Multi-satellite collaborative hydrological monitoring system

Abstract

The present invention relates to the field of hydrological monitoring technology, and in particular to a multi-satellite collaborative hydrological monitoring system. The method comprises the following steps: a water level altimeter module obtains radar pulse recovery delay data and calculates water level height through altimeter satellites, removes abnormal points to generate river water level data, a water body remote sensing module uses remote sensing satellites to collect river water body images to extract water body boundary features, and deduce water body area change data, a meteorological wind shear analysis module obtains river meteorological data through meteorological satellites, performs microwave scattering measurement and wind speed inversion, and generates wind-induced shear stress parameters, a flow estimation module combines river water level and water body area data to perform cross-section dynamic analysis and estimate water body flow, a flow correction module corrects river water body flow based on wind-induced shear stress parameters and performs real-time monitoring, and data is synchronously uploaded to a control terminal to achieve comprehensive dynamic monitoring of the river hydrological status. The present invention realizes a more efficient multi-satellite collaborative hydrological monitoring system.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrological monitoring, and in particular to a multi-satellite coordinated hydrological monitoring system. Background Art

[0002] Traditional hydrological monitoring methods often face problems such as poor data timeliness, limited spatial coverage, and insufficient monitoring accuracy. These factors make it difficult to obtain water level, flow and meteorological data in real time, making it difficult to meet the needs of modern water resources management. Especially under extreme weather conditions, the dynamic changes in river flow have a profound impact on the ecological environment and human activities. The current hydrological monitoring system mainly relies on ground observation stations, resulting in limitations and regionality in data collection, making it difficult to identify hydrological changes in some remote areas in a timely manner and lacking the ability to conduct comprehensive monitoring. Especially in a changing climate environment, the passivity of traditional monitoring methods shows great limitations and cannot quickly respond to the challenges brought about by hydrological changes. Effective technical means are urgently needed to improve the dynamic monitoring capabilities of river hydrological conditions. Summary of the Invention

[0003] Based on this, it is necessary to provide a multi-satellite collaborative hydrological monitoring system to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a multi-satellite coordinated hydrological monitoring system includes:

[0005] S1: Water level height measurement module, used to collect radar pulse recovery delay data through altimetry satellites; calculate the water level height based on the radar pulse recovery delay data to obtain candidate river water level data; and remove abnormal points from the candidate river water level data based on a preset normal water level distribution pattern to generate river water level data;

[0006] S2: Water remote sensing module, used to collect river water images through remote sensing satellites, extract water boundary features from river water images, and deduce water area change data based on water boundary features;

[0007] S3: Meteorological wind shear analysis module, used to collect river meteorological data through meteorological satellites; perform microwave scattering measurement on the river meteorological data and invert the river wind speed to obtain the water surface wind speed parameters; and deduce the wind flow shear torque based on the water surface wind speed parameters to generate wind-induced shear stress parameters;

[0008] S4: Flow estimation module, used to perform dynamic cross-sectional analysis based on river water level data and water area change data to generate the cross-sectional area of ​​each river area; and estimate the river water flow based on the cross-sectional area of ​​each river area and river water level data;

[0009] S5: Flow correction module, used to perform wind shear interference correction on river water flow based on wind-induced shear stress parameters to generate corrected river water flow; monitor the corrected river water flow in real time and upload it to the control terminal synchronously.

[0010] The present invention collects radar pulse recovery delay data by altimetry satellites and calculates water level height, thereby obtaining accurate candidate river channel water level data. Eliminating abnormal points based on the normal distribution pattern of water levels can ensure the stability of river channel water level data and improve the reliability of hydrological monitoring. By collecting river channel water body images and extracting water body boundary features through remote sensing satellites, changes in water body area can be accurately captured, thereby improving the accuracy of dynamic water body monitoring. Wind speed inversion through microwave scattering measurement can obtain water body surface wind speed parameters, and wind-induced shear stress parameters can be deduced in combination with wind flow shear torque, which helps to improve the impact of wind fields on hydrological The analysis accuracy of characteristic influence is improved by dynamic cross-section analysis based on river water level data and water area change data. The water cross-sectional area of ​​different regions in the river can be obtained in real time, and the water flow can be estimated in combination with the river water level data to improve the spatial resolution and temporal accuracy of flow estimation. The wind shear interference correction of water flow can be performed using wind-induced shear stress parameters to reduce the impact of wind stress on flow monitoring and improve the accuracy of flow data. The real-time monitoring of the corrected river water flow and its simultaneous upload to the control terminal can ensure the rapid transmission of hydrological information and improve the timeliness and availability of hydrological data.

[0011] Preferably, the water level height measurement module includes the following functions:

[0012] S11: Collecting altimetry satellite multi-beam radar data through altimetry satellites; performing pulse compression processing on the altimetry satellite multi-beam radar to obtain radar pulse recovery delay data, where the radar frequency range is 13.5GHz-14.0GHz, the pulse width range is 3.2-3.8μs, and the delay resolution is 0.1ns;

[0013] S12: performing phase differential correction on the radar pulse recovery delay data to generate denoised delay data, wherein the phase differential range is -180° to 180° and the delay correction range is -0.5 to 0.5 nanoseconds; performing refraction mapping water level inversion on the denoised delay data to obtain original water level height data;

[0014] S13: Perform atmospheric delay water level compensation on the original water level height data and perform ellipsoid fitting correction to obtain ellipsoid-corrected water level data;

[0015] S14: performing local coordinate projection transformation on the ellipsoid-corrected water level data to generate candidate river channel water level data; performing distribution fitting analysis on the candidate river channel water level data to obtain water level distribution deviation data;

[0016] S15: performing abnormal point detection on the water level distribution deviation data based on a preset normal water level distribution pattern to obtain abnormal water level data;

[0017] S16: performing isolated abnormal point verification on the abnormal water level data to generate verified isolated abnormal points; performing interpolation filling processing on the verified isolated abnormal points to generate river water level data.

[0018] The present invention generates high-precision radar pulse recovery delay data through pulse compression processing of the altimetry satellite multi-beam radar. The phase difference correction technology effectively removes noise in the delay data and improves data accuracy. The refraction mapping water level inversion technology converts the denoised delay data into original water level height data, ensuring high precision and high stability of water level inversion. The atmospheric delay water level compensation technology eliminates atmospheric interference on water level measurement. The ellipsoid fitting correction technology further improves the geometric accuracy of water level data. The local coordinate projection transformation technology ensures the seamless integration of water level data with the geographic information system. The distribution fitting analysis technology generates water level distribution deviation data, providing a reliable basis for outlier detection. The outlier detection technology effectively identifies abnormal water level data. The isolated outlier verification technology ensures the accuracy and reliability of the outliers. The interpolation filling technology generates complete and continuous river water level data. The overall system significantly improves the accuracy, stability and real-time performance of hydrological monitoring through multi-source data fusion and high-precision processing technology, providing strong technical support for water resources management, flood prevention and disaster reduction, and ecological environmental protection.

[0019] Preferably, performing atmospheric delay water level compensation on the original water level height data and performing ellipsoid fitting correction includes:

[0020] Extract atmospheric delay parameters from raw water level height data;

[0021] Calculate atmospheric delay amount for atmospheric delay parameters;

[0022] Perform delay compensation processing on the original water level height data based on the atmospheric delay to obtain the atmospheric correction water level;

[0023] Initialize the ellipsoid model parameters for the atmospheric correction water level to generate the ellipsoid model parameters, where the number of iterations is 5-15 times and the error convergence threshold is 0-0.001m;

[0024] Fit and optimize the atmospheric correction water level and ellipsoid model parameters to obtain the optimized ellipsoid parameters;

[0025] The atmospheric correction water level is subjected to ellipsoid correction according to the optimized ellipsoid parameters to obtain ellipsoid-corrected water level data.

[0026] The present invention extracts the atmospheric delay parameters of the original water level height data and calculates the atmospheric delay amount, accurately eliminates the interference of the atmosphere on the water level measurement, improves the data accuracy, performs delay compensation processing on the original water level height data based on the atmospheric delay amount, generates atmospheric corrected water level data, and ensures the high precision of water level measurement. The ellipsoid model parameter initialization technology generates high-precision ellipsoid model parameters through multiple iterations and error convergence threshold control. The fitting optimization technology further optimizes the ellipsoid parameters to ensure the geometric accuracy of the ellipsoid correction. The ellipsoid correction processing technology corrects the atmospheric corrected water level data according to the optimized ellipsoid parameters to generate ellipsoid corrected water level data, which significantly improves the geometric consistency and spatial accuracy of the water level data. The overall system significantly improves the accuracy and reliability of water level measurement through atmospheric delay compensation and ellipsoid fitting correction technology, providing strong data support for hydrological monitoring, water resources management and flood prevention and disaster reduction.

[0027] Preferably, the water remote sensing module collects river water images through remote sensing satellites and extracts water boundary features of the river water images, including:

[0028] Collect optical imaging data through remote sensing satellites;

[0029] Perform radiation correction on the optical imaging data and perform atmospheric elimination processing to obtain river water images;

[0030] Perform multi-view processing on SAR satellite microwave imaging data and extract scattering feature data;

[0031] Perform polarization phase decomposition on the scattering characteristic data and partition the backscattering pattern to obtain the polarization scattering pattern of the water body;

[0032] Perform tensor coupling processing on river water images and water polarization scattering patterns to generate remote sensing image fusion river data;

[0033] Perform water boundary segmentation and analysis on the river data fused with remote sensing images to obtain river water mask data;

[0034] The water boundary features of river water images are extracted based on river water mask data.

[0035] The present invention uses remote sensing satellites to efficiently acquire river water images. Radiation correction and atmospheric elimination processing improve the quality of water images. SAR satellite microwave imaging data provides additional scattering feature information. Polarization phase decomposition and backscattering pattern partitioning technology deeply analyze the scattering characteristics of water bodies. Tensor coupling processing realizes the fusion of different remote sensing images, significantly improving the comprehensiveness of river data. Water body boundary segmentation analysis accurately identifies the water body range, which improves the accuracy and reliability of river water feature extraction as a whole, providing strong data support for water resource management and environmental monitoring.

[0036] Preferably, the water body remote sensing module deduces water body area change data based on water body boundary characteristics, including:

[0037] The boundary features of the water body are located by using navigation satellites to obtain optimized water body boundary features;

[0038] Based on the optimized water body boundary features, the water body pixels within the boundary of the river water body image are extracted to obtain the number of water body pixels within the boundary;

[0039] Based on the preset satellite imaging spatial resolution, the number of water body pixels within the boundary is analyzed for point changes to generate boundary change data;

[0040] Water area mapping is performed on the river water body image based on the boundary change data to obtain water body area change data.

[0041] The present invention uses navigation satellites to accurately locate the boundary features of water bodies, thereby improving the optimization degree of water body boundaries. The water body pixel extraction within the boundary accurately identifies the water body range. The point change analysis based on the spatial resolution of satellite imaging provides reliable data support for water body area changes. The boundary change data further enhances the monitoring capability of river water body dynamics. The water body area mapping realizes the accurate assessment of water body area changes, which improves the timeliness and accuracy of water body change monitoring as a whole and provides a scientific basis for water resource management decision-making.

[0042] Preferably, the meteorological wind shear analysis module performs microwave scattering measurement on river meteorological data and performs river wind speed inversion, including:

[0043] Performing denoising transformation on river meteorological data to obtain denoised river meteorological data;

[0044] Decompose the characteristic spectrum of denoised river meteorological data in layers to obtain river meteorological characteristic data;

[0045] The radar scattering model is inverted on the river meteorological characteristic data to obtain the initial scattering coefficient;

[0046] The initial scattering coefficient is compensated for the microscale wave roughness to obtain the corrected scattering coefficient;

[0047] Perform water body electromagnetic feature mapping on the corrected scattering coefficient to generate water surface scattering characteristics;

[0048] Multi-angle polarization scattering fusion is performed on the water surface scattering characteristics to obtain multi-view scattering data;

[0049] performing directional scattering energy correction on the multi-view scattering data to obtain corrected scattering data;

[0050] The wind field dynamics deconstruction is performed on the corrected scattering data, and wind speed parameter mapping is performed to obtain the wind speed parameters on the water surface.

[0051] The present invention improves the quality of river meteorological data through denoising transformation, the hierarchical decomposition of characteristic spectrum effectively extracts key information of meteorological data, the radar scattering model inversion provides preliminary parameters for wind speed estimation, the microscale wave roughness compensation improves the accuracy of the scattering coefficient, the water body electromagnetic characteristic mapping enhances the description of scattering characteristics, the multi-angle polarization scattering fusion realizes the multi-perspective integration of information, the directional scattering energy correction further improves the reliability of the data, the wind field dynamics deconstruction and wind speed parameter mapping comprehensively consider the complex water surface effects, and overall provide accurate and comprehensive data support for river wind speed inversion, providing an important scientific basis for hydrological research and water resources management.

[0052] Preferably, the deduction of wind shear torque based on water surface wind speed parameters in the meteorological wind shear analysis module includes:

[0053] Reconstruct the spatiotemporal flow field of the wind speed parameters on the water surface to obtain continuous wind field distribution data;

[0054] Based on the continuous wind field distribution data, the fluid boundary layer analysis of the wind speed parameters on the water surface is carried out to generate the vertical distribution data of the wind speed;

[0055] Fit the flow field logarithmic function to the vertical distribution data of wind speed to obtain wind shear gradient data;

[0056] Perform gas dynamic density compensation on wind shear gradient data to generate density-corrected wind shear data;

[0057] Calculate aerodynamic drag from density-corrected wind shear data;

[0058] Perform drag coefficient fitting on aerodynamic wind resistance to generate wind resistance coefficient;

[0059] Wind-induced stress calculation is performed on the density-corrected wind shear data and wind drag coefficient to generate wind-induced shear stress parameters.

[0060] The present invention realizes the continuity and dynamics of the wind field through the reconstruction of the spatiotemporal flow field. The analysis of the fluid boundary layer provides an in-depth understanding of the wind speed. The vertical distribution data of the wind speed lays the foundation for the analysis of wind flow behavior. The logarithmic function fitting of the flow field improves the fitting accuracy of the wind shear gradient. The gas dynamic density compensation enhances the accuracy of the wind shear data. The calculation of the aerodynamic wind resistance provides a scientific basis for the resistance measurement. The drag coefficient fitting realizes the improvement of the wind resistance characteristics. The generation of wind-induced shear stress parameters provides an important indicator for the analysis of the interaction between water bodies and the atmosphere. Overall, the accuracy and reliability of the wind shear analysis are improved, and key data support and analysis tools are provided for environmental monitoring and hydrological research.

[0061] Preferably, the flow estimation module performs dynamic cross-section analysis based on river water level data and water area change data, including:

[0062] Deducing the river width at different times based on the water area change data;

[0063] Align the timestamps of river width and river water level data at different times to obtain synchronized hydrological data;

[0064] Based on the synchronized hydrological data, the river channel spatial registration is performed to obtain the hydrological data corresponding to the location;

[0065] Intercept the river cross-section distribution data based on the hydrological data corresponding to the location;

[0066] Based on the location-corresponding hydrological data and the river cross-sectional distribution data, the water-flowing cross-sectional area of ​​each region of the river is inferred.

[0067] The present invention effectively deduces changes in river width through water area change data, realizes the synchronization of data at different time points through timestamp alignment, ensures the accurate correspondence of hydrological data positions, and intercepts river cross-sectional distribution data to provide key spatial information. The accuracy of flow estimation is improved by inferring the cross-sectional area of ​​each region based on the position-corresponding hydrological data, and the overall dynamic response capability of river flow estimation is improved, providing timely and reliable data support for water resource management and river monitoring.

[0068] Preferably, the flow estimation module estimates the flow of the river based on the cross-sectional area of ​​each region of the river and the river water level data, including:

[0069] Calculate the wetted perimeter parameters of each river channel area based on the cross-sectional area of ​​each river channel area and the river water level data of the corresponding river channel area;

[0070] Derivation of hydraulic radius data for each region of the river channel based on the cross-sectional area of ​​each region of the river channel and the wetted perimeter parameters of each region of the river channel;

[0071] Mapping the hydraulic gradient of various areas of the river based on river water level data;

[0072] The Manning formula is used to estimate the flow rate of the river water body based on the preset river channel Manning roughness coefficient, the cross-sectional area of ​​each river channel area, the hydraulic radius data of each river channel area, and the hydraulic slope of each river channel area.

[0073] Among them, the Manning formula is specifically:

[0074] ;

[0075] Among them, the formula is the river water flow, is the Manning roughness coefficient, is the cross-sectional area of ​​water flow, is the hydraulic radius data, is the hydraulic slope.

[0076] The present invention improves the accuracy of flow estimation by calculating wetted perimeter parameters through cross-sectional area and river water level data. The derivation of hydraulic radius further enhances the understanding of water flow characteristics. The mapping of hydraulic gradient provides the necessary terrain information for flow calculation. The Manning formula is combined with the roughness coefficient, cross-sectional area, hydraulic radius and hydraulic gradient to achieve a scientific estimation of river water flow, which improves the accuracy and reliability of dynamic monitoring of river flow as a whole and provides important decision-making support for the rational allocation and utilization of water resources.

[0077] Preferably, the flow correction module includes the following functions:

[0078] Perform virtual river simulation on the river water flow to obtain simulated river water body;

[0079] The wind field is reconstructed based on the wind-induced shear stress parameters, and the wind shear interference simulation is performed on the simulated river water body based on the reconstructed wind field to obtain the simulated wind shear interference water body data;

[0080] Calculate the difference in water flow between simulated wind shear disturbance water body data and simulated river water body;

[0081] Perform wind shear interference correction on the river water flow according to the water flow difference to generate the corrected river water flow;

[0082] The water flow of the corrected river channel is monitored in real time based on the preset water flow safety threshold. When the water flow of the corrected river channel is less than the preset water flow safety threshold, the water flow of the corrected river channel is recorded and uploaded to the control terminal synchronously.

[0083] When the corrected river water flow exceeds the preset water flow safety threshold, a hydrological alarm is generated and uploaded to the control terminal simultaneously.

[0084] The present invention realizes the dynamic simulation of water flow characteristics through virtual river simulation. Wind field reconstruction provides an accurate basis for the analysis of wind shear interference. Simulating wind shear interference water data helps to quantify the impact of wind on water flow. The calculation of fluid flow gap provides a basis for accurate flow correction. The generation of wind shear interference correction improves the reliability of flow data. Real-time monitoring based on water flow safety threshold ensures flow safety guidance. The recording and synchronous upload functions improve the timeliness of data management. When the corrected flow is greater than the safety threshold, a hydrological alarm is generated to effectively maintain the safety of river management, and the overall monitoring accuracy of water flow and decision-making support capabilities are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 This is a schematic diagram of a module for multi-satellite collaborative hydrological monitoring;

[0086] Figure 2 This is a flow chart of the water level measurement module.

[0087] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0088] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0089] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0090] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0091] To achieve this, please refer to Figures 1 to 2 , a multi-satellite coordinated hydrological monitoring system, comprising:

[0092] S1: Water level height measurement module, used to collect radar pulse recovery delay data through altimetry satellites; calculate the water level height based on the radar pulse recovery delay data to obtain candidate river water level data; and remove abnormal points from the candidate river water level data based on a preset normal water level distribution pattern to generate river water level data;

[0093] S2: Water remote sensing module, used to collect river water images through remote sensing satellites, extract water boundary features from river water images, and deduce water area change data based on water boundary features;

[0094] S3: Meteorological wind shear analysis module, used to collect river meteorological data through meteorological satellites; perform microwave scattering measurement on the river meteorological data and invert the river wind speed to obtain the water surface wind speed parameters; and deduce the wind flow shear torque based on the water surface wind speed parameters to generate wind-induced shear stress parameters;

[0095] S4: Flow estimation module, used to perform dynamic cross-sectional analysis based on river water level data and water area change data to generate the cross-sectional area of ​​each river area; and estimate the river water flow based on the cross-sectional area of ​​each river area and river water level data;

[0096] S5: Flow correction module, used to perform wind shear interference correction on river water flow based on wind-induced shear stress parameters to generate corrected river water flow; monitor the corrected river water flow in real time and upload it to the control terminal synchronously.

[0097] The present invention collects radar pulse recovery delay data by altimetry satellites and calculates water level height, thereby obtaining accurate candidate river channel water level data. Eliminating abnormal points based on the normal distribution pattern of water levels can ensure the stability of river channel water level data and improve the reliability of hydrological monitoring. By collecting river channel water body images and extracting water body boundary features through remote sensing satellites, changes in water body area can be accurately captured, thereby improving the accuracy of dynamic water body monitoring. Wind speed inversion through microwave scattering measurement can obtain water body surface wind speed parameters, and wind-induced shear stress parameters can be deduced in combination with wind flow shear torque, which helps to improve the impact of wind fields on hydrological The analysis accuracy of characteristic influence is improved by dynamic cross-section analysis based on river water level data and water area change data. The water cross-sectional area of ​​different regions in the river can be obtained in real time, and the water flow can be estimated in combination with the river water level data to improve the spatial resolution and temporal accuracy of flow estimation. The wind shear interference correction of water flow can be performed using wind-induced shear stress parameters to reduce the impact of wind stress on flow monitoring and improve the accuracy of flow data. The real-time monitoring of the corrected river water flow and its simultaneous upload to the control terminal can ensure the rapid transmission of hydrological information and improve the timeliness and availability of hydrological data.

[0098] In an embodiment of the present invention, the multi-satellite coordinated hydrological monitoring system includes the following steps:

[0099] S1: Water level height measurement module, used to collect radar pulse recovery delay data through altimetry satellites; calculate the water level height based on the radar pulse recovery delay data to obtain candidate river water level data; and remove abnormal points from the candidate river water level data based on a preset normal water level distribution pattern to generate river water level data;

[0100] In this embodiment, the radar pulse transmission and recovery delay data of the river area are collected by the Ku-band radar altimeter carried by the altimetry satellite. When the radar pulse is transmitted, the transmission time is recorded by the satellite-borne high-precision time and frequency reference device. When it is recovered, the radar receiver receives the surface echo signal and determines the moment of the echo energy center, calculates the round-trip delay of the radar pulse, and calculates the surface height data in combination with the satellite orbit position to obtain the initial river water level data. The height deviation introduced by the satellite attitude correction error is corrected, and the water level deviation is corrected using the intersection water level data using the orbit intersection calibration method. The outlier probability is calculated using the regional water level time series change model, and the outlier points that exceed the preset confidence interval are eliminated to finally generate the river water level data.

[0101] S2: Water remote sensing module, used to collect river water images through remote sensing satellites, extract water boundary features from river water images, and deduce water area change data based on water boundary features;

[0102] In this embodiment, high-resolution remote sensing images of river water bodies are collected by multispectral sensors carried by remote sensing satellites, and the normalized difference water index (NDWI) is used to calculate the reflectivity difference between water body areas and non-water body areas. The image is segmented and the water body boundary is extracted. The water body boundary drift is calculated using a time series image comparison method. The water body area change trend is statistically calculated based on the pixel scale change. The water body area change data is refined through a dynamic interpolation method of the sub-region water body boundary point set to generate high-precision water body area change data.

[0103] S3: Meteorological wind shear analysis module, used to collect river meteorological data through meteorological satellites; perform microwave scattering measurement on the river meteorological data and invert the river wind speed to obtain the water surface wind speed parameters; and deduce the wind flow shear torque based on the water surface wind speed parameters to generate wind-induced shear stress parameters;

[0104] In this embodiment, the atmospheric wind speed and direction data above the river channel are collected by a microwave scatterometer carried by a meteorological satellite. The water body wind speed is calculated based on the microwave scattering characteristics of the water surface. A two-parameter model is used to analyze the wind speed influencing factors. The shear torque of the river flow is calculated based on the wind speed gradient distribution. The boundary layer fluid mechanics model is used to analyze the wind-induced shear stress parameters. The wind speed data is then smoothed in time series to eliminate abnormal fluctuations.

[0105] S4: Flow estimation module, used to perform dynamic cross-sectional analysis based on river water level data and water area change data to generate the cross-sectional area of ​​each river area; and estimate the river water flow based on the cross-sectional area of ​​each river area and river water level data;

[0106] In this embodiment, based on the river water level data and water area change data, the river cross-section point-by-point interpolation method is used to construct the water-passing section morphology, the instantaneous flow of each area of ​​the river is calculated according to the water-passing section area, the multi-section flow velocity weighted method is used to estimate the river water flow, the cross-section water flow velocity distribution is analyzed by the water flow dynamics equation, and the water flow calculation accuracy is optimized in combination with the measured water level data to generate river water flow data.

[0107] S5: Flow correction module, used to perform wind shear interference correction on river water flow based on wind-induced shear stress parameters to generate corrected river water flow; monitor the corrected river water flow in real time and upload it to the control terminal synchronously.

[0108] In this embodiment, wind shear interference correction is performed on the river water flow based on the wind-induced shear stress parameters, the flow velocity distribution parameters are adjusted using the wind shear influence weight correction method, the influence of wind speed gradient changes on flow calculation is analyzed, the wind shear correction coefficient is calculated, and the river water flow data is corrected using the flow deviation correction algorithm. The corrected river water flow is monitored in real time, and the data acquisition terminal is synchronously uploaded to the ground control center via the low-orbit data relay satellite.

[0109] Preferably, the water level height measurement module includes the following functions:

[0110] S11: Collecting altimetry satellite multi-beam radar data through altimetry satellites; performing pulse compression processing on the altimetry satellite multi-beam radar to obtain radar pulse recovery delay data, where the radar frequency range is 13.5GHz-14.0GHz, the pulse width range is 3.2-3.8μs, and the delay resolution is 0.1ns;

[0111] S12: performing phase differential correction on the radar pulse recovery delay data to generate denoised delay data, wherein the phase differential range is -180° to 180° and the delay correction range is -0.5 to 0.5 nanoseconds; performing refraction mapping water level inversion on the denoised delay data to obtain original water level height data;

[0112] S13: Perform atmospheric delay water level compensation on the original water level height data and perform ellipsoid fitting correction to obtain ellipsoid-corrected water level data;

[0113] S14: performing local coordinate projection transformation on the ellipsoid-corrected water level data to generate candidate river channel water level data; performing distribution fitting analysis on the candidate river channel water level data to obtain water level distribution deviation data;

[0114] S15: performing abnormal point detection on the water level distribution deviation data based on a preset normal water level distribution pattern to obtain abnormal water level data;

[0115] S16: performing isolated abnormal point verification on the abnormal water level data to generate verified isolated abnormal points; performing interpolation filling processing on the verified isolated abnormal points to generate river water level data.

[0116] In this embodiment, radar echo data from the river channel is collected using a multibeam radar carried by an altimeter satellite. The altimeter satellite multibeam radar operates in the Ku band with a frequency range of 13.5 GHz to 14.0 GHz. It transmits radar pulse signals with a pulse width set between 3.2 and 3.8 microseconds. The echo signals are received by the onboard radar receiver and pulse compressed to improve ranging accuracy. Pulse compression uses matched filtering to calculate the radar echo delay data with a delay resolution of less than 0.1 nanoseconds. Phase differential correction is then performed on the radar pulse recovery delay data. Coherent demodulation is first used to extract the phase information of the radar echo signal and calculate the phase differential value. The phase differential range is set between -180° and 180°. Phase compensation correction is then performed on the echo delay within a range of -0.5 to 0.5 nanoseconds. After correction, the refraction mapping algorithm is used to invert the water level. The refraction mapping parameters are calculated based on the incident angle of the altimeter satellite, the surface medium characteristics and the electromagnetic wave propagation model to generate the original water level height data. The original water level height data is compensated for the atmospheric delay water level. The delay error of the signal when propagating in the troposphere and ionosphere is calculated. The dual-frequency correction method is used to analyze the ionospheric refraction delay. The atmospheric water vapor content is measured by the onboard water vapor radiometer. The tropospheric refraction error is calculated in combination with the tropospheric model to compensate the water level data. The compensated data is then corrected by ellipsoid fitting. The least squares fitting method is used to construct the ellipsoid benchmark model. The measurement point data is aligned with the ellipsoid benchmark to generate the ellipsoid-corrected water level data. The ellipsoid-corrected water level data is transformed into the local coordinate projection and the Gauss-Krüger projection is used. The global coordinate system is converted into the local coordinate system of the river channel by the Projection method, and the water level data is spatially matched to generate candidate river channel water level data. The candidate river channel water level data is subjected to distribution fitting analysis, and the water level distribution deviation data is calculated by the Chi-Square Fitting method. The changing trend of the water level data is analyzed to generate water level distribution deviation data. Based on the preset normal distribution pattern of the water level, the water level distribution deviation data is detected for outliers. First, a normal distribution model is constructed, the mean and standard deviation of the water level data are calculated, and a confidence interval is set. The water level data that exceeds the confidence interval is marked. The boxplot analysis method is used to detect abnormal water level data, screen out abnormal points, and perform isolated outlier check on the abnormal water level data. The local density deviation of the abnormal water level data is calculated using the local outlier factor (LOF) method. The data with significant density deviation are marked as isolated outliers. Then, the Lagrange interpolation method is used to calculate the local density deviation of the abnormal water level data. The interpolation method is used to interpolate and fill in isolated abnormal points, calculate the changing trend of the surrounding water level data, perform data smoothing, and finally generate river water level data.

[0117] Preferably, performing atmospheric delay water level compensation on the original water level height data and performing ellipsoid fitting correction includes:

[0118] Extract atmospheric delay parameters from raw water level height data;

[0119] Calculate atmospheric delay amount for atmospheric delay parameters;

[0120] Perform delay compensation processing on the original water level height data based on the atmospheric delay to obtain the atmospheric correction water level;

[0121] Initialize the ellipsoid model parameters for the atmospheric correction water level to generate the ellipsoid model parameters, where the number of iterations is 5-15 times and the error convergence threshold is 0-0.001m;

[0122] Fit and optimize the atmospheric correction water level and ellipsoid model parameters to obtain the optimized ellipsoid parameters;

[0123] The atmospheric correction water level is subjected to ellipsoid correction according to the optimized ellipsoid parameters to obtain ellipsoid-corrected water level data.

[0124] In this embodiment, the atmospheric delay parameters of the original water level height data are extracted, and the atmospheric water vapor content data of the altimeter satellite transit area is collected using a satellite-borne water vapor radiometer, and the water vapor brightness temperature is recorded. The ionospheric delay is calculated using the dual-frequency radar echo data, wherein the low-frequency channel of the dual-frequency radar is set at 5GHz-7GHz and the high-frequency channel is set at 13GHz-14GHz. The dual-frequency difference method is used to calculate the ionospheric delay. The tropospheric refraction delay is calculated according to the atmospheric refraction model. The model parameters include temperature, air pressure and water vapor density. The data comes from the satellite-borne meteorological sensor and the ground meteorological station. The calculated ionospheric delay and tropospheric delay are used as the atmospheric delay parameters. The atmospheric delay parameters are calculated using the atmospheric refraction model. Delay, first normalize the atmospheric delay parameters, convert the ionospheric delay to millimeter level, and convert the tropospheric delay to centimeter level, then calculate the total atmospheric delay, set the ionospheric delay weight to 0.3, the tropospheric delay weight to 0.7, and use the weighted summation formula L=0.3L1+0.7L2 to calculate the atmospheric delay L, where L1 is the ionospheric delay and L2 is the tropospheric delay. Based on the atmospheric delay, the original water level height data is delayed and compensated to obtain the atmospheric correction water level. First, the original water level height data is converted to geodetic height, and the global navigation satellite system (Global Using the Earth-Centered Earth-Fixed (ECEF) coordinate system provided by the Global Navigation Satellite System (GNSS) as a reference, the raw water level data was converted to geocentric coordinates. The raw water level data was then corrected according to the delay calculated by L, using the calculation Hc = Ho - L, where Hc is the atmospherically corrected water level and Ho is the raw water level data. The ellipsoid model parameters were initialized using the atmospherically corrected water level to generate the ellipsoid model parameters. The number of iterations ranged from 5 to 15, with an error convergence threshold of 0 to 0.001 m. First, an initial ellipsoid model was established, with the initial semi-major axis a0 and initial flattening f0 set. The ellipsoid surface was fitted using the Hc data points, and the fitting error was calculated using the least squares method. The maximum number of iterations, N, was set to 15, and the error convergence threshold, ε, was set to 0.001m, and after each iteration, the ellipsoid parameters are updated as ai+1=ai-Δa and fi+1=fi-Δf, where Δa and Δf are fitting error corrections, until the error convergence conditions are met. The atmospheric correction water level and ellipsoid model parameters are fitted and optimized to obtain the optimized ellipsoid parameters. First, the normal distance from the atmospheric correction water level data point to the initial ellipsoid surface is calculated, where bopt=aopt(1-fopt). Then, the Hc data point is projected onto the optimized ellipsoid surface, and the corrected water level height Hfinal is calculated using Hfinal=Hc+ΔH, where ΔH is the projection error correction. Finally, the ellipsoid-corrected water level data is output.

[0125] Preferably, the water remote sensing module collects river water images through remote sensing satellites and extracts water boundary features of the river water images, including:

[0126] Collect optical imaging data through remote sensing satellites;

[0127] Perform radiation correction on the optical imaging data and perform atmospheric elimination processing to obtain river water images;

[0128] Perform multi-view processing on SAR satellite microwave imaging data and extract scattering feature data;

[0129] Perform polarization phase decomposition on the scattering characteristic data and partition the backscattering pattern to obtain the polarization scattering pattern of the water body;

[0130] Perform tensor coupling processing on river water images and water polarization scattering patterns to generate remote sensing image fusion river data;

[0131] Perform water boundary segmentation and analysis on the river data fused with remote sensing images to obtain river water mask data;

[0132] The water boundary features of river water images are extracted based on river water mask data.

[0133] In this embodiment, optical imaging data is collected by remote sensing satellites, and high-resolution multispectral remote sensing satellites are selected. The observation time window is set to 10:00 am to 2:00 pm. The working bands of the optical sensor include the visible light band of 400nm to 700nm and the near-infrared band of 700nm to 1100nm. The spatial resolution of the remote sensing image is set to 0.5m to 1m. The image data storage format adopts the GeoTIFF format (geographically tagged image format), and the transmission protocol adopts the CCSDS standard protocol. The optical imaging data is subjected to radiation correction and atmospheric elimination processing to obtain a river water image. The radiation correction adopts a linear transformation model. It is assumed that the original DN value (Digital Number) of the remote sensing image is , the radiation correction formula is ,in, is the gain coefficient, is the offset, and the unit of the corrected data is The atmospheric elimination processing uses the FLAASH (Fast Line-of-sight Atmospheric Analysis of SpectralHypercubes) model to perform aerosol scattering correction on the optical image data. The input parameters include the solar zenith angle, sensor altitude, and aerosol type (the continental aerosol mode is selected). The final output is the apparent reflectance image, which is stored in the ENVI (Environment for Visualizing Images) standard format. Multi-look processing is performed on the SAR satellite microwave imaging data, and scattering feature data is extracted. C-band (wavelength 5.6 cm) SAR satellite data is selected, and the polarization modes are HH (horizontal transmission and horizontal reception), HV (horizontal transmission and vertical reception), and VV (vertical transmission and vertical reception). The multi-look processing uses the sliding window mean filtering method with a window size of 5×5 pixels to calculate the backscattering coefficient. (Unit: dB), the calculation formula is: ;in is the calibration coefficient of the SAR system, and the backscatter data of the SAR image is finally obtained. The storage format is HDF5 (Hierarchical Data Format version 5). The scattering feature data is subjected to polarization phase decomposition and backscattering pattern partitioning to obtain the polarization scattering pattern of the water body. The Cloude-Pottier polarization decomposition method is used to input the three polarization channel data of HH, HV, and VV to calculate the polarization coherence matrix. The matrix dimension is , perform eigenvalue decomposition, extract scattering mechanism components, including surface scattering, volume scattering, and secondary scattering, set the scattering angle threshold to 30°, divide the water area into specular reflection area, rough water surface area, and mixed scattering area, output water polarization scattering pattern image, and store it in GeoTIFF format. Perform tensor coupling processing on river water image and water polarization scattering pattern to generate remote sensing image fusion river data. Use high-order tensor decomposition model to construct optical image data and SAR polarization scattering pattern data into a three-dimensional tensor, set the tensor rank to 3, and use CP decomposition (CANDECOMP / PARAFAC, canonical decomposition / parallel factor decomposition) algorithm for feature coupling calculation. The calculation formula is: ;in is the fused data tensor, 、 、 The decomposition factors of optical band characteristics, polarization scattering characteristics and spatial coordinate characteristics are respectively generated, and the remote sensing image fusion river data are finally generated. The storage format is NetCDF (Network Common Data Form). The water body boundary segmentation and analysis of the remote sensing image fusion river data are performed to obtain the river water mask data. The deep learning semantic segmentation method based on U-Net is adopted. The input image data size is 512×512 pixels. The U-Net network adopts a four-layer encoder-decoder structure, the activation function is ReLU (Rectified Linear Unit), the loss function is cross entropy loss, the optimization algorithm is Adam (Adaptive Moment Estimation), the learning rate is set to 0.001, the maximum training round is 100 rounds, and the final output is binary mask data, in which the water area is marked as 1 and the non-water area is marked as 0. The storage format is Shapefile (ESRI Shapefile, geographic information system vector data format), the water boundary features of the river water image are extracted based on the river water mask data, the Canny edge detection algorithm is used, the low threshold is set to 50, the high threshold is set to 150, and the Sobel operator is used to calculate the gradient direction. The calculation formula is: ;in and The gradients in the horizontal and vertical directions are used respectively. Morphological closing operation is used to fill the broken areas of the river boundary, and finally the water body boundary vector data is output. The storage format is GeoJSON (Geospatial JavaScript Object Notation).

[0134] Preferably, the water body remote sensing module deduces water body area change data based on water body boundary characteristics, including:

[0135] The boundary features of the water body are located by using navigation satellites to obtain optimized water body boundary features;

[0136] Based on the optimized water body boundary features, the water body pixels within the boundary of the river water body image are extracted to obtain the number of water body pixels within the boundary;

[0137] Based on the preset satellite imaging spatial resolution, the number of water body pixels within the boundary is analyzed for point changes to generate boundary change data;

[0138] Water area mapping is performed on the river water body image based on the boundary change data to obtain water body area change data.

[0139] In this embodiment, the boundary positioning of water body boundary features is performed by navigation satellites to obtain the specific implementation method of optimizing water body boundary features. First, a remote sensing satellite equipped with a high-precision global navigation satellite system (GNSS) is selected, and the coordinates of key points of the river water body boundary are acquired by using real-time dynamic differential positioning (RTK) technology. The GNSS receiver acquires the original observation data at a sampling frequency of 1 Hz, and calculates the coordinates in combination with the differential correction data of the ground reference station. The high-precision position data is solved by the carrier phase observation value, and the accuracy of the water body boundary point is controlled at the centimeter level. Secondly, the obtained water body boundary coordinates are matched with multi-phase data in combination with optical remote sensing images and microwave remote sensing data. The Kalman filter algorithm is used to smooth the discontinuous or noise-affected boundary points, and the outliers are eliminated, so as to finally form optimized water body boundary feature data. Based on the optimized water body boundary features, The specific implementation method for extracting water pixels within river water image boundaries and obtaining the number of water pixels within the boundary includes first converting the optimized water boundary features into a raster format and overlaying them onto optical remote sensing and SAR (synthetic aperture radar) images. A region growing algorithm is then used to automatically extract pixels within the water boundary. The initial seed point of the region growing algorithm is determined by the center point of the optimized water boundary features. The growth judgment condition is set as the spectral reflectance or backscatter coefficient of adjacent pixels meeting the water feature threshold. The water spectral features of the optical image are calculated based on the Normalized Difference Water Index (NDWI). The calculation formula is NDWI = (G-NIR) / (G+NIR), where G represents the reflectance of the green band and NIR represents the reflectance of the near-infrared band. The threshold is set to NDWI > 0.3 is determined to be a water pixel. At the same time, the Otsu adaptive threshold segmentation method is used to determine the water backscattering threshold of the SAR image, and the local mean filter is applied to the high noise area to ensure the accurate extraction of water pixels. Finally, the number of water pixels within the boundary is calculated, and the number of water pixels within the boundary is analyzed based on the preset satellite imaging spatial resolution. The specific implementation method of generating boundary change data includes: first, determining the spatial resolution of satellite imaging. The spatial resolution of optical remote sensing images is usually 10m, and the resolution of SAR images is usually 5m. When the image resolution is known, the actual ground area represented by each pixel can be calculated by the formula A = R^2, where A represents the actual ground area of ​​a single pixel, and R represents the imaging resolution. For example, the area of ​​a single pixel in an optical remote sensing image is 100 square meters, and the area of ​​a single pixel in a SAR image is 25 square meters. Secondly, the number of water pixels within the boundary at the current moment is compared with the number of pixels at the previous moment, and the change ratio P is calculated. The formula is P = (N_t -N_t-1) / N_t-1, where N_t represents the number of water body pixels at the current moment, and N_t-1 represents the number of water body pixels at the previous moment. When the absolute value of P is greater than the set threshold (such as 5%), it is determined that the water body boundary has changed significantly, and the coordinate information of the changed area is marked. Finally, time series data containing boundary change trends are generated. Based on the boundary change data, the river water body image is mapped to the water body area to obtain the specific implementation method of water body area change data. First, the boundary change data is matched with the optimized water body boundary features, and the least squares method is used to fit the water body area change trend. The objective function of the least squares method is F(x) = Σ(y_i - (a*x_i + b))^2, where x_i represents the observation time in the time series, y_i represents the water area at the corresponding time, and a and b are fitting parameters. Next, the rate of change of the water area is calculated using the formula dA / dt = (A_t - A_t - 1) / (t_t - t_t - 1), where A_t represents the water area at the current time, A_t - 1 represents the water area at the previous time, and t_t and t_t - 1 are the timestamps of the corresponding time. When the absolute value of dA / dt exceeds a set threshold (e.g., 500 square meters / day), a significant change in water area is determined, and the spatial coordinates of the changed area are marked. Finally, the water area change data is stored in a spatiotemporal database and visualized using a Geographic Information System (GIS) to generate a dynamic monitoring map of river water area changes.

[0140] Preferably, the meteorological wind shear analysis module performs microwave scattering measurement on river meteorological data and performs river wind speed inversion, including:

[0141] Performing denoising transformation on river meteorological data to obtain denoised river meteorological data;

[0142] Decompose the characteristic spectrum of denoised river meteorological data in layers to obtain river meteorological characteristic data;

[0143] The radar scattering model is inverted on the river meteorological characteristic data to obtain the initial scattering coefficient;

[0144] The initial scattering coefficient is compensated for the microscale wave roughness to obtain the corrected scattering coefficient;

[0145] Perform water body electromagnetic feature mapping on the corrected scattering coefficient to generate water surface scattering characteristics;

[0146] Multi-angle polarization scattering fusion is performed on the water surface scattering characteristics to obtain multi-view scattering data;

[0147] performing directional scattering energy correction on the multi-view scattering data to obtain corrected scattering data;

[0148] The wind field dynamics deconstruction is performed on the corrected scattering data, and wind speed parameter mapping is performed to obtain the wind speed parameters on the water surface.

[0149] In this embodiment, raw meteorological data acquired by multiple satellite platforms are collected, and the wavelet transform (WT) method is used to decompose the river meteorological data in the time-frequency domain. The high-frequency noise components are selected for threshold filtering, and the low-frequency main information components are reconstructed into a denoised meteorological data set. The meteorological data are adaptively decomposed by the empirical mode decomposition (EMD) method to remove the high-frequency noise components. The meteorological data matrix is ​​further decomposed by the singular value decomposition (SVD) method to eliminate the noise data corresponding to small singular values, thereby obtaining a low-noise river meteorological data matrix. Subsequently, the median filtering method is used to remove isolated outliers in the data, and finally the denoised river meteorological data is output. The denoised river meteorological data is subjected to a short-time Fourier transform (STFT) to obtain a time-frequency domain distribution matrix. The signal power spectral density (PSD) is used to calculate the signal power spectral density (PSD). The signal characteristics in multiple frequency bands, such as 0.01Hz-0.1Hz, 0.1Hz-1Hz, and 1Hz-10Hz, were extracted using wavelet packet decomposition (WPD) to further decompose the signal characteristics in each frequency band. A hierarchical spectral feature data matrix was obtained. The principal component analysis (PCA) method was used to extract the main characteristic components, and the Mahalanobis distance method was used to eliminate abnormal frequency points, thereby obtaining high-confidence river meteorological characteristic data. The backscatter coefficient (σ0) of the meteorological characteristic data was calculated based on the radar equation. The shortwave disturbance characteristics of the wind field were analyzed using the Bragg scattering model. The effects of factors such as radar wavelength, incident angle, and polarization state on backscattering were calculated. The Mouche model or CMOD5 model was used to perform a preliminary inversion of wind speed and direction to obtain the numerical distribution of the initial scattering coefficient σ0. The mean square slope (MSS) of the microscale waves on the water surface was calculated. The two-scale model is used to separate the short-wave and long-wave contributions of the waves, and the wave spectrum model (such as the Elfouhaily spectrum model) is combined to calculate the roughness effect caused by the waves. The iterative optimization method is used to correct the scattering coefficient σ0, and finally the corrected scattering coefficient is obtained. The reflectivity of the water surface to radar waves of different polarization states is calculated based on the Fresnel reflection law.The electromagnetic scattering characteristics of the water surface are calculated based on the radar incident angle and polarization direction. The scattering response at different electromagnetic wave frequencies is calculated using the microwave radiation transfer model (RTM), ultimately generating a water surface scattering characteristic dataset. The scattering data acquired at different angles and polarization modes are geometrically aligned. The polarimetric synthesis method is used to fuse the HH polarization (horizontal transmit-horizontal receive), VV polarization (vertical transmit-vertical receive), and HV polarization (horizontal transmit-vertical receive) data. The polarization scattering data is decomposed using the Cloude-Pottier decomposition method to extract the anisotropic scattering characteristics of the water surface and generate multi-view scattering data. The scattering intensity at different observation angles is calculated based on the bidirectional reflectance distribution function (BRDF). The scattering angle normalization method is used to transform the scattering data at each angle into a unified standard coordinate system. The scattering data are fitted using the least squares regression method to eliminate the energy deviation between different perspectives, ultimately generating corrected scattering data. The influence of wind speed on the water surface is calculated based on wind stress and wave generation mechanisms. The improved Log-Wind The wind speed profile is calculated using the Profile model, and the vertical distribution of wind speed is optimized by combining it with the Turbulent Boundary Layer Model. The corrected scattering data is mapped to the wind speed parameter space using the Neural Network Regression algorithm, ultimately obtaining the wind speed parameters on the water surface.

[0150] Preferably, the deduction of wind shear torque based on water surface wind speed parameters in the meteorological wind shear analysis module includes:

[0151] Reconstruct the spatiotemporal flow field of the wind speed parameters on the water surface to obtain continuous wind field distribution data;

[0152] Based on the continuous wind field distribution data, the fluid boundary layer analysis of the wind speed parameters on the water surface is carried out to generate the vertical distribution data of the wind speed;

[0153] Fit the flow field logarithmic function to the vertical distribution data of wind speed to obtain wind shear gradient data;

[0154] Perform gas dynamic density compensation on wind shear gradient data to generate density-corrected wind shear data;

[0155] Calculate aerodynamic drag from density-corrected wind shear data;

[0156] Perform drag coefficient fitting on aerodynamic wind resistance to generate wind resistance coefficient;

[0157] Wind-induced stress calculation is performed on the density-corrected wind shear data and wind drag coefficient to generate wind-induced shear stress parameters.

[0158] In this embodiment, the spatiotemporal flow field of the wind speed parameters on the water surface is reconstructed to obtain continuous wind field distribution data. It is necessary to interpolate and fuse the wind speed data based on remote sensing images and the wind speed data measured by ground meteorological stations. The three-dimensional Kriging interpolation method is used to perform spatial interpolation calculations on each wind speed data point to construct a preliminary wind field distribution. The Kalman filter method is used to dynamically correct the wind speed changes in the time series to ensure that the wind speed data maintains continuity in the time dimension. To address the spatial discreteness of the wind speed data, a finite volume method is used. The wind speed data is gridded and discretized using the fluid dynamics equations of FVM. The surface area of ​​the water body is divided into several finite volume units. The wind speed value in each unit is calculated by the control equation. The fluid mass conservation and momentum conservation equations are iteratively solved to finally form spatially continuous wind field distribution data. Based on the continuous wind field distribution data, the fluid boundary layer analysis of the wind speed parameters on the water body surface is performed to generate vertical wind speed distribution data. The air-water interface boundary layer flow model is constructed and the Reynolds-averaged Navier-Stokes (RANS) method is used.The Reynolds-Averaged Navier-Stokes equation describes the shear flow of wind speed on the water surface. The k-epsilon turbulence model is combined to calculate the wind speed change in the boundary layer. Assuming that the wind speed is logarithmically distributed along the height direction in the boundary layer, the wind speed in the boundary layer can be expressed as U(z)=(u* / κ)ln(z / z0), where U(z) represents the wind speed at height z, u* is the friction speed, κ is the Von Kármán constant, and z0 is the roughness length. The wind speed data of the water surface in different areas are fitted to obtain the vertical distribution data of wind speed at different heights on the water surface. The vertical distribution data of wind speed are fitted with the logarithmic function of the flow field to obtain the wind shear gradient data. It is necessary to use the least squares method (Least Squares) based on the vertical distribution data of wind speed. The logarithmic wind speed distribution function is fitted using the nonlinear least squares optimization method, and the optimal parameter values ​​of the logarithmic wind speed distribution equation are solved using the Newton's method. The gradient descent solution is used to ensure that the vertical wind speed distribution data can accurately match the logarithmic wind speed distribution model. When calculating the wind speed gradient, the finite difference method is used to calculate the wind speed change rate between adjacent heights, that is, the wind shear gradient. The wind shear gradient is calculated as dU / dz≈(U(z+Δz)-U(z)) / Δz, where Δz is the height increment. Finally, the wind shear gradient data on the water surface is obtained. The wind shear gradient data is compensated for gas dynamics density to generate density-corrected wind shear data. The influence of air density ρ needs to be considered. The International Standard Atmosphere model (ISAM) is used.Air density at different altitudes is calculated using the ISA (International Air Transport Association) method. The air density calculation formula is ρ = P / (RT), where P is the air pressure, R is the air gas constant, and T is the temperature. Based on the wind shear gradient data, a density-weighted correction is performed on the wind speed gradient at different altitudes. The density-corrected wind speed gradient is calculated using ρ(z) * dU / dz to compensate for the wind shear error caused by air density changes. This ultimately yields density-corrected wind shear data. Calculating the aerodynamic drag from this density-corrected wind shear data requires the calculation method for the drag coefficient Cd. The wind resistance F can be calculated using the formula F = 0. 5*ρ*Cd*A*U^2 is calculated, where A is the frontal area and U is the wind speed. The calculation of the drag coefficient Cd adopts the fluid dynamics experimental measurement value, and combined with the air turbulence model correction, the flow field simulation is used to calculate the relationship between wind speed and wind resistance, and the aerodynamic wind resistance corresponding to the density-corrected wind shear data is obtained. The drag coefficient is fitted for the aerodynamic wind resistance to generate the wind drag coefficient. It is necessary to compare and analyze the experimental measurement value with the fluid calculation result, and the least squares method is used to fit the empirical formula of the wind drag coefficient Cd. The wind drag coefficient is usually related to the Reynolds number (Reynolds The calculation formula is Re=ρUL / μ, where L is the characteristic length and μ is the aerodynamic viscosity. The calculation model of the wind resistance coefficient Cd is adjusted according to different Reynolds number ranges to make it consistent with the actual wind resistance data, and finally the wind resistance coefficient data is obtained. The wind-induced stress calculation is performed on the density-corrected wind shear data and the wind resistance coefficient to generate the wind-induced shear stress parameter. The wind-induced stress calculation formula τ=ρ*Cd*U^2 is used to calculate the wind-induced shear stress, where τ is the wind-induced shear stress, ρ is the air density, Cd is the wind resistance coefficient, and U is the wind speed. The numerical integration method is used to calculate the wind-induced shear stress distribution on the entire water surface area. The wind-induced stress values ​​under different wind speeds and resistance coefficients are numerically solved to finally obtain the wind-induced shear stress parameter data.

[0159] Preferably, the flow estimation module performs dynamic cross-section analysis based on river water level data and water area change data, including:

[0160] Deducing the river width at different times based on the water area change data;

[0161] Align the timestamps of river width and river water level data at different times to obtain synchronized hydrological data;

[0162] Based on the synchronized hydrological data, the river channel spatial registration is performed to obtain the hydrological data corresponding to the location;

[0163] Intercept the river cross-section distribution data based on the hydrological data corresponding to the location;

[0164] Based on the location-corresponding hydrological data and the river cross-sectional distribution data, the water-flowing cross-sectional area of ​​each region of the river is inferred.

[0165] In this embodiment, multi-temporal remote sensing image data is obtained and geometric correction is performed on the remote sensing image to ensure the spatial accuracy of the data. Index) or deep learning-based water body detection networks (such as U-Net) are used to extract water body regions from remote sensing images. The extracted water body mask is used to calculate the water body area at each moment. Based on the water body area and known river topography data, combined with the width-area change relationship curve in historical hydrological data, the river width at different moments is derived using curve fitting or interpolation. At the same time, the derived river width is smoothed in time series to remove outliers caused by short-term fluctuations and ensure that the temporal resolution of the water level data matches the temporal resolution of the water area change data. If the time interval of the water level data is less than the time interval of the water area change data, the water level data is downsampled using a moving average or linear interpolation method. If the time interval of the water level data is greater than the time interval of the water area change data, the water area data is interpolated using the historical water level change trend to match the time point of the water level data. Subsequently, the water level data and river width data at the same moment are merged through a timestamp-based index matching algorithm to generate synchronized hydrological data, and an outlier removal method is used, such as an IQR (interquartile range)-based method. Range) or Z-score (standard score) methods are used to remove outliers in the data. When performing spatial registration of river channels based on synchronized hydrological data, the geographic information data of the river channels, including elevation data, topographic maps, and river centerline data, is obtained. SIFT (Scale-Invariant Feature Transform) or ORB (Oriented FAST and Rotated Binary Features) based on image registration is used. BRIEF) algorithm is used to match water body boundary points with existing river boundary data, spatially align the synchronized hydrological data, and unify the data into the same geographic coordinate system. If there is an offset, it is corrected using rigid transformation (RigidTransformation), and data interpolation is performed in combination with the river centerline data to fill in the spatial breakpoints caused by image occlusion or data missing, and finally generate position-corresponding hydrological data. When intercepting the river cross-sectional distribution data based on the position-corresponding hydrological data, the river cross-sectional interception rules are first defined. Based on the river centerline data, at a certain spatial interval (for example, a section is intercepted every 500 meters), a cross-sectional sampling line is constructed at each interception position along the vertical direction of the river. The river water level data is used to calculate the water level height on each cross section to determine the water surface boundary. The terrain elevation data on the cross-sectional sampling line is clipped to obtain the underwater terrain contour. The water surface boundary data and the terrain elevation data are combined to construct the river cross-sectional distribution data.The cross-sectional shape is smoothed using curve fitting or cubic spline interpolation to eliminate the influence of discrete points. When inferring the cross-sectional area of ​​each region of the river channel from the cross-sectional distribution data of the river channel based on the corresponding hydrological data at the location, the aforementioned cross-sectional data is used to calculate the underwater area of ​​each cross-sectional area, and a numerical integration method (such as the trapezoidal integration method or the Simpson integration method) is used to calculate the cross-sectional area of ​​each section. Specifically, on each cross-sectional area, the area between the water surface boundary line and the terrain elevation line is divided into multiple small trapezoidal or curved units, and the area of ​​each unit is calculated separately. The areas of all units are summed to obtain the cross-sectional area of ​​the corresponding section. Subsequently, a velocity distribution model (such as the Manning formula or the two-dimensional shallow water equation model) is used to infer the velocity distribution of different regions of the river channel, and the velocity distribution is combined with the cross-sectional area of ​​the water flow.

[0166] Preferably, the flow estimation module estimates the flow of the river based on the cross-sectional area of ​​each region of the river and the river water level data, including:

[0167] Calculate the wetted perimeter parameters of each river channel area based on the cross-sectional area of ​​each river channel area and the river water level data of the corresponding river channel area;

[0168] Derivation of hydraulic radius data for each region of the river channel based on the cross-sectional area of ​​each region of the river channel and the wetted perimeter parameters of each region of the river channel;

[0169] Mapping the hydraulic gradient of various areas of the river based on river water level data;

[0170] The Manning formula is used to estimate the flow rate of the river water body based on the preset river channel Manning roughness coefficient, the cross-sectional area of ​​each river channel area, the hydraulic radius data of each river channel area, and the hydraulic slope of each river channel area.

[0171] Among them, the Manning formula is specifically:

[0172] ;

[0173] Among them, the formula is the river water flow, is the Manning roughness coefficient, is the cross-sectional area of ​​water flow, is the hydraulic radius data, is the hydraulic slope.

[0174] In this embodiment, when calculating the wetted perimeter parameters of each river channel region based on the cross-sectional area of ​​each river channel region and the river water level data of the corresponding river channel region, cross-sectional data of different river channel regions are first obtained. This data is obtained by remote sensing image mapping, drone laser radar bathymetry (LiDAR Bathymetry), or measured water depth profile data. Water surface boundary line data is extracted based on the remote sensing image, and the riverbed topography is determined by combining digital elevation model (DEM) data. After the river channel cross-sectional profile model is established, multiple discrete points are divided on each cross-section. Each discrete point includes lateral position coordinates, elevation values, and water level data. When calculating the wetted perimeter parameters, it is necessary to identify the boundary line where the water flow contacts the riverbed. The boundary line is interpolated using a curve fitting algorithm to discretize it into multiple short line segments. The length of each short line segment is calculated, and the lengths of all short line segments are summed to obtain the wetted perimeter parameter data of each river channel region. When deriving the hydraulic radius data of each river channel region based on the cross-sectional area of ​​each river channel region and the wetted perimeter parameters of each river channel region, the aforementioned calculation is first used. The hydraulic radius R is calculated based on the obtained wetted perimeter parameter P and the water-passing cross-sectional area A. The calculation formula of the hydraulic radius R is R=A / P, where A is the water-passing cross-sectional area and P is the wetted perimeter parameter. When calculating, it is necessary to ensure that the units of all parameters are consistent. For example, the unit of A is set to square meters, the unit of P is set to meters, and the unit of R is meters. In actual operation, the water-passing cross-sectional area can be calculated by the numerical integration method, and the wetted perimeter parameter is calculated by the curve discretization method. After substituting the two into the calculation formula, the hydraulic radius data of each area of ​​the river channel are obtained. When mapping the hydraulic slope of each area of ​​the river channel according to the river water level data, the river channel is first obtained using the multi-satellite remote sensing water level monitoring data. The water level data at different sections are used to calculate the longitudinal profile of the river channel based on the digital elevation model (DEM) data and the measured riverbed data. The water level values ​​are extracted at different section positions, and the water level difference Δh between adjacent sections is calculated. The horizontal distance ΔL between adjacent sections is calculated. The calculation formula of the hydraulic gradient S is S=Δh / ΔL, where Δh is the water level difference and ΔL is the horizontal distance between adjacent sections. During the calculation, the hydraulic gradients between all adjacent sections are spatially interpolated to obtain the hydraulic gradient data of each area of ​​the river channel. Based on the preset river channel Manning roughness coefficient, the water flow cross-sectional area of ​​each area of ​​the river channel, and the hydraulic gradient of each area of ​​the river channel, the hydraulic gradient of each area of ​​the river channel is calculated. When the radius data and the hydraulic gradient of each area of ​​the river are used to estimate the flow rate of the river water body through the Manning formula, the Manning formula is Q=(1 / n)*A*R^(2 / 3)*S^(1 / 2), where Q is the flow rate of the river water body, n is the Manning roughness coefficient, A is the cross-sectional area of ​​the water flow, R is the hydraulic radius data, and S is the hydraulic gradient. When calculating, first determine the Manning roughness coefficient n of each area of ​​the river. This parameter is related to the riverbed material, vegetation cover and flow rate, and can be determined by experimental data or empirical formulas. After obtaining the A, R, and S data, substitute each parameter into the Manning formula to calculate Q. When calculating, the flow rates of different cross-sectional areas need to be accumulated.To obtain the total flow of the entire river.

[0175] Preferably, the flow correction module includes the following functions:

[0176] Perform virtual river simulation on the river water flow to obtain simulated river water body;

[0177] The wind field is reconstructed based on the wind-induced shear stress parameters, and the wind shear interference simulation is performed on the simulated river water body based on the reconstructed wind field to obtain the simulated wind shear interference water body data;

[0178] Calculate the difference in water flow between simulated wind shear disturbance water body data and simulated river water body;

[0179] Perform wind shear interference correction on the river water flow according to the water flow difference to generate the corrected river water flow;

[0180] The water flow of the corrected river channel is monitored in real time based on the preset water flow safety threshold. When the water flow of the corrected river channel is less than the preset water flow safety threshold, the water flow of the corrected river channel is recorded and uploaded to the control terminal synchronously.

[0181] When the corrected river water flow exceeds the preset water flow safety threshold, a hydrological alarm is generated and uploaded to the control terminal simultaneously.

[0182] In this embodiment, when performing a virtual river simulation of the flow of a river water body, a river hydrodynamic model based on numerical hydrodynamics (CFD, Computational Fluid Dynamics) is first constructed. The model uses the finite element method or the finite volume method to discretize the river channel area and solves the river water velocity, flow rate and flow state based on the three-dimensional shallow water equations. The simulation area is constructed using high-resolution geographic information data (GIS, Geographic Information System), and the river channel boundary conditions are corrected in combination with multi-satellite remote sensing image data. During the simulation process, input parameters include river channel water level data, cross-sectional area, hydraulic gradient, Manning roughness coefficient, initial flow velocity field and boundary conditions. The simulation calculation uses the Reynolds-Averaged Navier-Stokes equations (RANS, Reynolds-Averaged Navier-Stokes) to solve the turbulence effect, and uses the Volume of Force (VOF) to calculate the turbulence effect. Fluid volume) method is used to process the evolution of the free surface of water bodies. The river water data obtained by simulation calculation includes instantaneous velocity distribution, flow data and water surface morphology information. When reconstructing the wind field based on wind-induced shear stress parameters, multi-satellite remote sensing wind speed data is first obtained and interpolated with ground wind speed station observation data to calculate the spatial distribution of the wind speed field in the river area. The wind field reconstruction is based on the Gaussian turbulence model or the k-ε turbulence model to calculate the spatial change rate of the wind speed field. The calculation formula of the wind-induced shear stress parameter is τ=ρ*Cd*U^2, where τ is wind-induced shear stress, ρ is air density, Cd is drag coefficient, and U is wind speed. When calculating wind-induced shear stress, the surface roughness data inverted by remote sensing is used to correct the drag coefficient Cd, and the spatial distribution of wind-induced shear stress is calculated based on wind speed data in different areas of the river. After the wind field reconstruction is completed, three-dimensional wind field data and wind shear force distribution information are obtained. When simulating wind shear interference on the simulated river water body based on the reconstructed wind field, first, based on the aforementioned wind field reconstruction data and wind-induced shear stress parameters, a fluid dynamics simulation software (such as OpenFOAM or Ansys) is used. Fluent) is used to calculate wind shear disturbance. During the simulation process, the air-water two-phase fluid modeling method is used to calculate the influence of wind shear on the surface velocity and direction of river water. The simulation calculation is based on the VOF method to process the free surface of the water body, and the k-ω turbulence model is used to calculate the energy loss of wind shear disturbance. The calculation results include the instantaneous velocity distribution and flow data of the water body after wind shear disturbance. When calculating the difference in water flow between the simulated wind shear disturbance water body data and the simulated river water body, the two sets of data are firstly time-space aligned to ensure that the calculated flow data are compared and analyzed at the same time step and spatial resolution. During the calculation, multiple measurement sections are selected to compare the flow Qw after wind shear disturbance with the baseline flow Qb without wind disturbance, and the flow difference ΔQ=Qw-Qb is calculated.Where ΔQ is the flow rate change caused by wind shear disturbance. After the calculation is completed, the wind shear disturbance flow rate change data at each cross-section of the river channel is obtained. When correcting the river channel water body flow rate according to the water body flow rate difference, first, based on the calculated ΔQ data, the flow rate at each cross-section of the river channel is adjusted. The correction method adopts numerical interpolation method or machine learning regression method (such as random forest or LSTM long short-term memory neural network). The flow rate correction model is trained through historical data, and the wind shear disturbance parameters and the calculated flow rate difference are input for correction calculation. The corrected flow rate data is denoted as Qr. After the correction calculation is completed, the river channel water body flow rate data after wind shear interference correction is obtained. When performing real-time monitoring on the corrected river channel water body flow rate based on a preset water body flow rate safety threshold, first, set the safety threshold Qsafe. This threshold is obtained based on the statistical data of historical extreme hydrological events and is set in combination with hydrological regulations and water resources management requirements. During the monitoring process, continuously monitor the corrected river channel water body flow rate Qr and determine whether Qr is less than Qsafe. When Qr < Qsafe is satisfied, record the current time and the corrected flow rate data, and synchronously upload it to the control terminal through a wireless transmission module (such as LoRa or NB-IoT). When the corrected river channel water body flow rate is greater than the preset water body flow rate safety threshold and a hydrological alarm is generated and synchronously uploaded to the control terminal, first, judge whether Qr is greater than Qsafe. When Qr > Qsafe is satisfied, trigger the alarm generation module. The alarm information includes the current flow rate value, the exceeding range, the time information, and the geographical location data. The alarm data is transmitted to the hydrological monitoring management platform through the network and synchronously sent to the relevant departments. The alarm information can be pushed in real time through text messages, emails, or the Internet of Things alarm system.,

[0183] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be included in the present invention.

[0184] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-satellite coordinated hydrological monitoring system, characterized in that: include: Water level altimetry module, used to collect radar pulse recovery delay data through altimetry satellite; Calculate the water level height of the radar pulse recovery time delay data to obtain the candidate river water level data; remove abnormal points from the candidate river water level data based on the preset normal water level distribution pattern to generate river water level data; The water remote sensing module is used to collect river water images through remote sensing satellites and extract water boundary features from river water images; Deducing water area change data based on water body boundary characteristics; The meteorological wind shear analysis module is used to collect river meteorological data through meteorological satellites; perform microwave scattering measurement on the river meteorological data and perform river wind speed inversion to obtain water surface wind speed parameters; and deduce wind flow shear torque based on the water surface wind speed parameters to generate wind-induced shear stress parameters; The flow estimation module is used to perform dynamic cross-sectional analysis based on river water level data and water area change data to generate the cross-sectional area of ​​each river area; and estimate the river water flow based on the cross-sectional area of ​​each river area and river water level data; A flow correction module is used to perform wind shear interference correction on the river water flow based on the wind-induced shear stress parameter to generate a corrected river water flow; The water flow in the corrected river is monitored in real time and uploaded to the control terminal simultaneously.

2. The multi-satellite coordinated hydrological monitoring system according to claim 1, characterized in that: The water level measurement module includes the following functions: The altimetry satellite multi-beam radar is collected through the altimetry satellite; pulse compression processing is performed on the altimetry satellite multi-beam radar to obtain radar pulse recovery delay data, where the radar frequency range is 13.5GHz-14.0GHz, the pulse width range is 3.2-3.8 microseconds, and the delay resolution is 0.1 nanosecond; Perform phase differential correction on the radar pulse recovery delay data to generate denoised delay data, where the phase differential range is -180° to 180° and the delay correction range is -0.5 to 0.5 nanoseconds. Perform refraction mapping water level inversion on the denoised delay data to obtain the original water level height data. Perform atmospheric delay water level compensation on the original water level height data and perform ellipsoid fitting correction to obtain ellipsoid-corrected water level data; Performing local coordinate projection transformation on the ellipsoid-corrected water level data to generate candidate river channel water level data; performing distribution fitting analysis on the candidate river channel water level data to obtain water level distribution deviation data; Based on the preset normal water level distribution pattern, abnormal point detection is performed on the water level distribution deviation data to obtain abnormal water level data; The abnormal water level data is checked for isolated abnormal points to generate checked isolated abnormal points; the checked isolated abnormal points are interpolated and filled to generate river water level data.

3. The multi-satellite coordinated hydrological monitoring system according to claim 2, characterized in that: The atmospheric delayed water level compensation for the original water level height data and the ellipsoid fitting correction include: Extract atmospheric delay parameters from raw water level height data; Calculate atmospheric delay amount for atmospheric delay parameters; Perform delay compensation processing on the original water level height data based on the atmospheric delay to obtain the atmospheric correction water level; Initialize the ellipsoid model parameters for the atmospheric correction water level to generate the ellipsoid model parameters, where the number of iterations is 5-15 times and the error convergence threshold is 0-0.001m; Fit and optimize the atmospheric correction water level and ellipsoid model parameters to obtain the optimized ellipsoid parameters; The atmospheric correction water level is subjected to ellipsoid correction according to the optimized ellipsoid parameters to obtain ellipsoid-corrected water level data.

4. The multi-satellite coordinated hydrological monitoring system according to claim 1, characterized in that: The water remote sensing module collects river water images through remote sensing satellites and extracts water boundary features of the river water images, including: Collect optical imaging data through remote sensing satellites; Perform radiation correction on the optical imaging data and perform atmospheric elimination processing to obtain river water images; Perform multi-view processing on SAR satellite microwave imaging data and extract scattering feature data; Perform polarization phase decomposition on the scattering characteristic data and partition the backscattering pattern to obtain the polarization scattering pattern of the water body; Perform tensor coupling processing on river water images and water polarization scattering patterns to generate remote sensing image fusion river data; Perform water boundary segmentation and analysis on remote sensing image fusion river data to obtain river water mask data; The water boundary features of river water images are extracted based on river water mask data.

5. The multi-satellite coordinated hydrological monitoring system according to claim 1, characterized in that: The water body remote sensing module deduces water body area change data based on water body boundary characteristics, including: The boundary features of the water body are located by using navigation satellites to obtain optimized water body boundary features; Based on the optimized water body boundary features, the water body pixels within the boundary of the river water body image are extracted to obtain the number of water body pixels within the boundary; Based on the preset satellite imaging spatial resolution, the number of water body pixels within the boundary is analyzed for point changes to generate boundary change data; Water area mapping is performed on the river water body image based on the boundary change data to obtain water body area change data.

6. The multi-satellite coordinated hydrological monitoring system according to claim 1, characterized in that: The meteorological wind shear analysis module performs microwave scattering measurement on river meteorological data and performs river wind speed inversion, including: Performing denoising transformation on river meteorological data to obtain denoised river meteorological data; Decompose the characteristic spectrum of denoised river meteorological data in layers to obtain river meteorological characteristic data; The radar scattering model is inverted on the river meteorological characteristic data to obtain the initial scattering coefficient; The initial scattering coefficient is compensated for the microscale wave roughness to obtain the corrected scattering coefficient; Perform water body electromagnetic feature mapping on the corrected scattering coefficient to generate water surface scattering characteristics; Multi-angle polarization scattering fusion is performed on the water surface scattering characteristics to obtain multi-view scattering data; performing directional scattering energy correction on the multi-view scattering data to obtain corrected scattering data; The wind field dynamics deconstruction is performed on the corrected scattering data, and wind speed parameter mapping is performed to obtain the wind speed parameters on the water surface.

7. The multi-satellite coordinated hydrological monitoring system according to claim 1, characterized in that: The deduction of wind shear torque based on water surface wind speed parameters in the meteorological wind shear analysis module includes: Reconstruct the spatiotemporal flow field of the wind speed parameters on the water surface to obtain continuous wind field distribution data; Based on the continuous wind field distribution data, the fluid boundary layer analysis of the wind speed parameters on the water surface is carried out to generate the vertical distribution data of the wind speed; Fit the flow field logarithmic function to the vertical distribution data of wind speed to obtain wind shear gradient data; Perform gas dynamic density compensation on wind shear gradient data to generate density-corrected wind shear data; Calculate aerodynamic drag from density-corrected wind shear data; Perform drag coefficient fitting on aerodynamic wind resistance to generate wind resistance coefficient; Wind-induced stress calculation is performed on the density-corrected wind shear data and wind drag coefficient to generate wind-induced shear stress parameters.

8. The multi-satellite coordinated hydrological monitoring system according to claim 1, characterized in that: The flow estimation module performs dynamic cross-section analysis based on river water level data and water area change data, including: Deducing the river width at different times based on the water area change data; Align the timestamps of river width and river water level data at different times to obtain synchronized hydrological data; Based on the synchronized hydrological data, the river channel spatial registration is performed to obtain the hydrological data corresponding to the location; Intercept the river cross-section distribution data based on the hydrological data corresponding to the location; Based on the location-corresponding hydrological data and the river cross-sectional distribution data, the water-flowing cross-sectional area of ​​each region of the river is inferred.

9. The multi-satellite coordinated hydrological monitoring system according to claim 1, characterized in that: The flow estimation module estimates the flow of the river based on the cross-sectional area of ​​each river region and the river water level data, including: Calculate the wetted perimeter parameters of each river channel area based on the cross-sectional area of ​​each river channel area and the river water level data of the corresponding river channel area; Derivation of hydraulic radius data for each region of the river channel based on the cross-sectional area of ​​each region of the river channel and the wetted perimeter parameters of each region of the river channel; Mapping the hydraulic gradient of various areas of the river based on river water level data; The Manning formula is used to estimate the flow rate of the river water body based on the preset river channel Manning roughness coefficient, the cross-sectional area of ​​each river channel area, the hydraulic radius data of each river channel area, and the hydraulic slope of each river channel area. Among them, the Manning formula is specifically: ； Among them, the formula is the river water flow, is the Manning roughness coefficient, is the cross-sectional area of ​​water flow, is the hydraulic radius data, is the hydraulic slope.

10. The multi-satellite coordinated hydrological monitoring system according to claim 1, characterized in that: The flow correction module includes the following functions: Perform virtual river simulation on the river water flow to obtain simulated river water body; The wind field is reconstructed based on the wind-induced shear stress parameters, and the wind shear interference simulation is performed on the simulated river water body based on the reconstructed wind field to obtain the simulated wind shear interference water body data; Calculate the difference in water flow between simulated wind shear disturbance water body data and simulated river water body; Perform wind shear interference correction on the river water flow according to the water flow difference to generate the corrected river water flow; The water flow of the corrected river channel is monitored in real time based on the preset water flow safety threshold. When the water flow of the corrected river channel is less than the preset water flow safety threshold, the water flow of the corrected river channel is recorded and uploaded to the control terminal synchronously. When the corrected river water flow exceeds the preset water flow safety threshold, a hydrological alarm is generated and uploaded to the control terminal simultaneously.

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