Ozone precursor monitoring and early warning method and system based on satellite remote sensing recognition

Abstract

The invention discloses an ozone precursor monitoring and early warning method and system based on satellite remote sensing recognition, particularly relates to the technical field of environment monitoring, and is used for solving the problem that in the prior art, a remote sensing satellite is difficult to capture daytime dynamic changes of ozone precursors due to limitation of orbits and observation time periods. The method comprises the following steps: acquiring remote sensing image data of a plurality of satellite platforms in different observation periods, and executing target recognition and pixel separation by adopting a spectral unmixing algorithm; calculating and correcting the signal deviation of the track overlapping region by using the cross consistency; generating continuous ozone precursor distribution data in combination with a multi-period interpolation algorithm; performing emission source analysis through an atmospheric chemical equilibrium model to generate concentration evolution information; an abnormal value is marked based on a dynamic monitoring threshold value, and a monitoring and early warning signal is generated by combining a geographic information system, so that high-precision dynamic monitoring and real-time early warning of the ozone precursor are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring. More specifically, the present invention relates to a method and system for monitoring and warning ozone precursors based on satellite remote sensing identification. Background Art

[0002] With the intensification of global environmental pollution problems, ozone, as an important air pollutant, has had a significant impact on the ecosystem and human health. The formation of ozone is closely related to the emissions of its precursors (such as nitrogen oxides and volatile organic compounds). Therefore, the accurate monitoring and dynamic warning of ozone precursors have become key technical requirements in environmental protection. Currently, satellite remote sensing technology has been widely used in the monitoring of air pollutants due to its wide coverage and strong observation ability. However, due to the strong spatio-temporal variation characteristics of ozone precursors, existing remote sensing monitoring methods still have deficiencies in dynamically capturing the distribution characteristics of ozone precursors.

[0003] In the prior art, due to the limitations of the orbit and observation period of remote sensing satellites, it is difficult to comprehensively capture the dynamic variation characteristics of ozone precursors at different day and night times, which will lead to insufficient representativeness of monitoring data and thus affect the accuracy of the air pollution warning system. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method and system for monitoring and warning ozone precursors based on satellite remote sensing identification to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for monitoring and warning ozone precursors based on satellite remote sensing identification, comprising the following steps:

[0007] S1: Obtain the original remote sensing image data of multiple satellite platforms at different observation times, and perform target recognition and pixel separation processing using a spectral unmixing algorithm;

[0008] S2: Analyze the overlapping effect of the overlapping areas within the observation range of multiple satellite orbits, correct the signal deviation caused by orbit differences through cross-consistency calculation, and generate corrected remote sensing image data;

[0009] S3: Use a multi-temporal interpolation algorithm to fuse the corrected remote sensing image data obtained at different times and different orbits to obtain continuous ozone precursor distribution data;

[0010] S4: Based on the continuous ozone precursor distribution data, use an atmospheric chemical equilibrium model to perform emission source analysis and generate the ozone precursor concentration evolution information of each region;

[0011] S5: Compare the hourly evolution information of ozone precursor concentrations based on the monitoring threshold, and mark abnormal values;

[0012] S6: Combine the marked abnormal values with the geographic information system to generate a monitoring and warning signal.

[0013] In a preferred embodiment, obtain the original remote sensing image data of multiple satellite platforms at different observation times, and perform target recognition and pixel separation processing, specifically including:

[0014] Analyze the orbital information of multiple satellite platforms to determine the observation time period and spatial coverage of each satellite platform;

[0015] Dispatch satellite platforms according to the orbital information to collect the original remote sensing image data at different observation times;

[0016] Preprocess the obtained original remote sensing image data;

[0017] Process the preprocessed original remote sensing image data to separate the target signal and the background signal, and extract the spectral characteristic information of ozone precursors within the pixel;

[0018] Classify the pixels in the remote sensing image data according to the spectral characteristic information of ozone precursors, and identify the target areas containing ozone precursor signals.

[0019] In a preferred embodiment, perform an overlap effect analysis on the overlapping areas of multiple satellite orbits within the observation range, correct the signal deviation caused by orbital differences through cross-consistency calculation, and generate corrected remote sensing image data, specifically including:

[0020] Obtain the remote sensing image data of multiple satellite platforms within the overlapping observation range, and determine the spatial boundary of the overlapping area;

[0021] Based on the orbital parameters and satellite observation angles, analyze the deviation types of the observation data of different orbits in the overlapping area, including spatial deviation, spectral deviation, and radiation intensity deviation;

[0022] Use the cross-consistency calculation method to perform multi-dimensional correction on the remote sensing image data within the overlapping area, including spatial registration correction, spectral distribution correction, and radiation intensity normalization;

[0023] Integrate the corrected overlapping area data with the non-overlapping area data to generate the corrected complete remote sensing image data.

[0024] In a preferred embodiment, use the multi-temporal interpolation algorithm to fuse the corrected remote sensing image data obtained at different times and different orbits to obtain continuous ozone precursor distribution data, specifically including:

[0025] Group the corrected remote sensing image data by time period, and divide the remote sensing image data within different time periods into multiple time series datasets in chronological order;

[0026] Based on the orbital parameters and observation range, perform spatial consistency adjustment on the remote sensing image data obtained from different orbits in the orbital intersection area to ensure that the spatial coverage ranges of the data from different orbits are aligned;

[0027] Adopt a multi-period interpolation algorithm to construct a continuous time series model for the remote sensing image data of each period, where the interpolation algorithm includes time interpolation and spatial interpolation, and is used to fill in the missing period and spatial observation data;

[0028] Perform time series fusion on the interpolated remote sensing image data, comprehensively process the remote sensing image data within a continuous time period, and generate continuous ozone precursor distribution data with temporal and spatial consistency.

[0029] In a preferred embodiment, based on the continuous ozone precursor distribution data, use an atmospheric chemical equilibrium model to perform emission source analysis and generate the evolution information of the ozone precursor concentration in each region, specifically including:

[0030] Divide the continuous ozone precursor distribution data into spatial grids in different regions, and each spatial grid represents a fixed spatial unit;

[0031] According to the spatio-temporal distribution characteristics within the spatial grid, extract the time series data of the ozone precursor concentration in each spatial unit;

[0032] Combine the atmospheric chemical equilibrium model and input the meteorological parameters related to each spatial grid, including temperature, humidity, wind speed, and solar radiation intensity;

[0033] Based on the atmospheric chemical equilibrium equation, calculate the generation, transformation, and diffusion rates of ozone precursors within the spatial grid;

[0034] Identify the emission source distribution of ozone precursors through a tracing algorithm, and map the analysis results to the corresponding spatial units to generate the evolution information of the ozone precursor concentration in each region.

[0035] In a preferred embodiment, compare the evolution information of the ozone precursor concentration hour by hour based on the monitoring threshold and mark abnormal values, specifically including:

[0036] Set the monitoring threshold according to historical data and real-time monitoring data. The monitoring threshold includes a time threshold and a regional threshold. The time threshold represents the early warning threshold range for different time periods, and the regional threshold represents the early warning threshold range for different regions;

[0037] Extract hourly concentration change data from the evolution information of ozone precursor concentrations. The hourly concentration change data includes the ozone precursor concentrations in each region during each monitoring period.

[0038] Compare the hourly concentration change data with the corresponding time threshold and regional threshold.

[0039] Mark the abnormal values in the hourly concentration change data that exceed the monitoring threshold, and record the time period and regional information corresponding to the abnormal values.

[0040] In a preferred embodiment, combine the marked abnormal values with a geographic information system to generate a monitoring warning signal, specifically including:

[0041] Match the marked abnormal values with the spatial location data in the geographic information system. The matching data includes the time period, regional identifier, and specific geographic coordinates corresponding to the abnormal values.

[0042] Map the abnormal values to the spatial layer of the geographic information system according to the matching results to generate a spatial distribution map of the abnormal areas.

[0043] Mark the specific information of the abnormal values on the spatial distribution map, including the abnormal concentration value, the range exceeding the threshold, and the time period when the abnormality occurred.

[0044] Generate a monitoring warning signal based on the spatial distribution map. The warning signal includes the geographical location of the abnormal area, the time of the abnormality, and a detailed description of the abnormal concentration.

[0045] On the other hand, the present invention provides an ozone precursor monitoring and warning system based on satellite remote sensing identification, including a data acquisition module, an overlap correction module, a data fusion module, an emission analysis module, an abnormal marking module, and a warning generation module.

[0046] Data acquisition module: Acquire the original remote sensing image data of multiple satellite platforms at different observation time periods, and perform target recognition and pixel separation processing using a spectral unmixing algorithm.

[0047] Overlap correction module: Analyze the overlap effect of the overlapping areas within the observation range of multiple satellite orbits, and correct the signal deviation caused by orbit differences through cross-consistency calculation to generate corrected remote sensing image data.

[0048] Data fusion module: Use a multi-time interpolation algorithm to fuse the corrected remote sensing image data obtained at different time periods and different orbits to obtain continuous ozone precursor distribution data.

[0049] Emission analysis module: Based on the continuous ozone precursor distribution data, use an atmospheric chemical equilibrium model to perform emission source analysis and generate the evolution information of ozone precursor concentrations in each region.

[0050] Abnormal Marking Module: Compare the hourly evolution information of ozone precursor concentrations based on monitoring thresholds, and mark abnormal values;

[0051] Early Warning Generation Module: Combine the marked abnormal values with a geographic information system to generate monitoring early warning signals.

[0052] Technical effects and advantages of a method and system for monitoring and early warning of ozone precursors based on satellite remote sensing identification in the present invention:

[0053] 1. By obtaining the original remote sensing image data of multiple satellite platforms at different observation times and combining spectral unmixing algorithms to identify and separate pixels of the target, the limitations of a single satellite platform's observation in a fixed time period are overcome. Through the analysis of the overlapping effects of the overlapping areas of multiple satellite orbits and the use of cross-consistency calculations to correct the signal deviations caused by orbital differences, the fusion quality of multi-source data is significantly improved, ensuring the consistency of remote sensing image data in the spatial dimension. At the same time, using the multi-temporal interpolation algorithm to perform temporal fusion on the corrected data of different times and different orbits, continuous ozone precursor distribution data is generated, thus realizing the comprehensive capture of the spatio-temporal dynamic characteristics of ozone precursors and laying a solid data foundation for subsequent air pollution analysis and early warning.

[0054] 2. Further, through the atmospheric chemical equilibrium model, the emission source analysis of the continuous ozone precursor distribution data is carried out to generate the concentration evolution information of ozone precursors in each region, accurately reflecting the processes of pollutant generation, diffusion, and transformation. By comparing the concentration evolution information hour by hour based on dynamic monitoring thresholds, marking abnormal values, and combining with a geographic information system to generate monitoring early warning signals, the present invention not only realizes the accurate positioning of abnormal areas of ozone precursors but also can issue early warning signals in a timely manner, effectively improving the accuracy and efficiency of air pollution risk monitoring. The method proposed in the present invention can more comprehensively reflect the distribution characteristics of ozone precursors under diurnal dynamic changes, making up for the deficiencies of the prior art in spatio-temporal coverage and data dynamic capture, and providing important technical support for air environmental governance and scientific decision-making. Brief Description of the Drawings

[0055] Figure 1 Schematic diagram of a method for monitoring and early warning of ozone precursors based on satellite remote sensing identification in the present invention;

[0056] Figure 2 Schematic diagram of the structure of a system for monitoring and early warning of ozone precursors based on satellite remote sensing identification in the present invention. Detailed Embodiments

[0057] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0058] Embodiment 1: Figure 1 A method for monitoring and warning ozone precursors based on satellite remote sensing identification according to the present invention is provided, which includes the following steps:

[0059] S1: Obtain the original remote sensing image data of multiple satellite platforms at different observation times, and perform target recognition and pixel separation processing using a spectral unmixing algorithm.

[0060] S2: Analyze the overlapping areas within the observation range of multiple satellite orbits, correct the signal deviation caused by orbit differences through cross-consistency calculation, and generate corrected remote sensing image data.

[0061] S3: Use a multi-temporal interpolation algorithm to fuse the corrected remote sensing image data obtained at different times and different orbits to obtain continuous ozone precursor distribution data.

[0062] S4: Based on the continuous ozone precursor distribution data, use an atmospheric chemical equilibrium model to perform emission source analysis and generate the ozone precursor concentration evolution information of each region.

[0063] S5: Compare the ozone precursor concentration evolution information hour by hour based on the monitoring threshold and mark abnormal values.

[0064] S6: Combine the marked abnormal values with a geographic information system to generate a monitoring and warning signal.

[0065] Obtain the original remote sensing image data of multiple satellite platforms at different observation times and perform target recognition and pixel separation processing, which specifically includes:

[0066] Analyze the orbit information of multiple satellite platforms to determine the observation time and spatial coverage range of each satellite platform:

[0067] Systematically analyze the orbit information of multiple remote sensing satellite platforms to obtain the orbit parameters of each satellite platform, including the observation time, spatial coverage range, and the specific coordinate range of the orbit intersection area. By analysis, the observation priority of each satellite and the possible overlapping observation areas are obtained, providing a basis for subsequent data scheduling.

[0068] Schedule satellite platforms according to the orbit information and collect the original remote sensing image data at different observation times:

[0069] Schedule relevant satellite platforms for remote sensing data acquisition based on the orbital information of the satellite platforms. Priority is given to satellite platforms with high temporal resolution and high spatial resolution to ensure the observation requirements for different periods are covered. In specific operations, adjust the satellite shooting mode according to the ability of the ground station to receive satellite data, and collect the original remote sensing image data covering the target area.

[0070] Preprocess the acquired original remote sensing image data, including data denoising, radiometric correction, and geometric correction, to eliminate the interference of the observation environment on the image data:

[0071] Data denoising: Use a denoising algorithm to eliminate the background noise and high-frequency interference signals in the original data;

[0072] Radiometric correction: Standardize the radiometric deviation in the image caused by differences in lighting conditions and sensor sensitivity;

[0073] Geometric correction: For the geometric distortion caused by satellite orbital motion, use georegistration technology to align the image with geographical coordinates to ensure that the spatial accuracy of the image meets the requirements of subsequent processing.

[0074] Process the preprocessed original remote sensing image data, separate the target signal from the background signal, and extract the spectral feature information of ozone precursors within the pixel:

[0075] Target and background signal separation: Based on the spectral characteristics of the remote sensing image data, separate the target signal from the background signal in the image;

[0076] Spectral feature extraction: Extract the spectral feature information related to ozone precursors in the image pixels, including key parameters such as reflectance and absorption peak position;

[0077] Accurate calculation: Based on the separation and extraction results, calculate the proportions of various substances in the pixel, especially the spectral component proportion of ozone precursors.

[0078] Classify the pixels in the remote sensing image data according to the spectral feature information of ozone precursors, and identify the target areas containing ozone precursor signals:

[0079] Classify each pixel in the remote sensing image data to determine whether it contains ozone precursor signals. For pixels with significant ozone precursor characteristics shown in the classification results, further combine them with the spatial position information in the image to label the target areas. Finally, generate the recognition results of the target areas for subsequent data correction and dynamic distribution analysis.

[0080] Perform an overlapping effect analysis on the overlapping areas of multiple satellite orbits within the observation range, correct the signal deviation caused by orbital differences through cross-consistency calculation, and generate corrected remote sensing image data, specifically including:

[0081] Obtain the remote sensing image data of multiple satellite platforms within the overlapping observation range, and determine the spatial boundary of the overlapping area:

[0082] First, for the overlapping areas of multiple satellite platforms within the observation range, obtain their remote sensing image data. Specifically, it is necessary to receive the original remote sensing data of each satellite platform through a ground station, and determine the spatial range of the overlapping area according to the parameter information of the satellite's orbit. The determination of the spatial range needs to combine orbital parameters, including information such as the inclination angle, altitude, orbital intersection point, and coverage range of the satellite, to ensure that the boundary of the overlapping area is accurately defined. To this end, a spatial geometric model can be constructed to calculate the boundary coordinates of the overlapping area, ensuring that the obtained overlapping area image data has a consistent spatial scale.

[0083] Based on the orbital parameters and satellite observation angles, analyze the deviation types of the observation data of different orbits in the overlapping area, including spatial deviation, spectral deviation, and radiation intensity deviation:

[0084] For the obtained remote sensing image data of the overlapping area, it is necessary to combine the orbital parameters and observation angles of the satellite to analyze the specific types of signal deviation. The deviation types include but are not limited to the following three categories:

[0085] Spatial deviation: Due to the different orbital altitudes and observation angles of each satellite platform, there is a spatial registration error in the image. Such deviation is mainly manifested as the inconsistency of the pixel positions within the overlapping area.

[0086] Spectral deviation: The difference in the spectral response characteristics of different satellite sensors will cause the offset of the spectral signal, specifically including the fluctuation of the reflectance value and the drift of the spectral range.

[0087] Radiation intensity deviation: Affected by the sensor sensitivity and external observation conditions (such as solar radiation intensity, cloud thickness, etc.), there are differences in the radiation intensity of the image data obtained from different orbits.

[0088] By analyzing the deviation types, provide an input basis for subsequent multi-dimensional correction.

[0089] Adopt the cross-consistency calculation method to perform multi-dimensional correction on the remote sensing image data within the overlapping area, including spatial registration correction, spectral distribution correction, and radiation intensity normalization:

[0090] For spatial deviation, a georegistration algorithm is used. Using the common control points in the overlapping area as reference points, by calculating the translation, rotation, and scaling parameters of the pixels in the remote sensing image, spatial consistency correction is achieved. The correction formula is: P′(x′, y′) = R·P(x, y) + T; where P(x, y) is the pixel coordinate before correction, P′(x′, y′) is the pixel coordinate after correction, R represents the rotation matrix (used to describe the angle and direction of image rotation), and T represents the translation vector (used to adjust the position deviation of the pixel).

[0091] For spectral deviation, a spectral matching algorithm is adopted. By fitting the offset of the spectral response curve, the spectral values of the image data of each orbit are normalized. The correction formula is: S′(i, λ) = S(i, λ)·C(λ); where S′(i, λ) is the spectral value of the pixel after correction, S(i, λ) represents the spectral value of the pixel before correction, C(λ) represents the correction factor (used to adjust the spectral offset), i represents the pixel index in the remote sensing image (used to identify the specific pixel position in the image data), and λ represents the spectral wavelength (used to describe the spectral characteristics of different bands in the remote sensing image).

[0092] For radiation intensity deviation, a radiation normalization method is used. By establishing a radiation intensity normalization model, the radiation values of the pixels in the image are uniformly corrected; in the normalization correction formula, the normalized radiation value of the pixel is calculated through the formula, which is to subtract the minimum radiation value within the observation range from the radiation value before correction, and then divide by the difference between the maximum radiation value and the minimum radiation value. The normalized radiation value in the formula represents the result after correction, the radiation value before correction is the original data, and the maximum and minimum radiation values are used to determine the normalization range.

[0093] Integrate the corrected data of the overlapping area with the data of the non-overlapping area to generate the corrected complete remote sensing image data:

[0094] Integrate the image data of the overlapping area that has been corrected in multiple dimensions with the image data of the non-overlapping area to ensure the consistency of the image data in dimensions such as space, spectrum, and radiation intensity. During the integration process, the weight distribution method is used to fuse the data of the overlapping area and the non-overlapping area to ensure the smooth transition of the image data.

[0095] Use the multi-temporal interpolation algorithm to fuse the corrected remote sensing image data obtained at different times and different orbits to obtain continuous ozone precursor distribution data, specifically including:

[0096] Group the corrected remote sensing image data by time period, and divide the remote sensing image data within different time periods into multiple time-series data sets in chronological order:

[0097] Before starting the fusion, first perform time period grouping on the remotely sensed image data that has been corrected. By analyzing the time stamp information of the remotely sensed image data, the image data within different time periods are divided into multiple consecutive time series data sets. Each time series data set is arranged in order according to the acquisition time of the images, ensuring a clear time series relationship after data grouping. The specific grouping method is as follows:

[0098] First, extract the time stamp information of the remotely sensed image. The format of the time stamp is standard time.

[0099] Sort the data according to the time stamp and divide it into multiple equally spaced time periods, for example, group by hour, day or week intervals.

[0100] Generate a grouping index table to record the start and end times and image data indexes of each time series data set, ensuring the traceability of the grouping information.

[0101] Based on the orbital parameters and observation range, perform spatial consistency adjustment on the remotely sensed image data obtained from different orbits to ensure the alignment of the spatial coverage ranges of the data from different orbits:

[0102] For the remotely sensed image data obtained from different orbits, due to the different orbital parameters (such as altitude, inclination, observation angle) of each satellite platform, it may lead to inconsistent spatial coverage ranges of the data within the orbital intersection area. Before fusion, it is necessary to perform spatial consistency adjustment on the remotely sensed image data within the orbital intersection area. The specific adjustment method is as follows:

[0103] First, calculate the overlapping area of the images according to the orbital parameters. The boundary coordinates of the overlapping area are defined by a geometric model.

[0104] Use georegistration technology to spatially align the image data from different orbits.

[0105] Through the above adjustment method, ensure the consistency of the spatial range of the orbital intersection area, and eliminate the spatial deviation between the data for the fusion step.

[0106] Adopt a multi-time period interpolation algorithm to construct a continuous time series model for the remotely sensed image data of each time period. The interpolation algorithm includes time interpolation and spatial interpolation, which are used to fill in the missing time periods and spatial observation data:

[0107] After completing the time period grouping and spatial consistency adjustment, adopt a multi-time period interpolation algorithm to construct a continuous time series model. The interpolation algorithm includes two parts: time interpolation and spatial interpolation, which are used to supplement the missing time periods and spatial data to ensure the continuity and integrity of the final data. The specific algorithm includes the following steps:

[0108] For the data missing points in the time series, use a linear interpolation algorithm to fill them. The linear interpolation formula is as follows: Among them, V t represents the numerical value of the interpolation time point, V 1 , V 2 represents the data values of two adjacent known time points, t 1 , t 2 represents two adjacent known time points, and t represents the interpolation time point.

[0109] For the data blank area in the spatial distribution, the inverse distance weighted interpolation method is used for filling; the formula is as follows: Among them, w u is determined by the distance, and the formula is: Among them, V(x, y) represents the interpolation result at the target position, V u represents the numerical value of the known data point, d u represents the distance between the target position and the known data point, p is based on the distance weight index, U represents the total number of known data points participating in the interpolation calculation, and w u represents the weight value (inversely proportional to the distance).

[0110] Perform temporal fusion on the interpolated remote sensing image data, comprehensively process the remote sensing image data within a continuous time period, and generate continuous ozone precursor distribution data with temporal and spatial consistency:

[0111] After interpolation, perform temporal fusion on the image data generated in the continuous time series model. The fusion process includes the following steps:

[0112] First, smooth the image data within each time period to eliminate the discontinuity between adjacent time periods.

[0113] Secondly, use the weighted average method to combine the interpolation results of multiple time periods to generate the final continuous ozone precursor distribution data: Use the weighted average method to comprehensively process the interpolation data of multiple time periods to generate ozone precursor distribution data with temporal and spatial continuity. The specific method includes the following steps: First, organize the interpolation data of each time period into a numerical matrix corresponding to the spatial coordinates, and each matrix represents the ozone precursor distribution situation of that time period. Secondly, according to the characteristics of each time period, such as data quality, time interval, and integrity of the observation range, assign weights to each time period. The weight value reflects the importance and reliability of the data, and the time period with a higher weight has a greater impact on the final result. Finally, comprehensively calculate the data of all time periods according to the assigned weights and fuse them into a complete spatio-temporal distribution data matrix.

[0114] Through temporal fusion, the finally generated ozone precursor distribution data has temporal and spatial continuity, laying a data foundation for subsequent emission source analysis and early warning analysis.

[0115] Based on the continuous ozone precursor distribution data, an atmospheric chemical equilibrium model is used for emission source analysis to generate the evolution information of ozone precursor concentrations in each region, specifically including:

[0116] The continuous ozone precursor distribution data is divided into spatial grids of different regions, and each spatial grid represents a fixed spatial unit:

[0117] According to the spatial distribution range of the continuous ozone precursor distribution data, it is divided into multiple spatial grids using a grid division algorithm. Each spatial grid represents a fixed spatial unit. The grid division is based on the geographic coordinate system, with longitude and latitude as the basis, ensuring that the area of each grid is fixed and there is no overlap. The size of the grid is adjusted according to the monitoring accuracy requirements. For example, a spatial resolution of 1 square kilometer or smaller can be selected. The purpose of gridification is to discretize the complex continuous distribution data for subsequent analysis steps.

[0118] According to the spatio-temporal distribution characteristics within the spatial grid, the time series data of ozone precursor concentrations in each spatial unit is extracted:

[0119] After the grid division is completed, according to the geographical boundaries of each spatial grid, the time series data of ozone precursor concentrations within each grid is extracted from the continuous ozone precursor distribution data. The time series data of each spatial grid consists of continuous observations in the time dimension, representing the change of ozone precursor concentration within the grid over time. When extracting the time series data, the accuracy of the grid boundary needs to be ensured to avoid data confusion between adjacent grids.

[0120] Combined with the atmospheric chemical equilibrium model, meteorological parameters related to each spatial grid are input, including temperature, humidity, wind speed, and solar radiation intensity:

[0121] For each spatial grid, meteorological parameters of the atmospheric chemical equilibrium model related to it are input, including temperature, humidity, wind speed, solar radiation intensity, etc. These parameters are obtained through meteorological observation data or model prediction data, and the meteorological parameters of each grid need to be fully matched with its time and space dimensions. For example: Temperature represents the atmospheric temperature at a certain time point within the grid, with the unit of degrees Celsius; Humidity represents the relative humidity within the grid, with the unit of percentage; Wind speed represents the horizontal wind speed of the atmosphere within the grid, with the unit of meters per second; Solar radiation intensity represents the solar radiation energy at a certain time point within the grid, with the unit of watts per square meter.

[0122] These parameters are used as input variables of the atmospheric chemical equilibrium model to ensure that the model calculation results are consistent with the actual atmospheric conditions.

[0123] Based on the atmospheric chemical equilibrium equation, the generation, transformation, and diffusion rates of ozone precursors within the spatial grid are calculated:

[0124] After inputting meteorological parameters and time-series data within the grid, the generation, transformation, and diffusion rates of ozone precursors within the grid are calculated using the atmospheric chemical equilibrium equation. The specific calculation formulas are as follows: R p = k g · [P] · [OH], R d = k d · [P], R t = R p - R d ; where, R p is the generation rate of ozone precursors, k g represents the reaction rate constant of generation, [P] is the concentration of ozone precursors within the grid, [OH] represents the concentration of hydroxyl radicals, R d is the decomposition rate of ozone precursors, k d represents the reaction rate constant of decomposition, R t represents the net transformation rate of ozone precursors.

[0125] This calculation process is achieved by processing grid by grid, and finally the generation, transformation, and diffusion rates of ozone precursors within each spatial grid are obtained.

[0126] The emission source distribution of ozone precursors is identified through a tracing algorithm, and the analysis results are mapped to the corresponding spatial units to generate the concentration evolution information of ozone precursors in each region:

[0127] After calculating the generation, transformation, and diffusion rates of ozone precursors in each grid, a tracing algorithm is used to identify the emission source distribution of ozone precursors. The specific method is as follows:

[0128] Based on the time-series data of each grid, analyze the change trend of the ozone precursor concentration, compare it with the diffusion rate of the surrounding grids, and determine the possible emission sources.

[0129] Quantitatively analyze the contribution of emission sources to each spatial grid, and map the results to the corresponding grid cells.

[0130] The goal of the tracing algorithm is to locate the source of pollutant emissions, calculate its diffusion contribution to the downstream grids, and generate the emission source distribution information of each grid.

[0131] Combine the analysis results of the tracing algorithm with the concentration time-series data within the grid to generate the concentration evolution information of ozone precursors in each region. The evolution information includes the time change trend of the ozone precursor concentration, the dynamic characteristics of generation and diffusion, and the spatial distribution characteristics of emission sources.

[0132] Based on the monitoring threshold, compare the concentration evolution information of ozone precursors hour by hour, and mark abnormal values, specifically including:

[0133] Set monitoring thresholds based on historical data and real-time monitoring data. The monitoring thresholds include a time threshold and a regional threshold. The time threshold represents the warning threshold range for different time periods, and the regional threshold represents the warning threshold range for different regions:

[0134] Time threshold: Represents the warning threshold range for different time periods, which is divided according to the characteristics of ozone precursor concentrations in different time periods found in historical monitoring. For example, day and night have different warning thresholds due to differences in human activities and atmospheric conditions.

[0135] Regional threshold: Represents the warning threshold range for different geographical regions, which is set in combination with regional characteristics (such as industrial activity intensity, terrain, and meteorological conditions). For example, the threshold in urban areas may be higher than that in rural areas. The setting of the threshold is based on the statistical analysis of historical data and the dynamic adjustment of current real-time monitoring data. Through the distribution analysis of the data, the concentration threshold range for each time period and region is determined, and a threshold table is generated for subsequent comparison.

[0136] Extract hourly concentration change data from the ozone precursor concentration evolution information. The hourly concentration change data includes the ozone precursor concentrations in each region during each monitoring period:

[0137] Analyze the ozone precursor concentration evolution information for each monitoring period and extract the concentration values for each region. Arrange the extracted concentration values according to the monitoring period to form an hourly concentration change data sequence. Each record includes a time point, a regional identifier, and the corresponding concentration value.

[0138] The extracted hourly concentration change data is the basis for subsequent comparison and marking, ensuring the integrity and accuracy of the concentration information for each monitoring period.

[0139] Compare the hourly concentration change data with the corresponding time threshold and regional threshold:

[0140] First, obtain the corresponding time threshold and regional threshold from the monitoring threshold table according to the current time period and the corresponding region.

[0141] Compare whether each record value in the hourly concentration change data exceeds the corresponding time threshold and regional threshold range. The specific judgment conditions are:

[0142] If the concentration value is higher than the time threshold and the regional threshold, it is determined as an abnormal value.

[0143] If the concentration value is within the threshold range, it is determined as normal.

[0144] Mark the abnormal values in the hourly concentration change data that exceed the monitoring threshold, and record the time period and regional information corresponding to the abnormal values:

[0145] Mark the abnormal values exceeding the monitoring threshold in the hourly concentration change data, and record the detailed information of the abnormal values, including the corresponding time period and area. The marking method is as follows:

[0146] Add a marking field to each record exceeding the threshold. The marking field includes an abnormality identifier and an abnormality type (such as exceeding the time threshold, exceeding the area threshold, or exceeding both).

[0147] While marking, record the specific information of the abnormal value, including the time period, area, concentration value, and the specific range exceeding the threshold. For example, the record shows "Time period A, Area B, Concentration value C exceeds the time threshold X and the area threshold Y".

[0148] After marking and recording are completed, generate a marking list containing the abnormal values for subsequent analysis and early warning signal generation.

[0149] Combine the marked abnormal values with the Geographic Information System to generate monitoring early warning signals, specifically including:

[0150] Match the marked abnormal values with the spatial location data in the Geographic Information System. The matching data includes the time period, area identifier, and specific geographical coordinates corresponding to the abnormal values:

[0151] Analyze the marked abnormal value data, extract the monitoring time period, area identifier, and abnormal concentration value from each record; find the corresponding geographical spatial location data in the Geographic Information System according to the area identifier in the marked abnormal values; through the geographical coordinate matching algorithm, accurately locate the marked abnormal values to the specific spatial units in the Geographic Information System to ensure that the spatial location corresponds one-to-one with the abnormal data.

[0152] After the matching is completed, generate a set of matching results containing the abnormal values and the corresponding spatial locations for subsequent spatial distribution processing.

[0153] Map the abnormal values to the spatial layer of the Geographic Information System according to the matching results to generate a spatial distribution map of the abnormal areas:

[0154] In the Geographic Information System, load the basic spatial layer of the area, including geographical boundaries, administrative area divisions, and basic geographical information; overlay the abnormal values in the matching results on the basic spatial layer, and identify the abnormal areas with different colors or symbols; ensure that the identification of each abnormal area on the spatial layer is accurate and visually classify them according to the magnitude of the concentration value, for example, represent the concentration level through color gradient.

[0155] The final spatial distribution map can intuitively display the geographical distribution of the abnormal values, laying a foundation for the subsequent generation of early warning signals.

[0156] Mark the specific information of the abnormal values on the spatial distribution map, including the abnormal concentration value, the range exceeding the threshold, and the time period when the abnormality occurs:

[0157] For each abnormal area, add an information window to display the abnormal data of that area;

[0158] The content of the information window includes:

[0159] Abnormal concentration value: Display the specific marked value indicating the concentration situation of that area;

[0160] Range exceeding the threshold: Clearly indicate the difference between the abnormal concentration value and the time threshold or area threshold;

[0161] Time period when the abnormality occurs: Display the specific time period when the abnormal value occurs, ensuring clear association between time information and spatial information.

[0162] Typeset the marked content according to the visualization requirements of the spatial distribution map to avoid information superposition or confusion.

[0163] Generate a monitoring and warning signal based on the spatial distribution map. The warning signal includes the geographical location of the abnormal area, the time of abnormality occurrence, and a detailed description of the abnormal concentration:

[0164] Extract the key information of each abnormal area, including geographical location, abnormal concentration value, occurrence time period, and range exceeding the threshold;

[0165] Format the extracted information into a standardized warning signal. The warning signal includes:

[0166] Geographical location of the abnormal area: Represented by longitude and latitude coordinates or administrative division names;

[0167] Time of abnormality occurrence: Specify the specific time range when the abnormal value occurs;

[0168] Abnormal concentration description: Include the abnormal concentration value and its specific range exceeding the threshold;

[0169] Integrate the information of all abnormal areas to generate a warning signal file or data packet for subsequent warning release and response processing.

[0170] Example 2: The difference between Example 2 and Example 1 of the present invention is that this example introduces a monitoring and warning system for ozone precursors based on satellite remote sensing identification.

[0171] Figure 2The structural schematic diagram of a monitoring and early warning system for ozone precursors based on satellite remote sensing identification of the present invention is given. A monitoring and early warning system for ozone precursors based on satellite remote sensing identification includes a data acquisition module, an overlap correction module, a data fusion module, an emission analysis module, an anomaly marking module, and an early warning generation module.

[0172] Data acquisition module: Acquire the original remote sensing image data of multiple satellite platforms at different observation times, and perform target recognition and pixel separation processing using a spectral unmixing algorithm.

[0173] Overlap correction module: Analyze the overlap effect of the overlapping areas of multiple satellite orbits within the observation range, correct the signal deviation caused by orbit differences through cross-consistency calculation, and generate corrected remote sensing image data.

[0174] Data fusion module: Use a multi-temporal interpolation algorithm to fuse the corrected remote sensing image data obtained at different times and different orbits to obtain continuous ozone precursor distribution data.

[0175] Emission analysis module: Based on the continuous ozone precursor distribution data, use an atmospheric chemical equilibrium model to perform emission source analysis and generate the evolution information of ozone precursor concentrations in each region.

[0176] Anomaly marking module: Compare the evolution information of ozone precursor concentrations hour by hour based on the monitoring threshold and mark the abnormal values.

[0177] Early warning generation module: Combine the marked abnormal values with a geographic information system to generate a monitoring and early warning signal.

[0178] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0179] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0180] Those of ordinary skill in the art will realize that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0181] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0182] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0183] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical module, and it may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] In addition, in each embodiment of the present application, each functional module can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0185] If the function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0186] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0187] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring and early warning of ozone precursors based on satellite remote sensing identification, characterized in that: The steps include: S1: Obtain the original remote sensing image data of multiple satellite platforms at different observation periods, and use the spectral unmixing algorithm to perform target recognition and pixel separation processing; S2: Analyze the overlapping effect of multiple satellite orbits in the observation range, correct the signal deviation caused by orbit differences through cross-consistency calculation, and generate corrected remote sensing image data; S3: Use a multi-period interpolation algorithm to fuse the corrected remote sensing image data acquired at different time periods and orbits to obtain continuous ozone precursor distribution data; S4: Based on the continuous ozone precursor distribution data, the atmospheric chemical balance model is used to analyze the emission sources and generate the evolution information of ozone precursor concentration in each region; S5: Compare the evolution of ozone precursor concentrations hour by hour based on monitoring thresholds and mark abnormal values; S6: Combine the marked abnormal values ​​with the geographic information system to generate monitoring and early warning signals.

2. The ozone precursor monitoring and early warning method based on satellite remote sensing identification according to claim 1 is characterized in that: Obtain original remote sensing image data from multiple satellite platforms at different observation periods, and perform target recognition and pixel separation processing, including: Analyze the orbital information of multiple satellite platforms to determine the observation period and spatial coverage of each satellite platform; Dispatch satellite platforms based on orbital information to collect original remote sensing image data at different observation periods; Preprocess the acquired original remote sensing image data; Process the pre-processed raw remote sensing image data, separate the target signal from the background signal, and extract the spectral characteristic information of ozone precursors in the pixel; Pixels in remote sensing image data are classified according to the spectral characteristic information of ozone precursors to identify target areas containing ozone precursor signals.

3. The ozone precursor monitoring and early warning method based on satellite remote sensing identification according to claim 2 is characterized in that: The overlapping effect analysis is performed on the overlapping areas of multiple satellite orbits within the observation range, and the signal deviation caused by the orbit difference is corrected by cross-consistency calculation to generate corrected remote sensing image data, including: Acquire remote sensing image data from multiple satellite platforms within overlapping observation ranges and determine the spatial boundaries of overlapping areas; Based on orbital parameters and satellite observation angles, the deviation types of observation data from different orbits in the overlapping area are analyzed, including spatial deviation, spectral deviation and radiation intensity deviation; The cross-consistency calculation method is used to perform multi-dimensional correction on the remote sensing image data in the overlapping area, including spatial registration correction, spectral distribution correction and radiation intensity normalization; The corrected overlapping area data and non-overlapping area data are integrated to generate corrected complete remote sensing image data.

4. The ozone precursor monitoring and early warning method based on satellite remote sensing identification according to claim 3 is characterized in that: The corrected remote sensing image data acquired at different time periods and orbits are fused using a multi-period interpolation algorithm to obtain continuous ozone precursor distribution data, including: The corrected remote sensing image data is grouped into time periods, and the remote sensing image data in different time periods are divided into multiple time series data sets in chronological order; Based on orbital parameters and observation range, the remote sensing image data obtained from different orbits are adjusted for spatial consistency in the orbital intersection area to ensure that the spatial coverage of data from different orbits is aligned; A multi-period interpolation algorithm is used to construct a continuous time series model of remote sensing image data in each period. The interpolation algorithm includes time interpolation and space interpolation to fill in the missing period and space observation data. The interpolated remote sensing image data are time-series fused, and the remote sensing image data in continuous time periods are comprehensively processed to generate continuous ozone precursor distribution data with temporal and spatial consistency.

5. The ozone precursor monitoring and early warning method based on satellite remote sensing identification according to claim 4 is characterized in that: Based on the continuous ozone precursor distribution data, the atmospheric chemical balance model is used to analyze the emission sources and generate the evolution information of ozone precursor concentration in each region, including: The continuous ozone precursor distribution data are divided into spatial grids of different regions, each of which represents a fixed spatial unit; According to the spatiotemporal distribution characteristics in the spatial grid, the time series data of the concentration of ozone precursors in each spatial unit is extracted; Combined with the atmospheric chemical balance model, the meteorological parameters related to each spatial grid are input, including temperature, humidity, wind speed and solar radiation intensity; Based on the atmospheric chemical balance equation, the generation, transformation and diffusion rates of ozone precursors in the spatial grid are calculated; The emission source distribution of ozone precursors is identified through the source tracing algorithm, and the analysis results are mapped to the corresponding spatial units to generate the concentration evolution information of ozone precursors in each region.

6. The ozone precursor monitoring and early warning method based on satellite remote sensing identification according to claim 5 is characterized in that: Compare the evolution of ozone precursor concentrations hour by hour based on monitoring thresholds and mark abnormal values, including: Set monitoring thresholds based on historical data and real-time monitoring data. The monitoring thresholds include time thresholds and regional thresholds. The time thresholds represent the warning threshold ranges for different time periods, and the regional thresholds represent the warning threshold ranges for different regions. Extracting hourly concentration variation data from the ozone precursor concentration evolution information, the hourly concentration variation data including the ozone precursor concentration in each area during each monitoring period; Compare the hourly concentration change data with the corresponding time threshold and regional threshold; Mark the abnormal values ​​in the hourly concentration change data that exceed the monitoring threshold, and record the time period and area information corresponding to the abnormal values.

7. The ozone precursor monitoring and early warning method based on satellite remote sensing identification according to claim 6 is characterized in that: Combine the marked abnormal values ​​with the geographic information system to generate monitoring and early warning signals, including: Matching the marked abnormal values ​​with the spatial location data in the geographic information system, the matching data including the time period, regional identification and specific geographic coordinates corresponding to the abnormal values; According to the matching results, the abnormal values ​​are mapped to the spatial layer of the geographic information system to generate a spatial distribution map of the abnormal area; Mark the specific information of abnormal values ​​on the spatial distribution map, including abnormal concentration value, range exceeding the threshold, and time period when the abnormality occurred; A monitoring warning signal is generated based on the spatial distribution map. The warning signal includes the geographical location of the abnormal area, the time of abnormal occurrence, and a detailed description of the abnormal concentration.

8. An ozone precursor monitoring and early warning system based on satellite remote sensing identification, used to implement an ozone precursor monitoring and early warning method based on satellite remote sensing identification as described in any one of claims 1 to 7, characterized in that: It includes data acquisition module, overlap correction module, data fusion module, emission analysis module, abnormal marking module and warning generation module; Data acquisition module: acquires original remote sensing image data from multiple satellite platforms at different observation periods, and uses spectral unmixing algorithm to perform target recognition and pixel separation processing; Overlap correction module: performs overlap effect analysis on the overlapping areas of multiple satellite orbits within the observation range, corrects the signal deviation caused by orbit differences through cross-consistency calculation, and generates corrected remote sensing image data; Data fusion module: Use multi-period interpolation algorithm to fuse the corrected remote sensing image data acquired in different periods and orbits to obtain continuous ozone precursor distribution data; Emission analysis module: Based on the continuous ozone precursor distribution data, the atmospheric chemical balance model is used to analyze the emission sources and generate the evolution information of ozone precursor concentration in each region; Abnormal marking module: compares the evolution information of ozone precursor concentration hour by hour based on the monitoring threshold and marks abnormal values; Early warning generation module: Combine the marked abnormal values ​​with the geographic information system to generate monitoring early warning signals.

Images