A method and system for monitoring and early warning of ozone precursors based on satellite remote sensing identification

Abstract

The present invention discloses a method and system for monitoring and early warning of ozone precursors based on satellite remote sensing identification, which specifically relates to the field of environmental monitoring technology. The method and system are used to solve the problem in the prior art that remote sensing satellites are difficult to capture the diurnal dynamic changes of ozone precursors due to orbit and observation period limitations. The method obtains remote sensing image data from multiple satellite platforms in different observation periods and uses a spectral unmixing algorithm to perform target identification and pixel separation. The method uses cross-consistency calculation to correct signal deviations in orbital overlapping areas. The method generates continuous ozone precursor distribution data in combination with a multi-period interpolation algorithm. The method uses an atmospheric chemical equilibrium model to analyze emission sources and generate concentration evolution information. The method marks abnormal values ​​based on dynamic monitoring thresholds and generates monitoring and early warning signals in combination with a geographic information system. The method achieves high-precision dynamic monitoring and real-time early warning of ozone precursors.

Description

Technical Field

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

[0002] As global environmental pollution problems intensify, ozone, as an important atmospheric pollutant, has had a significant impact on ecosystems and human health. The generation of ozone is closely related to the emission of its precursors (such as nitrogen oxides and volatile organic compounds). Therefore, accurate monitoring and dynamic early warning of ozone precursors have become key technical requirements in environmental protection. At present, satellite remote sensing technology has been widely used in the monitoring of atmospheric pollutants due to its wide coverage and strong observation capabilities. However, due to the strong temporal and spatial variation characteristics of ozone precursors, the existing remote sensing monitoring methods still have shortcomings in dynamically capturing the distribution characteristics of ozone precursors.

[0003] In existing technologies, remote sensing satellites are unable to fully capture the dynamic changes of ozone precursors at different times of the day and night due to limitations in their orbits and observation periods. This results in insufficient representativeness of monitoring data, thus affecting the accuracy of the atmospheric pollution early 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 an ozone precursor monitoring and early warning method and system based on satellite remote sensing identification to solve the problems raised in the above-mentioned background technology.

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

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

[0007] S1: Acquire raw remote sensing image data from multiple satellite platforms at different observation times, and use spectral unmixing algorithms to perform target recognition and pixel separation processing;

[0008] 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;

[0009] 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;

[0010] S4: Based on continuous ozone precursor distribution data, an atmospheric chemical balance model is used to perform emission source analysis and generate ozone precursor concentration evolution information for each region;

[0011] S5: Compare the evolution of ozone precursor concentrations hour by hour based on monitoring thresholds and mark abnormal values;

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

[0013] In a preferred embodiment, raw remote sensing image data from multiple satellite platforms at different observation periods are obtained, and target recognition and pixel separation processing are performed, specifically including:

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

[0015] Dispatching satellite platforms based on orbital information to collect original remote sensing image data at different observation periods;

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

[0017] Process the pre-processed original remote sensing image data, separate the target signal from the background signal, and extract the spectral characteristic information of ozone precursors in the pixel;

[0018] 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.

[0019] In a preferred embodiment, overlapping effect analysis is performed on the overlapping areas of multiple satellite orbits within the observation range, and signal deviations caused by orbital differences are corrected by cross-consistency calculation to generate corrected remote sensing image data, specifically including:

[0020] Acquire remote sensing image data from multiple satellite platforms within overlapping observation ranges and determine the spatial boundaries of overlapping areas;

[0021] 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;

[0022] 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;

[0023] The corrected overlapping area data and non-overlapping area data are integrated to generate corrected complete remote sensing image data.

[0024] In a preferred embodiment, a multi-period interpolation algorithm is used to fuse the corrected remote sensing image data acquired at different time periods and different orbits to obtain continuous ozone precursor distribution data, specifically including:

[0025] 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;

[0026] Based on orbital parameters and observation range, remote sensing image data acquired from different orbits are spatially aligned in the orbital intersection area to ensure that the spatial coverage of data from different orbits is aligned.

[0027] 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 spatial interpolation to fill in the missing period and spatial observation data.

[0028] The interpolated remote sensing image data is temporally 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.

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

[0030] The continuous ozone precursor distribution data are divided into spatial grids of different regions, each spatial grid represents a fixed spatial unit;

[0031] According to the spatiotemporal distribution characteristics within the spatial grid, the time series data of ozone precursor concentrations within each spatial unit are extracted;

[0032] 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;

[0033] Based on the atmospheric chemical balance equation, the generation, transformation and diffusion rates of ozone precursors in the spatial grid are calculated;

[0034] 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 evolution information of ozone precursor concentrations in each region.

[0035] In a preferred embodiment, the ozone precursor concentration evolution information is compared hourly based on the monitoring threshold and abnormal values ​​are marked, specifically including:

[0036] 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.

[0037] Extracting hourly concentration change data from the ozone precursor concentration evolution information, the hourly concentration change data including the ozone precursor concentration in each area during each monitoring period;

[0038] Compare the hourly concentration change data with the corresponding time thresholds and regional thresholds;

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

[0040] In a preferred embodiment, the marked abnormal values ​​are combined with a geographic information system to generate monitoring and early warning signals, specifically including:

[0041] Match the marked abnormal values ​​with the spatial location data in the geographic information system, where the matching data includes the time period, region identifier, and specific geographic coordinates corresponding to the abnormal values;

[0042] According to the matching results, the abnormal values ​​are mapped to the spatial layer of the geographic information system to generate the spatial distribution map of the abnormal area;

[0043] 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;

[0044] 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.

[0045] On the other hand, the present invention provides an ozone precursor monitoring and early warning system based on satellite remote sensing identification, comprising 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;

[0046] 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;

[0047] 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;

[0048] Data fusion module: uses 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;

[0049] Emission analysis module: Based on continuous ozone precursor distribution data, the atmospheric chemical balance model is used to perform emission source analysis and generate ozone precursor concentration evolution information in each region;

[0050] Abnormal marking module: compares the evolution of ozone precursor concentrations hour by hour based on monitoring thresholds and marks abnormal values;

[0051] Early warning generation module: combines the marked abnormal values ​​with the geographic information system to generate monitoring early warning signals.

[0052] The technical effects and advantages of the ozone precursor monitoring and early warning method and system based on satellite remote sensing identification of the present invention are as follows:

[0053] 1. By acquiring raw remote sensing image data from multiple satellite platforms at different observation times and combining it with a spectral unmixing algorithm to identify targets and separate pixels, the limitations of a single satellite platform observing in a fixed time period are overcome. By analyzing the overlap effect of multiple satellite orbits and using cross-consistency calculations to correct for 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, a multi-period interpolation algorithm is used to perform a time-series fusion of corrected data from different time periods and orbits, generating continuous ozone precursor distribution data. This fully captures the spatiotemporal dynamic characteristics of ozone precursors, laying a solid data foundation for subsequent atmospheric pollution analysis and early warning.

[0054] 2. Further, the atmospheric chemical equilibrium model is used to analyze the emission sources of continuous ozone precursor distribution data, and the concentration evolution information of ozone precursors in each region is generated, which accurately reflects the process of pollutant generation, diffusion and transformation. By comparing the concentration evolution information hour by hour based on the dynamic monitoring threshold, marking abnormal values, and generating monitoring and early warning signals in combination with the geographic information system, the present invention not only achieves the precise 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 atmospheric pollution risk monitoring. The method proposed in the present invention can more comprehensively reflect the distribution characteristics of ozone precursors under dynamic changes during the day and night, making up for the shortcomings of existing technologies in temporal and spatial coverage and dynamic data capture, and providing important technical support for atmospheric environmental governance and scientific decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a schematic diagram of an ozone precursor monitoring and early warning method based on satellite remote sensing identification according to the present invention;

[0056] Figure 2 The diagram is a structural diagram of an ozone precursor monitoring and early warning system based on satellite remote sensing identification according to the present invention. DETAILED DESCRIPTION

[0057] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0058] Example 1: Figure 1 The present invention provides a method for monitoring and early warning of ozone precursors based on satellite remote sensing identification, which includes the following steps:

[0059] S1: Acquire raw remote sensing image data from multiple satellite platforms at different observation times, and use spectral unmixing algorithms to perform target recognition and pixel separation processing.

[0060] S2: Perform 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.

[0061] S3: Use the multi-period 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.

[0062] S4: Based on continuous ozone precursor distribution data, the atmospheric chemical balance model is used to analyze emission sources and generate ozone precursor concentration evolution information in each region.

[0063] S5: Compare the evolution of ozone precursor concentrations hour by hour based on monitoring thresholds and mark abnormal values.

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

[0065] Acquire raw remote sensing image data from multiple satellite platforms at different observation times and perform target recognition and pixel separation processing, including:

[0066] Analyze the orbital information of multiple satellite platforms to determine the observation period and spatial coverage of each satellite platform:

[0067] A systematic analysis of the orbital information of multiple remote sensing satellite platforms was conducted to obtain the orbital parameters of each satellite platform, including observation period, spatial coverage, and the specific coordinate range of the orbital intersection area. This analysis determined the observation priority of each satellite and the possible overlapping observation areas, providing a basis for subsequent data scheduling.

[0068] According to the orbit information, the satellite platform is dispatched to collect the original remote sensing image data of different observation periods:

[0069] Remote sensing data collection is dispatched based on satellite platform orbital information. Priority is given to satellite platforms with high temporal and spatial resolution to ensure coverage of observation needs across different time periods. In practice, satellite capture modes are adjusted based on the ground station's ability to receive satellite data, collecting raw remote sensing imagery covering the target area.

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

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

[0072] Radiometric correction: Standardizes radiometric deviations in images due to differences in lighting conditions and sensor sensitivity.

[0073] Geometric correction: To address the geometric distortion caused by satellite orbital motion, georeferencing technology is used to align the image with geographic coordinates to ensure that the image spatial accuracy meets subsequent processing requirements.

[0074] The pre-processed original remote sensing image data is processed to separate the target signal and the background signal, and the spectral characteristic information of the ozone precursors in the pixel is extracted:

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

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

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

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

[0079] Each pixel in the remote sensing imagery is classified to determine whether it contains ozone precursor signals. Pixels identified as having significant ozone precursor signatures are further combined with spatial location information in the imagery to label target areas. Ultimately, target area identification results are generated for subsequent data correction and dynamic distribution analysis.

[0080] The overlap effect analysis is performed on the overlapping areas of multiple satellite orbits within the observation range. The signal deviation caused by the orbit difference is corrected by cross-consistency calculation to generate corrected remote sensing image data. Specifically, it includes:

[0081] Acquire remote sensing image data from multiple satellite platforms within overlapping observation ranges and determine the spatial boundaries of the overlapping areas:

[0082] First, remote sensing image data is acquired for the overlapping areas within the observation range of multiple satellite platforms. Specifically, it is necessary to receive the raw remote sensing data from each satellite platform through a ground station and determine the spatial extent of the overlapping area based on the parameters of the satellite's orbit. The determination of the spatial extent requires combining orbital parameters, including the satellite's inclination, altitude, orbit intersection point, and coverage, to ensure that the boundaries of the overlapping area are precise and clear. To this end, a spatial geometric model can be constructed to calculate the boundary coordinates of the overlapping area, ensuring that the acquired image data of the overlapping area has a consistent spatial scale.

[0083] 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:

[0084] For the remote sensing image data obtained in the overlapping area, it is necessary to combine the satellite's orbital parameters and observation angle to analyze the specific type of signal deviation. 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 will be errors in the spatial registration of images. This deviation mainly manifests as inconsistency in the position of pixels in the overlapping area.

[0086] Spectral bias: Differences in the spectral response characteristics of different satellite sensors can lead to spectral signal offsets, including fluctuations in reflectance values ​​and drift in spectral range.

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

[0088] By analyzing the deviation type, we can provide input basis for subsequent multi-dimensional correction.

[0089] 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:

[0090] For spatial deviation, the georeferencing algorithm is used, with the common control points in the overlapping area as the reference points. The translation, rotation, and scaling parameters of the pixels in the remote sensing image are calculated to achieve spatial consistency correction. 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] To address spectral deviation, a spectral matching algorithm is used to normalize the spectral values ​​of the image data of each track by fitting the offset of the spectral response curve. The correction formula is: S′(i, λ) = S(i, λ)·C(λ); where S′(i, λ) is the pixel spectral value after correction, S(i, λ) is the pixel spectral value before correction, C(λ) is the correction factor (used to adjust the spectral offset), i is the pixel index in the remote sensing image (used to identify the specific pixel position in the image data), and λ is the spectral wavelength (used to describe the spectral characteristics of different bands in the remote sensing image).

[0092] In response to the radiation intensity deviation, the radiation normalization method is adopted. 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 pixel radiation value is calculated by subtracting the minimum radiation value in the observation range from the radiation value before correction, and then dividing it 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 overlapping area data with the non-overlapping area data to generate the corrected complete remote sensing image data:

[0094] The overlapping area image data that has undergone multi-dimensional correction is integrated with the non-overlapping area image data to ensure the consistency of the image data in terms of spatial, spectral, and radiation intensity. During the integration process, the weight distribution method is used to fuse the data of the overlapping and non-overlapping areas to ensure a smooth transition of the image data.

[0095] The multi-period interpolation algorithm is used to fuse the corrected remote sensing image data acquired at different time periods and orbits to obtain continuous ozone precursor distribution data, including:

[0096] 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:

[0097] Before starting fusion, the corrected remote sensing image data is first grouped into time periods. By analyzing the time stamp information of the remote sensing image data, the image data within different time periods are divided into multiple continuous time series data sets. Each time series data set is arranged in order according to the acquisition time of the image to ensure that the data has a clear time series relationship after grouping. The specific grouping method is as follows:

[0098] First, extract the timestamp information of the remote sensing image. The timestamp format is standard time.

[0099] Sort the data by timestamp and divide it into multiple equally spaced time periods, such as hourly, daily, or weekly intervals.

[0100] Generate a group index table to record the start and end time and image data index of each time series data set to ensure the traceability of group information.

[0101] Based on orbital parameters and observation range, the remote sensing image data obtained from different orbits are spatially aligned in the orbital intersection area to ensure that the spatial coverage of data from different orbits is aligned:

[0102] For remote sensing image data acquired from different orbits, due to the different orbital parameters (such as altitude, inclination, and observation angle) of each satellite platform, the spatial coverage of the data in the orbital intersection area may be inconsistent. Before fusion, the remote sensing image data in the orbital intersection area needs to be spatially consistent. The specific adjustment method is as follows:

[0103] First, the overlapping area of ​​the images is calculated based on the orbital parameters, and the boundary coordinates of the overlapping area are defined by the geometric model.

[0104] Image data from different tracks were spatially aligned using georeferencing techniques.

[0105] Through the above adjustment method, the spatial range of the track intersection area is ensured to be consistent, eliminating the spatial deviation between data for the fusion step.

[0106] 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 spatial interpolation to fill in the missing period and spatial observation data:

[0107] After completing the time period grouping and spatial consistency adjustment, a multi-period interpolation algorithm is used 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 period and spatial data to ensure the continuity and integrity of the final data. The specific algorithm includes the following steps:

[0108] For missing data points in the time series, linear interpolation algorithm is used to fill them. The linear interpolation formula is as follows: Among them, V t Represents the value of the interpolation time point, V1 and V2 represent the data values ​​of two adjacent known time points, t1 and t2 represent two adjacent known time points, and t represents the interpolation time point.

[0109] For the data blank areas in the spatial distribution, the inverse distance weighted interpolation method is used to fill them; the formula is as follows: Among them, w u Determined by the distance, the formula is: Among them, V(x, y) represents the interpolation result of the target position, V u Represents the value of a known data point, d u Indicates 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 involved in the interpolation calculation, and w u Represents the weight value (inversely proportional to the distance).

[0110] The interpolated remote sensing image data is temporally fused, and the remote sensing image data within a continuous time period is comprehensively processed to generate continuous ozone precursor distribution data with temporal and spatial consistency:

[0111] After interpolation is completed, the image data generated in the continuous time series model is subjected to time series fusion. The fusion process includes the following steps:

[0112] First, the image data in each time period is smoothed to eliminate the discontinuity between adjacent time periods.

[0113] Secondly, the weighted average method is used to combine the interpolation results of multiple time periods to generate the final continuous ozone precursor distribution data: the weighted average method is used 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, the interpolation data of each time period is organized into a numerical matrix corresponding to the spatial coordinates, and each matrix represents the distribution of ozone precursors in that time period. Secondly, a weight is assigned to each time period based on the characteristics of each time period, such as data quality, time interval, and the integrity of the observation range. The weight value reflects the importance and reliability of the data, and time periods with higher weights have a greater impact on the final results. Finally, the data of all time periods are comprehensively calculated according to the assigned weights and integrated into a complete spatiotemporal distribution data matrix.

[0114] Through time series fusion, the final generated ozone precursor distribution data has continuity in time and space, laying a data foundation for subsequent emission source analysis and early warning analysis.

[0115] Based on continuous ozone precursor distribution data, an atmospheric chemical balance model is used to analyze emission sources and generate information on the evolution of ozone precursor concentrations in various regions, including:

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

[0117] Based on the spatial distribution of continuous ozone precursor distribution data, a gridding algorithm is used to divide it into multiple spatial grids, each representing a fixed spatial unit. Gridding is based on a geographic coordinate system, using longitude and latitude, ensuring that each grid has a fixed area and no overlap. The grid size is adjusted based on the required monitoring accuracy, for example, a spatial resolution of 1 square kilometer or less can be selected. The purpose of gridding is to discretize complex, continuous distribution data to facilitate processing in subsequent analytical steps.

[0118] According to the spatiotemporal distribution characteristics within the spatial grid, the time series data of ozone precursor concentrations within each spatial unit are extracted:

[0119] After gridding is complete, time series data for ozone precursor concentrations within each grid are extracted from the continuous ozone precursor distribution data, based on the geographic boundaries of each grid. The time series data for each grid consists of continuous observations along the time dimension, representing the temporal evolution of ozone precursor concentrations within the grid. When extracting time series data, it is important to ensure the accuracy of grid boundaries to avoid data confusion between adjacent grids.

[0120] 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:

[0121] For each spatial grid, the meteorological parameters associated with the atmospheric chemical balance model are input, including temperature, humidity, wind speed, and solar radiation intensity. These parameters are obtained through meteorological observations or model predictions, and the meteorological parameters for each grid must fully match its temporal and spatial dimensions. For example, temperature represents the atmospheric temperature at a specific point in time within the grid, in degrees Celsius; humidity represents the relative humidity within the grid, in percentage; wind speed represents the horizontal wind speed within the grid, in meters per second; and solar radiation intensity represents the solar radiation energy at a specific point in time within the grid, in watts per square meter.

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

[0123] Based on the atmospheric chemical balance 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 atmospheric chemical balance equation is used to calculate the generation, transformation and diffusion rates of ozone precursors within the grid. The specific calculation formula is as follows: R p =k g ·[P]·[OH],R d =k d [P], R t =R p -R d ; Among them, R p Ozone precursor production rate, k g represents the reaction rate constant, [P] represents the concentration of ozone precursors in the grid, [OH] represents the concentration of hydroxyl radicals, R d represents the decomposition rate of ozone precursors, k d represents the decomposition reaction rate constant, R t Represents the net conversion rate of ozone precursors.

[0125] The calculation process is implemented through grid-by-grid processing, and ultimately the generation, transformation and diffusion rates of ozone precursors within each spatial grid are obtained.

[0126] The 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 evolution information of ozone precursor concentrations in each region:

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

[0128] Based on the time series data of each grid, the changing trend of the concentration of ozone precursors is analyzed and compared with the diffusion rate of the surrounding grids to determine the possible emission sources.

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

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

[0131] The results of the source tracing algorithm are combined with the time-series concentration data within the grid to generate information on the evolution of ozone precursor concentrations in each region. This information includes the temporal trends of ozone precursor concentrations, their generation and diffusion dynamics, and the spatial distribution characteristics of emission sources.

[0132] Compare the evolution of ozone precursor concentrations hour by hour based on monitoring thresholds and flag abnormal values, including:

[0133] 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:

[0134] Time thresholds: These represent the range of warning thresholds for different time periods, based on the concentration characteristics of ozone precursors found in historical monitoring. For example, daytime and nighttime warning thresholds may differ due to differences in human activities and atmospheric conditions.

[0135] Regional Thresholds: These represent warning threshold ranges for different geographic areas, based on regional characteristics (such as industrial activity intensity, topography, and meteorological conditions). For example, urban areas may have higher thresholds than rural areas. Thresholds are set based on statistical analysis of historical data and dynamic adjustments to current real-time monitoring data. Data distribution analysis determines concentration threshold ranges for each time period and region, and a threshold table is generated for subsequent comparison.

[0136] Hourly concentration change data are extracted from the ozone precursor concentration evolution information. The hourly concentration change data include the ozone precursor concentrations in each area during each monitoring period:

[0137] The evolution of ozone precursor concentrations during each monitoring period is analyzed, and concentration values ​​for each region are extracted. These extracted concentration values ​​are arranged by monitoring period to form a sequence of hourly concentration change data. Each record includes a time point, region identifier, and corresponding concentration value.

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

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

[0140] First, according to the current time period and the corresponding area, the corresponding time threshold and area threshold are obtained from the monitoring threshold table.

[0141] Compare each record value in the hourly concentration change data to see if it exceeds the corresponding time threshold and area threshold range. The specific judgment conditions are:

[0142] If the concentration value is higher than the time threshold and the area threshold, it is determined to be an abnormal value.

[0143] If the concentration value is within the threshold range, it is judged to be normal.

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

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

[0146] A tag field is added to each record that exceeds the threshold. The tag field contains an exception identifier and an exception type (such as exceeding the time threshold, exceeding the area threshold, or exceeding both).

[0147] When marking an abnormal value, specific information about the value is recorded, including the time period, area, concentration value, and the specific range of the threshold exceeded. For example, the record shows "Time period A, area B, concentration value C exceeded time threshold X and area threshold Y."

[0148] After marking and recording are completed, a mark list containing abnormal values ​​is generated for subsequent analysis and early warning signal generation.

[0149] Combine the marked abnormal values ​​with the geographic information system to generate monitoring and early warning signals, including:

[0150] Match the marked abnormal values ​​with the spatial location data in the geographic information system. The matching data includes the time period, region identifier, and specific geographic coordinates corresponding to the abnormal value:

[0151] Parse the marked abnormal numerical data and extract the monitoring period, regional identifier and abnormal concentration value in each record; find the corresponding geographic spatial location data in the geographic information system based on the regional identifier in the marked abnormal numerical value; use the geographic coordinate matching algorithm to accurately locate the marked abnormal numerical value to the specific spatial unit in the geographic information system to ensure that the spatial location corresponds to the abnormal data one by one.

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

[0153] According to the matching results, the abnormal values ​​are mapped to the spatial layer of the geographic information system to generate the spatial distribution map of the abnormal area:

[0154] In the geographic information system, load the basic spatial layer of the area, including geographic boundaries, administrative divisions, and basic geographic information; overlay the abnormal values ​​in the matching results onto the basic spatial layer, and identify abnormal areas with different colors or symbols; ensure that each abnormal area is accurately identified on the spatial layer, and visually classify them according to the size of the concentration value, such as using color gradients to represent concentration levels.

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

[0156] 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:

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

[0158] The information window contents include:

[0159] Abnormal concentration value: Displays the specific mark value, indicating the concentration situation in the area;

[0160] Out of threshold range: clearly indicate the difference between the abnormal concentration value and the time threshold or regional threshold;

[0161] Abnormal occurrence period: Displays the specific time period when the abnormal value occurs, ensuring a clear correlation between time information and spatial information.

[0162] The annotation content should be arranged according to the visualization requirements of the spatial distribution map to avoid information overlap or confusion.

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

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

[0165] The extracted information is formatted into standardized early warning signals, which include:

[0166] Geographical location of the abnormal area: expressed in latitude and longitude coordinates or administrative division name;

[0167] Abnormal occurrence time: specifies the specific time range when the abnormal value occurs;

[0168] Abnormal concentration description: including the abnormal concentration value and the specific range where it exceeds the threshold;

[0169] Integrate information from all abnormal areas and generate warning signal files or data packets for subsequent warning issuance and response processing.

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

[0171] Figure 2A structural schematic diagram of an ozone precursor monitoring and early warning system based on satellite remote sensing identification is given in the present invention. The ozone precursor monitoring and early warning system 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: 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.

[0173] 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.

[0174] Data fusion module: Use the multi-period interpolation algorithm to fuse the corrected remote sensing image data obtained in different time periods and different orbits to obtain continuous ozone precursor distribution data.

[0175] Emission analysis module: Based on continuous ozone precursor distribution data, the atmospheric chemical balance model is used to analyze emission sources and generate ozone precursor concentration evolution information in each region.

[0176] Abnormal marking module: Based on the monitoring threshold, the concentration evolution information of ozone precursors is compared hourly and abnormal values ​​are marked.

[0177] Early warning generation module: combines the marked abnormal values ​​with the geographic information system to generate monitoring early warning signals.

[0178] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0179] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. 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 program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. 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 a wired (e.g., infrared, wireless, microwave, etc.) method. 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 contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0180] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0181] Those skilled in the art will 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 aforementioned method embodiments and will not be repeated here.

[0182] In the several embodiments provided in this 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 schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0183] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

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

[0185] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling 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 method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0186] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0187] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection 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: Acquire raw remote sensing image data from multiple satellite platforms at different observation times, and use spectral unmixing algorithms to perform target recognition and pixel separation processing; S2: Analyze the overlap effect 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, it includes: 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; Integrate the corrected overlapping area data with the non-overlapping area data to generate corrected complete 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 continuous ozone precursor distribution data, an atmospheric chemical balance model is used to perform emission source analysis and generate ozone precursor concentration evolution information for 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: Acquire raw remote sensing image data from multiple satellite platforms at different observation times 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; Dispatching 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 original 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 multi-period interpolation algorithm is used to fuse the corrected remote sensing image data acquired at different time periods and orbits 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, remote sensing image data acquired from different orbits are spatially aligned 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 spatial interpolation to fill in the missing period and spatial observation data. The interpolated remote sensing image data is temporally 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.

4. The ozone precursor monitoring and early warning method based on satellite remote sensing identification according to claim 3 is characterized in that: Based on continuous ozone precursor distribution data, an atmospheric chemical balance model is used to analyze emission sources and generate information on the evolution of ozone precursor concentrations in various regions, including: The continuous ozone precursor distribution data are divided into spatial grids of different regions, each spatial grid represents a fixed spatial unit; According to the spatiotemporal distribution characteristics within the spatial grid, the time series data of ozone precursor concentrations within each spatial unit are 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 evolution information of ozone precursor concentrations in each region.

5. The ozone precursor monitoring and early warning method based on satellite remote sensing identification according to claim 4 is characterized in that: Compare the evolution of ozone precursor concentrations hour by hour based on monitoring thresholds and flag 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 change data from the ozone precursor concentration evolution information, the hourly concentration change data including the ozone precursor concentration in each area during each monitoring period; Compare the hourly concentration change data with the corresponding time thresholds and regional thresholds; Mark abnormal values ​​that exceed the monitoring threshold in the hourly concentration change data, and record the time period and area information corresponding to the abnormal values.

6. The ozone precursor monitoring and early warning method based on satellite remote sensing identification according to claim 5 is characterized in that: Combine the marked abnormal values ​​with the geographic information system to generate monitoring and early warning signals, including: Match the marked abnormal values ​​with the spatial location data in the geographic information system, where the matching data includes the time period, region identifier, 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 the 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.

7. An ozone precursor monitoring and early warning system based on satellite remote sensing identification, used to implement the ozone precursor monitoring and early warning method based on satellite remote sensing identification according to any one of claims 1 to 6, characterized in that: It includes data acquisition module, overlap correction module, data fusion module, emission analysis module, anomaly 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: uses 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; Emission analysis module: Based on continuous ozone precursor distribution data, the atmospheric chemical balance model is used to perform emission source analysis and generate ozone precursor concentration evolution information in each region; Abnormal marking module: compares the evolution of ozone precursor concentrations hour by hour based on monitoring thresholds and marks abnormal values; Early warning generation module: combines the marked abnormal values ​​with the geographic information system to generate monitoring early warning signals.

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