrochemical sensor, ozone (O) ultraviolet absorption sensor; 2. Meteorological parameters: temperature and humidity sensor, wind speed and direction meter, atmospheric pressure sensor, precipitation monitor; 3. Auxiliary parameters: ultraviolet radiation sensor, carbon dioxide (CO) infrared absorption sensor.

[0073] Ground monitoring stations should be arranged in concentric circles, centered around pollution sources, with a focus on monitoring characteristic pollutants such as VOCs and SO2. For ecologically sensitive areas, such as nature reserves and urban green spaces, a grid-based layout should be adopted, with a focus on PM2.5, O3, and related meteorological parameters.

[0074] S14. Build a hierarchical transmission and multi-source data fusion framework based on the Beidou space-time benchmark to achieve the unification of space-time benchmarks and dynamic correction of global atmospheric ecological environment monitoring data.

[0075] Specifically, a network-wide NTP synchronization service is established based on BeiDou Time (BDT) to achieve cross-platform data timestamp alignment; a 50km-spaced ground reference station network is deployed to solve atmospheric delays and tropospheric errors in real time, and Kalman filtering is used to achieve sub-meter spatial matching of air-ground data.

[0076] S2. Based on the hybrid interpolation model, hybrid interpolation and residual analysis are performed on the atmospheric ecological environment monitoring data collected over the entire region to identify weak monitoring areas and perform location annotation.

[0077] It should be noted that the hybrid interpolation model is based on the adaptive mutation chaotic particle swarm optimization (AMCPSO) algorithm, which optimizes the kriging interpolation algorithm. Its core principle is to improve the accuracy and stability of spatial interpolation through the integration of multiple strategies. Kriging interpolation relies on the semivariogram model, which uses range and smoothness parameters to characterize spatial correlation.

[0078] Traditional kriging methods require manual parameter adjustment, are subject to subjective experience, and lack adaptability to complex terrain or dynamic pollutant diffusion scenarios. By introducing a chaotic particle swarm algorithm, a sinusoidal chaotic map is used to generate the initial particle swarm, enhancing global search capabilities. Adaptive inertia weights dynamically balance global and local optimization to avoid premature convergence. When the global optimal solution stagnates, a Gaussian mutation mechanism is triggered to force a break from the local optimum, improving parameter optimization efficiency.

[0079] Using an optimized Kriging interpolation algorithm, sparse ground-based monitoring data is integrated with satellite inversion data to generate high-resolution pollutant concentration maps, covering areas traditionally blinded by monitoring. Based on the residual sequence between interpolated predictions and measured values, combined with sliding time window analysis (e.g., if the residual exceeds a threshold for five consecutive cycles), potential weak areas are dynamically identified. Multiple sets of interpolation results are generated by parameter perturbation, and the quantile intervals of pollutant concentrations at each grid point are calculated to provide a basis for prioritizing subsequent drone inspections.

[0080] In the description of the present invention, based on the hybrid interpolation model, hybrid interpolation and residual analysis are performed on the atmospheric ecological environment monitoring data collected over the entire area to identify weak monitoring areas and perform positioning and annotation, including:

[0081] S21. Perform sparse processing on the monitoring data and divide it into several spatial grids. Each grid retains an optimal monitoring point and marks the area not covered by the ground monitoring station as the area to be verified.

[0082] S22. Based on the adaptive mutation chaotic particle swarm algorithm, the range parameter and smoothness parameter of the Kriging interpolation algorithm are optimized to construct a hybrid interpolation model.

[0083] In the description of the present invention, the range parameter and smoothness parameter of the Kriging interpolation algorithm are optimized based on the adaptive mutation chaotic particle swarm algorithm to construct a hybrid interpolation model including:

[0084] S221. Use sinusoidal chaos mapping to generate the initial position of the particle swarm and initialize the population size.

[0085] Specifically, using the sinusoidal chaotic sequence (formula: x n+1 = μ sin( πxn )) Generate the initial positions of particles, break the local aggregation that may be caused by traditional random initialization, and enhance population diversity.

[0086] The kriging range and smoothness parameters are mapped to the two-dimensional coordinates (X, Y) of the particles, and the chaotic values ​​are converted into the actual parameter range through linear scaling to ensure that the parameter search space covers the theoretical feasible domain.

[0087] S222. Mapping the range parameter and smoothness parameter of the Kriging interpolation to the two-dimensional position vector of the particle swarm, and converting the chaotic sequence value into the parameter range through linear scaling.

[0088] S223. Calculate the particle fitness value using the cross-validation root mean square error as the fitness function.

[0089] S224. Set an adaptive update strategy for the inertia weight and adjust the dynamic weight and learning factor.

[0090] Specifically, the weight is dynamically adjusted according to the iteration progress (formula: ω = ω max ( ω max ω min )× t / T , t is the current iteration number, T is the total number of times), focusing on global exploration (high ω) in the early stage and turning to local optimization (low ω) in the later stage, balancing convergence speed and accuracy.

[0091] S225. When the global optimal solution has not been updated for six consecutive generations, perform Gaussian mutation on the global optimal solution.

[0092] S226. When the maximum number of iterations is reached or the convergence threshold is met, the iteration is stopped and the globally optimal parameter combination is output.

[0093] S23. Use the mixed interpolation model to perform mixed interpolation calculations on the monitoring data of the verification area to generate the global pollutant concentration field and confidence distribution.

[0094] In the description of the present invention, the hybrid interpolation model is used to perform hybrid interpolation calculation on the monitoring data of the verification area to generate the global pollutant concentration field and confidence distribution, including:

[0095] S231. Call the range parameter and the smoothness parameter to initialize the hybrid interpolation model, and load the preprocessed data set, including the monitoring data of the ground monitoring station, the inverted value of the satellite data and the covariate.

[0096] Specifically, the Kriging interpolation parameters obtained by optimizing the adaptive mutation chaotic particle swarm optimization algorithm (AMCPSO) are called, including the range parameter (Range, which determines the range of spatial correlation) and the smoothness parameter (Smoothness, which controls the smoothness of the interpolation result).

[0097] Loading preprocessed multi-source datasets involves the following:

[0098] 1. Ground monitoring data: hourly average values ​​of pollutants such as PM2.5 and NO2 after spatial and temporal standardization, with outliers (such as interference data when humidity is greater than 85%) removed;

[0099] 2. Satellite data retrieval value: Aerosol optical depth (AOD) data after radiometric calibration and cloud mask processing, with a resolution of 1 km;

[0100] 3. Covariate data: Parameters such as wind speed vector, boundary layer height, and inversion layer thickness output by the WRF meteorological model are used to correct the direction of pollution diffusion.

[0101] S232. Generate a basic field based on the monitoring data of ground monitoring stations, calculate the baseline value of pollutant concentration at each spatial grid point for interpolation, convert the inverted value of satellite data into the spatial distribution trend of pollutants, superimpose it on the basic field, and use covariates to perform anisotropic correction on the pollution diffusion direction.

[0102] Specifically, for base field interpolation, ordinary kriging interpolation is performed based on ground-based monitoring station data to generate an initial pollutant concentration field that reflects local pollution distribution characteristics. Satellite AOD data are converted into a spatial trend term for PM2.5 concentration using a geographically weighted regression (GWR) model to eliminate the nonlinear deviation between AOD and ground-based concentrations. This trend term is then superimposed on the base field to enhance the accuracy of large-scale spatial distribution. Finally, an inverse distance weighted (IDW) correction is applied to the pollution diffusion direction, using wind speed as a weighting factor.

[0103] S233. Apply a Gaussian noise generation parameter combination to the range parameter and the smoothness parameter, perform interpolation calculations respectively, and count the quantile intervals of the pollutant concentration values ​​at each grid point to calculate the confidence index.

[0104] For example, a ±15% Gaussian noise value was applied to the range and smoothness parameters to generate 300 parameter combinations. Interpolation calculations were then performed on each of these combinations to simulate the impact of parameter uncertainty on the results. The 90th percentile interval (i.e., the 5th to 95th percentile) of the 300 concentration values ​​for each grid point was calculated, and a confidence level was calculated: confidence level = 1 median concentration value / width of the percentile interval. Finally, a density clustering algorithm (such as DBSCAN, with Eps = 300m) was used to spatially aggregate grid points with a confidence level less than 75%, outputting the boundaries and area parameters of the weak monitoring zone.

[0105] The confidence index directly reflects the reliability of the hybrid interpolation model's predictions at a specific spatial location. The confidence distribution, generated through Monte Carlo parameter perturbation simulations, reveals the fluctuation range of the pollutant concentration predictions. For example, if the median PM2.5 concentration at a grid point is 50 μg / m and the quantile interval is [45, 55], the confidence index is 0.8 (i.e., 1-10 / 50). A high confidence index (≥0.8) indicates that the prediction at that location is minimally affected by parameter perturbations and the results are stable. A low confidence index (<0.6) indicates that the model has significant uncertainty in that area, possibly due to sparse monitoring data or complex meteorological conditions, which may lead to reduced interpolation reliability.

[0106] The confidence distribution map uses spatial clustering to identify low-confidence areas (e.g., confidence levels of multiple consecutive grids < 0.7). Combining terrain data (slope, obstruction) with pollution source distribution, it accurately labels two types of weak areas: 1. Data-deficient weak areas: Due to insufficient coverage of monitoring stations, interpolation relies on extrapolation, and mobile monitoring equipment must be deployed first. 2. Model-limited weak areas: Areas with complex terrain or sudden meteorological changes require adjustment of the interpolation algorithm or introduction of additional covariates.

[0107] Therefore, the confidence distribution map can also be used to directly drive drone inspection path planning, and low-confidence areas are automatically promoted to high-priority verification targets, optimizing the efficiency of monitoring resource investment.

[0108] S24. Perform residual analysis based on the interpolation data prediction results, dynamically identify weak monitoring areas, and mark the location information of the weak monitoring areas and the corresponding area types.

[0109] In the description of the present invention, residual analysis is performed based on the interpolation data prediction results to dynamically identify weak monitoring areas, and the location information and corresponding area types of the weak monitoring areas are marked, including:

[0110] S241. Extract the predicted concentration of the global interpolation grid into the coordinate system of the surface monitoring station, calculate the residual of the grid point prediction data by presetting the differentiated trigger conditions, and mark the potential weak points.

[0111] In the description of the present invention, by presetting differentiated trigger conditions, calculating the grid point prediction data residuals, and marking potential weak points include:

[0112] S2411. Set the sliding time window and residual calculation period, and calculate the interpolation prediction value sequence and sliding standard deviation of N consecutive periods for the non-measured grid points in the verification area.

[0113] S2412. Filter the interpolation prediction value sequence to divide it into a trend term and a period term, and set the ratio of the difference between the interpolation prediction value and the trend term to the trend term as the relative residual.

[0114] S2413. Set a differentiated trigger condition. When the relative residual for M consecutive periods is greater than a preset threshold and the sliding standard deviation is greater than a preset threshold, mark the grid point as a potential weak area point.

[0115] S242. Use a clustering algorithm to perform spatial clustering, perform mathematical morphological expansion operations on the initial clustering results, synthesize fragmented areas, and output monitoring weak areas containing boundaries through terrain constraints.

[0116] Specifically, a density clustering algorithm was used to spatially aggregate the marked weak points, setting clustering parameters of a 300-meter neighborhood radius and a minimum number of three points to identify discrete clusters of outliers. A mathematical morphological dilation operation was then performed on the clustering results, horizontally expanding the weak area boundaries using a 3×3 pixel rectangular structuring element. Isolated areas smaller than 0.1 square kilometers were merged into continuous blocks.

[0117] At the same time, digital elevation model (DEM) data are superimposed to eliminate inaccessible areas with slopes exceeding 15 degrees (such as steep slopes and canyons). The corrected polygonal boundaries of weak areas not only retain the spatial continuity of pollutant diffusion, but also meet the actual terrain accessibility constraints.

[0118] S243. Use terrain analysis tools to identify the location coordinates and terrain information of each weak monitoring area, and divide the area type of the weak monitoring area into drone-allowed flying areas and drone-restricted flying areas based on the terrain information.

[0119] Specifically, terrain parameters such as elevation standard deviation, average slope and undulation of weak areas are extracted based on terrain analysis tools, and the weak areas are divided into drone-flyable areas and no-fly zones based on airspace control information (such as airport clearance areas and high-voltage line distribution).

[0120] Flyable zones must meet requirements such as slopes ≤ 15 degrees, be free of obstacles, and have no permanent no-fly restrictions. Their coordinates and pollutant types are automatically imported into the drone mission system. No-fly zones trigger the deployment of ground mobile monitoring equipment or localized optimization of model parameters. Topographical characteristics and pollution types are simultaneously recorded in the weak zone attribute table, providing data support for subsequent differentiated governance.

[0121] S3. Based on the location distribution of monitored weak areas, drones are enabled to perform weak area inspection tasks. Through field monitoring, weak area monitoring data is collected to achieve monitoring-prediction closed-loop verification.

[0122] In the description of the present invention, based on the distribution of weak zone locations, drones are activated to perform weak zone inspection tasks, and weak zone monitoring data is collected through field monitoring to achieve monitoring-prediction closed-loop verification, which includes:

[0123] S31. Based on the location coordinates and area type of the monitored weak area, generate a UAV weak area inspection mission, and output a waypoint coordinate set and a time-altitude profile.

[0124] S32. Dynamically configure monitoring payloads based on pollutant types in weak monitoring areas, start drone inspections, and conduct on-site monitoring and data quality verification in weak monitoring areas.

[0125] In the description of the present invention, the monitoring payload is dynamically configured according to the pollutant type in the weak monitoring area, the drone is activated for inspection, and the field monitoring and data quality verification of the weak monitoring area include:

[0126] S321. Configure laser scattering sensors and PID sensors on the UAV payload end, access the WRF real-time wind speed field, and dynamically adjust the flight altitude.

[0127] S322. Perform multi-point hovering monitoring within the monitoring range set at the center coordinates of the weak monitoring area, and collect minute-level environmental monitoring data in real time.

[0128] S323. Compare the measured weak zone monitoring data with the interpolation prediction data to calculate the measured relative deviation. When the measured relative deviation exceeds the preset threshold, it is marked as a high deviation area, triggering the lidar vertical profile scan to assist in pollution tracing.

[0129] S33. Transmitting the measured data packet through encryption, and using the measured weak area monitoring data to correct and replace the interpolation data of the grid points.

[0130] S4. Integrate measured and predicted data to reconstruct the global monitoring network, build a digital twin platform through three-dimensional situation mapping, and achieve global perception and synchronous monitoring of the atmospheric ecological environment.

[0131] Specifically, the real-time data from ground monitoring stations, measured values ​​from drone inspections, satellite remote sensing inversion data, and WRF meteorological model output are integrated. Through data cleaning and format standardization, the multi-source data are mapped to a unified space-time grid using a space-time alignment algorithm.

[0132] Build a 3D digital twin platform that integrates geographic information data, pollution concentration fields, meteorological fields, and monitoring equipment status. Use WebGL technology to achieve lightweight browser-side rendering, supporting pollutant concentration isosurface sectioning, historical data backtracking, and multi-layer overlay.

[0133] See also Figure 2 The present invention also provides a system for synchronously monitoring atmospheric ecological environment data, which includes:

[0134] The global monitoring module 1 is used to build a space-air-ground global monitoring network to achieve global data collection and unification of time and space benchmarks.

[0135] Interpolation prediction module 2 is used to perform mixed interpolation and residual analysis on the atmospheric ecological environment monitoring data collected over the entire region based on a mixed interpolation model, identify weak monitoring areas, and predict pollutant concentrations in weak areas.

[0136] The closed-loop verification module 3 is used to enable the drone to perform weak area inspection tasks based on the location distribution of the monitored weak areas, collect weak area monitoring data through field monitoring, and realize monitoring-prediction closed-loop verification.

[0137] Synchronous perception module 4 is used to integrate measured and predicted data to reconstruct the global monitoring network, build a digital twin platform through three-dimensional situation mapping, and realize global perception and synchronous monitoring of the atmospheric ecological environment.

[0138] In summary, with the help of the above technical solutions of the present invention, by constructing a three-dimensional monitoring network that coordinates the sky, air and ground, the synchronous collection and dynamic integration of all elements of atmospheric pollution data are realized. Satellite remote sensing provides periodic scanning data for the entire area, drone formations perform real-time dynamic verification for weak areas, and ground-based sensor networks complete minute-level high-frequency monitoring. Based on a unified framework of time and space benchmarks, the timestamp alignment and spatial coordinate correction of multi-source data ensure the temporal and spatial consistency of cross-platform data, breaking through the temporal and spatial separation limitations of traditional single-point monitoring. Thanks to the synchronization mechanism of "wide-area coverage-dynamic verification-fixed-point high frequency", the core problem of the spatial and temporal mismatch of monitoring data during the diffusion of pollutants is effectively solved, providing a seamless data base for pollution tracing and trend prediction. Through real-time comparison of the prediction results of the hybrid interpolation model with the actual measured data of the drone, a synchronous closed-loop verification system of "prediction-verification-correction" is constructed. The drone dynamically adjusts its flight path and payload configuration based on the location of the weak zone and performs fixed-point hovering monitoring. The measured data is transmitted back in real time through an encrypted link to drive the update of the interpolation model parameters. For high-deviation areas, the lidar vertical profile scan and pollution source correlation analysis are synchronously triggered to form a complete feedback chain of "spatial anomaly identification-field verification-model iteration", thereby realizing two-way synchronous optimization of monitoring data and prediction models, and improving the dynamic prediction accuracy of the pollutant concentration field and the reliability of weak zone identification.

[0139] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

Claims

1. A method for synchronously monitoring atmospheric ecological environment data, characterized in that: include: S1. Build a global monitoring network of space, air, and ground to achieve global data collection and unified spatiotemporal benchmarks; S2. Based on the hybrid interpolation model, perform hybrid interpolation and residual analysis on the atmospheric ecological environment monitoring data collected across the entire region to identify weak monitoring areas and perform location annotation. S3. Based on the location distribution of the weak monitoring area, the UAV is activated to perform the weak area inspection task, and the weak area monitoring data is collected through field monitoring to realize the monitoring-prediction closed-loop verification; including S31. Based on the location coordinates and area type of the weak monitoring area, the UAV weak area inspection task is generated, and the waypoint coordinate set and time-height profile are output; S32. According to the type of pollutants in the weak monitoring area, the monitoring payload is dynamically configured, the UAV is activated for inspection, and the weak monitoring area is monitored and the data quality verification is carried out in the field, including S321. The laser scattering sensor and PID are configured on the UAV payload end. The sensor is connected to the WRF real-time wind speed field and the flight altitude is dynamically adjusted. S322: Multi-point hovering monitoring is performed within the monitoring range set at the center coordinate of the weak monitoring area to collect minute-level environmental monitoring data in real time. S323: The measured weak area monitoring data is compared with the interpolation prediction data to calculate the measured relative deviation. When the measured relative deviation exceeds the preset threshold, it is marked as a high deviation area, triggering the lidar vertical profile scan to assist in pollution tracing. S33: The measured weak area monitoring data is used to encrypt the transmission of the measured data packet and correct and replace the interpolation data of the grid point. S4. Reconstruct the global monitoring network by integrating measured and predicted data, build a digital twin platform through three-dimensional situation mapping, and realize global perception and synchronous monitoring of the atmospheric ecological environment; including integrating real-time data from ground monitoring stations, measured values ​​from drone inspections, satellite remote sensing inversion data and WRF meteorological model output, through data cleaning and format standardization, using spatiotemporal alignment algorithms to map multi-source data to a unified spatiotemporal grid, build a three-dimensional digital twin platform, integrate geographic information data, pollution concentration fields, meteorological fields and monitoring equipment status, and realize lightweight rendering on the browser side through WebGL technology, supporting pollutant concentration isosurface sectioning, historical data backtracking and multi-layer overlay.

2. The method for synchronously monitoring atmospheric ecological environment data according to claim 1, characterized in that: The establishment of a global space-air-ground monitoring network to achieve global data collection and unified spatiotemporal benchmarks includes: S11. Build a space-based monitoring network based on remote sensing satellites, achieving full coverage and periodic scanning through multispectral, thermal infrared, and aperture radar payloads; S12. Use mixed drone formations to achieve multi-region hierarchical monitoring and form an air-based monitoring network; S13. Deploy distributed ground monitoring stations and use ground-based sensors to form a ground-based monitoring network; S14. Build a hierarchical transmission and multi-source data fusion framework based on the Beidou space-time benchmark to achieve the unification of space-time benchmarks and dynamic correction of global atmospheric ecological environment monitoring data.

3. The method for synchronously monitoring atmospheric ecological environment data according to claim 1, characterized in that: The hybrid interpolation model is used to perform hybrid interpolation and residual analysis on the atmospheric ecological environment monitoring data collected over the entire region, identify weak monitoring areas, and perform positioning and annotation, including: S21. Perform sparse processing on the monitoring data and divide it into several spatial grids. Each grid retains an optimal monitoring point, and marks the area not covered by the ground monitoring station as a verification area. S22. Optimizing the range parameter and smoothness parameter of the Kriging interpolation algorithm based on the adaptive mutation chaotic particle swarm algorithm to construct a hybrid interpolation model; S23. Perform mixed interpolation calculations on the monitoring data of the verification area using a mixed interpolation model to generate a global pollutant concentration field and confidence distribution; S24. Perform residual analysis based on the interpolation data prediction results, dynamically identify weak monitoring areas, and mark the location information of the weak monitoring areas and the corresponding area types.

4. A method for synchronously monitoring atmospheric ecological environment data according to claim 3, characterized in that: The optimization of the range parameter and smoothness parameter of the Kriging interpolation algorithm based on the adaptive mutation chaotic particle swarm algorithm to construct a hybrid interpolation model includes: S221, using sinusoidal chaos mapping to generate the initial position of the particle swarm and initialize the population size; S222, mapping the range parameter and smoothness parameter of the Kriging interpolation to the two-dimensional position vector of the particle swarm, and converting the chaotic sequence value into the parameter range by linear scaling; S223, using the cross-validated root mean square error as the fitness function, calculating the particle fitness value; S224, setting an adaptive update strategy for the inertia weight, adjusting the dynamic weight and learning factor; S225. When the global optimal solution has not been updated for six consecutive generations, perform Gaussian mutation on the global optimal solution; S226. When the maximum number of iterations is reached or the convergence threshold is met, the iteration is stopped and the globally optimal parameter combination is output.

5. The method for synchronously monitoring atmospheric ecological environment data according to claim 3, characterized in that: The method of using the hybrid interpolation model to perform hybrid interpolation calculation on the monitoring data of the verification area to generate the global pollutant concentration field and confidence distribution includes: S231, calling the range parameter and the smoothness parameter to initialize the hybrid interpolation model, and loading the preprocessed data set, including the monitoring data of the ground monitoring station, the inverted value of the satellite data and the covariate; S232. Generate a basic field based on ground monitoring station monitoring data, calculate the baseline value of pollutant concentration at each spatial grid point for interpolation, convert the satellite data inversion value into the spatial distribution trend of pollutants, superimpose it on the basic field, and use covariates to perform anisotropic correction on the pollution diffusion direction; S233. Apply a Gaussian noise generation parameter combination to the range parameter and the smoothness parameter, perform interpolation calculations respectively, and count the quantile intervals of the pollutant concentration values ​​at each grid point to calculate the confidence index.

6. The method for synchronously monitoring atmospheric ecological environment data according to claim 3, characterized in that: The residual analysis based on the interpolation data prediction results is performed to dynamically identify weak monitoring areas and mark the location information and corresponding area types of the weak monitoring areas. S241. Extract the predicted concentration of the global interpolation grid to the ground monitoring station coordinate system, calculate the residual of the grid point prediction data by presetting the differentiated trigger conditions, and mark the potential weak points; S242, using a clustering algorithm to perform spatial clustering, performing a mathematical morphological expansion operation on the initial clustering results, synthesizing fragmented areas, and outputting a monitoring weak area containing a boundary through terrain constraints; S243. Use terrain analysis tools to identify the location coordinates and terrain information of each weak monitoring area, and divide the area type of the weak monitoring area into drone-allowed flying areas and drone-restricted flying areas based on the terrain information.

7. A method for synchronously monitoring atmospheric ecological environment data according to claim 6, characterized in that: The aforementioned methods of calculating the residuals of the grid point prediction data and marking potential weak points by presetting differentiated trigger conditions include: Set the sliding time window and residual calculation period, and calculate the interpolation prediction value sequence and sliding standard deviation of N consecutive periods for the non-measured grid points in the verification area; The interpolation prediction value sequence is filtered and divided into trend term and period term, and the ratio of the difference between the interpolation prediction value and the trend term is set as the relative residual; Set the differentiated trigger condition. When the relative residual for M consecutive periods is greater than the preset threshold and the sliding standard deviation is greater than the preset threshold, the grid point is marked as a potential weak area point.

8. A system for synchronously monitoring atmospheric ecological environment data, for implementing the method for synchronously monitoring atmospheric ecological environment data according to any one of claims 1 to 7, characterized in that: The system includes: The global monitoring module is used to build a global monitoring network of space, air and ground, realizing global data collection and unified space-time benchmarks; The interpolation prediction module is used to perform mixed interpolation and residual analysis on the atmospheric ecological environment monitoring data collected across the entire region based on a mixed interpolation model, identify weak monitoring areas, and predict pollutant concentrations in weak areas; The closed-loop verification module is used to monitor the location distribution of weak areas, enable drones to perform weak area inspection tasks, collect weak area monitoring data through on-site monitoring, and realize monitoring-prediction closed-loop verification; The synchronous perception module is used to integrate measured and predicted data to reconstruct the global monitoring network, build a digital twin platform through three-dimensional situation mapping, and realize global perception and synchronous monitoring of the atmospheric ecological environment.

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